Starts Tonight! Intro to Data Science Enroll Now

Made at Metis Graduate Directory

In this free directory, you will find resumes, bios, and final project presentation videos (4-8 minutes in length) for each Metis data science bootcamp graduate actively seeking employment.

Share your open roles with our grads.
Upskill your teams to find value in data.

FALL 2020

Amanda J. Cheney

Amanda Cheney earned her PhD in political science from Cornell University in 2017. For the past three years she has worked as a postdoctoral researcher at Lund University in Sweden and as an international affairs analyst and consultant for the Norwegian Institute of International Affairs, though she is currently based in Vancouver. Amanda is a lifelong learner and a creative problem solver. She loves using data to help navigate uncertainty, concisely explaining nuanced topics and making complex ideas easy to understand.
Amanda J.
Actionable Insights from Lululemon Product Reviews
Natural Language Processing / Recommender Systems
Natural language processing & unsupervised learning exploration of customer reviews of lululemon’s best-selling sports bras. Findings indicate reviews generally concerned with 6 unique topics. Created a streamlit app to curate reviews for customer product needs.

Andrew Auyeung

Andrew is a high school AP Physics teacher turned data scientist. Preparing weekly lab investigations, he found his passion in looking for ways to guide his students to the “wow” moment of understanding. This motivated him to transition into data science to learn more about how he could share the story that data can tell. With his projects at Metis, he gravitated towards natural language processing and classification of text documents. Andrew is a great team worker who leverages his experience in education with his data science knowledge to join the ideas of his peers to make the “whole greater than the sum of its parts.”
What Do Data Scientists Talk About?
Natural Language Processing / Neural Networks / Tableau/Dashboards
Combining neural networks and topic modeling, this project strives to determine the hot topics in data science. Document embeddings are analyzed using Tableau to provide a commentary on how data scientists write on the Towards Data Science editorial.

Andrew Duncan Sweeney

Duncan holds a BSC in economics with a mathematical emphasis from the University of Wisconsin. Prior to joining Metis he was an equity trader for a proprietary trading firm in New York and Chicago. He has built his technical background through a combination of self-instruction and formal coursework. His interest in solving problems using an analytical approach and a drive to always be learning led him to pursue Data Science full-time.
Andrew Duncan
Sheepshead Card Hand Evaluator
Classification / Regression / Tableau/Dashboards
Developed a live game assistant app that uses image processing techniques to identify held and played cards. Additionally, it uses machine learning to classify the quality of the player's cards, trained on a custom built database of hands.

Andrew Zhou

After graduating from Harvard with a degree in Computer Science and a minor in English, Andrew Zhou set his sights on using his quantitative know-how and humanistic worldview to better the world in his own way. He discovered that his particular passion lays in data science, a field uniting considerable mathematical depth with an astounding capacity for fostering communication and understanding. After completing a data deep dive at Metis, he’s excited to bring his skills to bear on whatever problem comes next.
My Honor!: Modeling Avatar: The Last Airbender Fanfiction with Natural Language Processing
Neural Networks / Recommender Systems / Computer Software and Security
Using natural language processing, Andrew constructed topic and language models for Avatar: The Last Airbender fanfiction. He developed an application that leverages these models to recommend, generate, and present visual analytics for fanfiction.

Anthony Tagliente

Before Metis, Anthony spent four years as a Data Associate at a private foundation, where he worked closely on rating and reporting their social impact investments. Working with a small team he took on many roles including managing databases, scripting data pipelines, vetting external data sources, and offering ad-hoc research. In undergrad, Anthony conducted an independent research project on youth unemployment initiatives in Bosnia and Herzegovina under World Learning’s School of International Training. He received his BA in Economics and Political Science from the State University of New York at Albany in 2012. He finally found his love of skiing and rock climbing by his late 20s, and spends weekends camping with friends in the Adirondacks when he can.
Live Audio Gunshot Classification
Classification / Neural Networks / Tableau/Dashboards
Using CNNs to make real time gunshot classifications with streaming audio.

Bao Nguyen

Bao is a Data Scientist with a passion for economizing and expediting real-time workflows using data and analytics. He created several novel imaging algorithms as a PhD graduate to improve the time and storage complexities of seismic imaging routines that produce a realization of the Earth's subsurface. As a Geophysicist for over 7 years in Houston, he's worked on geoscience problems focused on quantitatively characterizing oil and gas reservoirs using geophysical and rock physics methods. Bao is motivated by turning insight into foresight as his interests are focused at the intersection of innovation, automation, and technology. With a background in geophysics, his work includes flavors of signal processing for exploring the value in expedient classification models. Bao holds a PhD from The University of Texas at Dallas.
Augmenting Seismic Interpretation: Predicting Rock Types From Exploration Seismic Data to Improve Workflow Pipelines
Anomaly Detection / Classification / Neural Networks
Classification of six lithologies (rock types) from 3D seismic volume data domain-specific user-engineered features for tree-based classifier models

Beth Baumann

Metis grad Beth Baumann is experienced in the domains of biology and health care. She received her PhD in Life Sciences from Northwestern University and completed a productive post-doctoral research position at University of Illinois at Chicago. Her research has focused mainly on prostate cancer, non-coding RNAs and transcriptomics. While Beth is passionate about applications of data science to sequencing data, precision medicine and digital pathology, she is generally excited about applying her analytical and research experience in a business setting. She is a quick learner and adept at solving challenging problems.
Detecting Tumor Mutational Signatures using a Convolutional Neural Network
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Mutational signatures or patterns in tumor DNA can give clues to a tumor’s etiology. The goal of this project was to train a convolutional neural network to identify tumor type from short aligned DNA fragments. Training data was generated using The Cancer Genome Atlas and gnomAD.

Brian Tam

Brian is a Berkeley Engineer with 3 years of experience analyzing geospatial data. Currently Brian is pursuing a Masters Degree in Analytics from Georgia Tech.
Myers Brigg NLP Classifier
Big Data/Data Engineering / Classification / Cloud Computing (AWS/Google Cloud)
Deployed web app that determines which of the 16 personality types a person aligns with based solely on their writing, to simplify the MBTI classification process.

Chris Doenlen

With over 10 years in early-stage startups, Chris Doenlen is a scrappy self-starter with extensive experience in financial forecasting and analysis, operations, strategy, and fundraising. He’s worked in a multitude of industries including digital media publishing, consulting in Silicon Valley, SaaS, and manufacturing. Chris’ growing interest in automation and the power of machine learning led him to make a dedicated push towards data science, where he’s found a passion for natural language processing and synthetic media.
AI Generated Tarot
Clustering / Natural Language Processing / Neural Networks
An exploration of media creation with artificial intelligence through the lens of tarot. Created 22 original tarot cards using StyleGAN2-ADA to generate images and GPT-2 for annotations.

Christine Lloyd

Christine Lloyd is a data scientist and educator. While getting her PhD in microbiology, she wrestled with one of the problems facing most organizations today: does it really help to have all this data if you don’t have a streamlined and reproducible way to figure out what the data means? After several years teaching college, she realized that data science was an opportunity to continue wrestling with data problems, communicate with diverse stakeholders, and be a force for good in whatever organization she works with.
Sweater Getter
Classification / Clustering / Neural Networks
Recommends sweater patterns on Ravelry that are most similar to the user-input image.

Collins Westnedge

Collins graduated from University of Chicago with a B.A. in Philosophy where his focus was on analytic philosophy; specifically, language and its relation to formal logic. Prior to Metis, he was consulting for Hanover investment Advisors where he worked with commercial real estate data and provided statistical analysis and insights for various groups and organizations. Collins went on to do continuing his studies in Art and Technology at the School of the Art institute of Chicago where he focused on creative applications and uses of AI.In particular, he is very interested in language as well as the different ways we represent it both logically and computationally, but most importantly he enjoys rock climbing whenever he gets the chance.
Mental Health Online
Natural Language Processing / Neural Networks / SQL
Used topic modeling and word embeddings to examine how mental health is discussed online as well as the benefits and common issues users experience in these support spaces.

Drew Hibbard

Drew has a passion for turning data into intuitive visualizations and insights. After graduating with a degree in finance from the University of Minnesota, Drew gained experience in the finance/banking industry, where his role became increasingly focused on data analysis. This led him to enhance his data skills at Metis and pursue a career in data science. Drew is excited to be joining a field with rapid advancements and is always eager to learn the latest tech.
Saving the Rainforest with Machine Learning
Cloud Computing (AWS/Google Cloud) / Neural Networks / Tableau/Dashboards
Developed a convolutional neural network that uses satellite imagery to identify the leading indicators of deforestation with 92% accuracy. Similar technologies could be used to prevent deforestation.

Gavin Jones

Gavin is a data scientist and financial analyst with five years of investment and operational experience. Prior to Metis, he was a private equity fund and co-investment analyst at Barings in New York City, where he conducted and supported investment underwriting and monitoring activities on behalf of third-party institutional clients. Gavin holds a Bachelor of Science in Political Science and Business Administration with a Concentration in Finance from Northeastern University.
Amazon Investing and Press Release Seasonality
Anomaly Detection / Natural Language Processing / Time Series/Forecasting
Created an Amazon investment model by conducting latent dirichlet allocation to understand which press release topics drive outperformance. Once established, outperforming topics were fed into Facebook Prophet to establish and capitalize on outperformance patterns.

Ian Livingston

Ian graduated from UNC-Chapel Hill with a degree in Journalism and before Metis served for seven years as the traveling editor of a major online travel publication. He is based in Brooklyn, NY. He is excited to bring new data science insights and machine learning tools, plus patience, curiosity, and a strong interest in NLP, to new projects.
Wiki2Vec: Connecting People With the World's Best Foods
Natural Language Processing / Neural Networks / Recommender Systems
A tool with which users can identify new favorite foods and dishes from around the world based on foods and dishes they know.

J Patty

J holds a BS in International Business from Indiana University and an MBA from the University of Washington. Previously, he served as Strategy Manager, Global Consulting for BDO Panama where he researched and established analytical frameworks and conducted business analyses. Furthermore, J licensed and expanded U.S. brand “Life In Color” into Latin America, where he managed the overall project delivery, systems implementation, and recruitment of a 65-person multi-disciplinary team. His appreciation of data mining, problem-solving, and programming led him to pursue a full-time career in data science.
Bike Sharing: Smart Solution For Traffic Congestion
Big Data/Data Engineering / Cloud Computing (AWS/Google Cloud) / Time Series/Forecasting
Time-series analysis and forecasting to analyze a potential smart solution, a bike sharing system, for the congestion issues in Chicago.

Jaeseok Park

Jaeseok (Jay) comes from a background in experimental biology, but his desire to utilize programming to solve practical problems eventually led him to pursue data-science as a full-time career. He finds many similarities between experimental science and data science, and finds that his intuition about data comes in handy when crafting creative, data-driven solutions. Outside of work, he is an avid reader of The Economist and the Wall Street Journal. Jaeseok holds a PhD in neurobiology from Massachusetts Institute of Technology and a BS in biochemistry and cell biology from Rice University.
DeepFlag: The Deep Learning Flag Generator
Neural Networks
This project harnesses the power of deep learning to aid citizens and organizations in the difficult process of designing flags.

Jason Pizzollo

Jason has extensive experience as a research scientist with a background in genomics, bioinformatics, and drug discovery. He earned his Ph.D. at the University of Massachusetts studying evolution in the human genome, and has worked in the pharmaceutical industry developing novel antibacterial agents. Seeing the possibilities for learning from the abundance of data in our lives, Jason chose to strengthen his skills in machine learning and data science at Metis. His interests are in all things science, health, wellness, fitness, and nutrition.
Heart Rate Variability to Predict Stress and Rest
Classification / Time Series/Forecasting
Classification model to identify stressful events during daily activities and disturbances during sleep using heart rate variability and actigraphy data.

Jennifer Sun

Jen is a data scientist passionate about the utilization of data to discover solutions within sustainability and environmentalism. Prior to Metis, she engaged with big data as a data visualization intern at the United Nation’s innovation lab, UN Global Pulse. She then joined Glanstone Capital as an analyst where she leveraged strategic support for the firm’s portfolio companies with a focus on data and technology. Jen completed her undergraduate studies at New York University with a B.A. in Economics.
The Climate of Climate Change
Clustering / Natural Language Processing / SQL
Utilized topic modeling and applied NLP techniques to discover focuses and biases within media coverage of climate change.

Jonathan Cosme

Jonathan has had a passion for big data and predictive analytics since his undergraduate days at Florida State University, where he took graduate-level time series courses, and performing academic research alongside a professor, before completing his bachelors in Economics & Finance. Since graduating, Jonathan has worked as a treasuries trader, and algorithmic trading analyst, where he was able to analyze terabytes of market data for trading insight and ideas. Most recently, he was a Business analyst and Product Owner at an IT consulting firm, where he architected a new pharmaceutical product tracking system for the Florida Department of Health. Outside of data, Jonathan enjoys baking challah for his local synagogue, and is an avid sabre fencer.
Project Shema
Clustering / Natural Language Processing / Neural Networks
English translations of the Hebrew bible, and Hebrew texts are explored using NLP and cluster analysis, before building a Hebrew-to-English transformer translation model using TensorFlow.

Joseph Cowell

Joe graduated from the University of Pennsylvania with a degree in Materials Science & Engineering and a minor in Engineering Entrepreneurship. With two internships and a college education under his belt, Joe decided to follow his passion in music after graduation. Applying his entrepreneurial educational to his musical journey, Joe accomplished great feats, such as, performing around the world, releasing six albums, and securing publishing and distribution deals. After the band decided to part, Joe began exploring the world of programming and data science through online courses, ultimately enrolling at Metis to fortify his data science foundation. By combining his data science skills and entrepreneurial experiences in the music industry, he seeks to bring data to the forefront of driving business decisions in this next chapter of his life.
AI-Generated Lyrics from Your Favorite Beatle
Classification / Natural Language Processing / Neural Networks
Combined Open-AI’s GPT-2 model and the NLTK libraries to create an NLP project that dives into The Beatles’ lyrical content through topic modeling, sentiment analysis, and text generation in the form of a full song.

Julia Nguyen

Prior to making the career transition into data science, Julia graduated with a PhD in organic chemistry from the University of Washington. Julia enjoys using data to help understand the world and solve problems that have a meaningful impact on people's lives and she is excited to apply her analytical skills to fields beyond chemistry.
Measuring Urban Sprawl Using Satellite Images
Classification / Neural Networks / Cloud Computing (AWS/Google Cloud)
Developed a convolutional neural network that can detect areas of urban development in satellite images. This model can be applied to measure urban sprawl in various cities.

Julia Qiao

Julia Qiao has worked in analytics for 4 years at Expedia Group, partnering with high-level stakeholders and using data to drive impact across B2B and B2C products. She holds a B.S. from New York University where she double-majored in Economics and Communications. Julia is constantly curious and a lifelong learner; at Metis, she deepened her mastery of the end-to-end data science workflow. Julia is passionate about natural language processing and data literacy. Her career mission is to use the power of data to help people, products, and processes fulfill their greatest potential.
Noteworthy Usage: building product use cases via natural language processing
Cloud Computing (AWS/Google Cloud) / Natural Language Processing / Neural Networks
Partnered with private e-notes startup which promotes gratitude and connection, to analyze product usage; generated 5 core product use cases and strategies for repeat user conversion.

Julian Cheng

Julian Cheng is a current undergraduate at Stanford University majoring in Bioengineering and minoring in Data Science. Prior to Metis, he worked as a research assistant in a pathology research laboratory. His lifelong interest in statistics and helping others motivated him to create data science projects that explored COVID-19 infection rates and lung cancer risk prediction. He is eager to leverage his analytical skills to tackle challenges in the professional setting.
Identifying Underwater Trash Using Neural Networks and Natural Language Processing
Classification / Natural Language Processing / Neural Networks
Designed a neural network to assist autonomous robots in cleaning up ocean debris from the sea floor by differentiating trash from organisms. Leveraged natural language processing and transfer learning to identify commonly predicted images.

Laura Urdapilleta

Laura is a Ph.D. student in Evaluation, Measurement, and Research (EMR) at Western Michigan University. Her data analysis journey started with her first job in a marketing research firm and continued during grad school. With her work as an Engineering & Quality Data Analyst at a manufacturing company, Laura is excited to take her skills and domain knowledge and apply it to a new position in data science.
Paintings, Movies, and Emotions in the Context of Computer Vision
Neural Networks
Applied Convolutional Neural Network, transfer learning with Resenet-101, and data augmentation to classify painting images by the emotion evoked in the observer.

Lucy Abbot

Lucy earned her bachelor's degree in Criminology and Psychology with highest honors from the University of Pennsylvania. Her education in social sciences motivated her to a career using data to tell stories, solve problems, and help people work more effectively. She began her career as a consultant where she got exposure to how data is used across different industries. She then joined Braven, an education startup, as a founding team member and data analyst, and spent the last five years building out Braven’s data team and systems as the organization grew quickly. Lucy loves communicating data and insights in an accessible way, and enjoys learning and mastering new tools.
Improving Accessibility on Twitter with Deep Learning
Clustering / Neural Networks
This project explores the potential of deep learning image captioning models to generate alternative text for images shared on twitter, improving accessibility for users who rely on screen readers.

Lewis Sears

Lewis Sears is a Data Scientist based in New York City obsessed with using data to better understand implicit human behavior and exposing elemental patterns. While at metis, he designed and worked on projects using supervised and unsupervised learning algorithms, natural language processing, and neural networks. Before data science, Lewis was a published academic who spent years doing research in pure mathematics and presented at conferences around the country. Lewis holds a bachelor's degree in mathematics from Washington and Lee University and a master’s degree in mathematics from Wake Forest University.
DeepLew: My Chess Engine
Big Data/Data Engineering / Neural Networks
Combined minimax algorithms and convolutional neural networks to develop a robust chess engine from scratch.

Matt Ranalletta

Working on the digital and product side of presidential campaigns and issue advocacy organizations for the past nine years, Matt Ranalletta has always been interested in the intersection of data and public policy. This year, he took the leap into data science by enrolling in Metis — to expand his technical expertise in Python, statistics, and machine learning. Matt is especially interested in applying these skills in roles in government, city planning, or at nonprofits — as well as technology companies with great public missions to make change. His projects with Metis include predicting subscriber status in the Bay Area bike share system, exploring the relationship between household gender dynamics and COVID-19, and performing natural language processing and topic modeling of Joe Biden's tweets.
COVID-19 and its Effect on Gender Equality and Household Hardship
Regression / Tableau/Dashboards
Performed regression analysis on worldwide COVID-19 data combined with gender equality survey responses to predict an aggregated household hardship metric.

Marcos Dominguez

Born and raised in California, Marcos is a banking professional with 8 years of experience in the industry. He started as a teller, and worked his way up to the position of financial analyst and eventually to senior credit analyst. He earned a B.S. in Economics in 2010 and an MBA, Accounting in 2016. He is in the process of transitioning to data science within the banking industry to explore innovative ways to reach under-banked people around the world!
Loan Portfolio Risk using Machine Learning
Classification / Cloud Computing (AWS/Google Cloud) / Regression
Loan portfolio risk with supervised machine learning . Used classification for predicting risky loans and regression for predicting loan loss.

Mason Ellard

Mason holds a B.S. in Economics from the University of Central Florida, and it was his formal training in econometrics at UCF that catalyzed his exploration into how data and machine learning could be used to inform decision-making at the producer and consumer levels. Prior to joining Metis, Mason worked as a real estate private equity intern at Encore Capital Management, where he researched consumer behavior in primary commercial real estate markets such as San Francisco and Los Angeles. His analytical curiosity, economic intuition, and technical background led him to pursue a full-time career in data science.
Predicting Gentrification in California
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Built an artificial neural network to predict if a California neighborhood will experience gentrification using U.S. census data. Plan to deploy model in an interactive dashboard using Flask and D3.

Michael Green

Michael holds an MSE EE from the University of Michigan and a BS EE from Kettering University. He has worked in high performance semiconductor design for companies like Intel Corporation, Advanced Micro Devices, and Samsung. His love of engineering, mathematics, and passion for applying these to solve problems has led to his pursuing a full-time career in data science.
Sentiment Analysis of IMDB Movie Reviews
Classification / Natural Language Processing / Neural Networks
Developed a bidirectional LSTM with pre-trained GloVE word embedding using keras that classified a movie review using the Net Promoter Score (NPS). The model was trained on movie reviews scraped from using request, Selenium, and BeautifulSoup. The model was deployed as a Flask web application that accepts the movie title and the free-form text review of the movie and then sends back to the user a predicted NPS. It is able to capture these reviews and the users desired score into a database that can then be repurposed to further train the model.

Natalie Chen

Natalie Chen is a data scientist with a background in biology and sustainability. After graduating from University of California, San Diego, she spent several years working in research and development at Poseida Therapeutics, a gene therapy company and Indigo Agriculture, an agriculture technology company. Her experience working at two start-ups and attending bootcamp gave her the invaluable skill to learn quickly. Natalie is curious and passionate about using data science to solve environmental challenges. In her free time, she loves to go rock climbing or snowboarding and hanging out with her rabbits.
Plant Disease Detection with Neural Networks
Anomaly Detection / Classification / Neural Networks
Utilized a transfer model to optimize a convolutional neural network to classify images and predict plant disease, then an app was created and deployed online.

Natarajan Chandrakumar

Natarajan Chandrakumar is an IT consultant specializing in software application development, product and service delivery management. Chandra is innovation oriented and enjoys problem solving. He has gained valuable experience over the years from leading teams to deliver large scale enterprise projects for organizations in healthcare, financial services, professional services and technology consulting sectors. Chandra loves books and has a diverse set of other passionate interests that include art history, literature, music, sports, and art films.
Crime Trend Prediction Prediction for Chicago
Neural Networks / Regression / Time Series/Forecasting
Crime Trend Prediction for Chicago using SARIMA and LSTM based models.

Neda Saleem

Neda earned her BS cum laude in Applied Mathematics from California State University, Northridge and her MA in Mathematics from University of Pennsylvania. Her research included qualitative and quantitative analyses of network models and epidemiological models. She has enjoyed expanding into data driven models that lead to actionable insights and is excited to continue developing as a Data Scientist.
Classify Muay Thai
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Built a model that can classify Muay Thai strikes in real-time via images, pre-recorded videos, and live webcam feed. Users can upload an image or video into a Streamlit app and have the moves identified.

Nick Wilders

Before Metis, Nick worked as a musical director and keyboardist around the world, with companies ranging from NBC to the national tour of Jersey Boys. His creative skills as a musician were a natural match for Python, data visualization, and the "big picture" world of data science. A keen aesthetic eye and experience with data-driven positions at Rock and Roll Hall of Fame and Museum and Broadway Cares / Equity Fights AIDS makes him a unique and qualified candidate for data analytics and data science positions. He earned his BA in a self-designed curriculum focused on Arts Management from Baldwin Wallace University, where he also studied Statistics and Psychology.
On The Road Again: Using Deep Learning and Regional Analysis to Remap the American Tour Route
Neural Networks / Tableau/Dashboards / Time Series/Forecasting
Trained a neural network to auto-generate U.S. tour routes for traveling productions, leading to user-friendly app aggregating COVID-19 NLP sentiment analysis and regionalized search term engagement.

Noah Jaffe

Noah is a passionate data scientist most concerned with applying his analytical skills to natural science and studying pop culture. Before coming to Metis, Noah received his master’s in marine biology from San Francisco State University, where he conducted a thesis detailing the coastal phylogeography of a small, intertidal sea star. He channeled this passion for marine biology in his Metis passion project, training a neural network to classify images of intertidal invertebrates. While at Metis, he also conducted projects exploring linear regression, classification, and natural language processing. In addition to data science, Noah is passionate about running, watching movies (Star Wars and The Incredibles are his favorite), and playing games such as Magic: the Gathering.
Using a Neural Network to Classify Images of Intertidal Invertebrates
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
I trained a convolutional neural network to classify images of intertidal invertebrates into six groups.

Peter Prudhomme

With a BA in Physics from the University of California, Santa Cruz and business management experience, it was a desire to create company value through applied analytics that drove Peter into the field of data science. His technical background is a combination of formal university education, fast-paced skill development on the job, and constant self-instruction. After graduating college, he worked as an Operations Manager and implemented inventory control, shipping, and employee safety strategies that significantly lowered overhead and could handle company scaling. Apart from data science, Peter is interested in rock climbing, playing guitar, and literature/poetry.
Poetry Pitch: An Interactive Poetry Recommender
Classification / Natural Language Processing / Recommender Systems
Poetry Pitch is a content based poetry recommendation engine combined with a user interface that generates a poem based on poems 'liked' by the user.

Rami Subramaniam

Rami is a data scientist, problem solver and team player. He has over 10 years experience in engineering where he has developed a passion for using data to uncover valuable insights. His approach involves a unique blend of technical expertise, thorough communication, and a good bit of creativity. Metis’ curriculum provided a solid foundation in supervised and unsupervised learning, natural language processing, and big data with an emphasis on project work. Rami holds an MS in engineering from University of Houston and a BS in engineering from Johns Hopkins University.
Off The Beaten Path Travel Recommender
Big Data/Data Engineering / Natural Language Processing / Recommender Systems
Sick of the busy tourist traps? This app provides a list of unique towns in a selected country based on a variety of popular interests.

Ridwan Alam

Ridwan has a history of presenting technical solutions to non-technical audiences. He has worked 5 years as an Energy Engineer in the US, Mexico, and Canada focusing on energy efficiency projects in the commercial and industrial sectors, and then transitioned into Business Development for generating new solar PV projects in the New York/New Jersey area. Having taught himself software development in the past year and now completing the Data Science bootcamp, he is searching for a role where he can deliver innovative solutions to society. He would like to work at a Hyperloop company, Tesla, transportation, or a company that is focusing on an emerging technology. In his ideal roll he would like to apply NLP, Neural Networks, Computer Vision, and/or Object Detection. He has a BS in Industrial Engineering from Virginia Tech.
Autonomous Driving Object Detection
Big Data/Data Engineering / Classification / Cloud Computing (AWS/Google Cloud)
Created an object detector model using Darknet neural network and YOLO object detector. Model detects traffic signs, traffic lights, and other cars with a Mean Average Precision score of 74%.

Ryan Lewis

Currently located in Berkeley, Ryan holds a Bachelor's degree in Mechatronic Engineering and has primarily held positions at companies developing robotics and automation engineering products. His passion is for discovering how new technologies can be applied to existing problems in order to find simple and elegant solutions. His positive outlook and ability to convey complex principles in a digestible manner make him a joy to work with!
Using Reinforcement Learning to Train Autonomous Vehicles
Anomaly Detection / Big Data/Data Engineering / Neural Networks
Used Deep Reinforcement Learning to train and model autonomous vehicles interacting with human drivers in a simulated urban environment.

Ryan Werth

Ryan received his B.S. in Physics from California Polytechnic State University, San Luis Obispo. After college he entered the web development world where he worked for a fantasy sports and poker training website. He is looking to combine his mathematical and analytical skills from Physics with the programming skills he’s honed over the past few years, naturally leading him into the Data Analytics world. He enjoys solving problems, and in particular figuring out clever ways to get computers to solve problems. When he is not behind the keyboard, he loves surfing, ball sports, and anything outdoors.
AI Generated Basketball Highlights
Neural Networks
Used computer vision and object tracking to automate basketball highlights and extract important game information from raw film. Trained a custom neural network for the object tracking, and used openCV for the computer vision.

Sibongile Toure

Sibongile graduated from Howard University with a BSc in Computer Science. Upon graduating she worked as a technology analyst at an IT consulting firm. Throughout her time as a technology analyst she became interested in becoming a data scientist. In her desire to gain the skills needed she joined Metis where she found ways to combine her passion for data and for the arts and developed various applications using complex machine learning models and algorithms. Sibongile is also passionate about giving back to her community and in her free time volunteers for organizations such as Girls Who Code and Black Girls Code. Additionally, Sibongile is a self-described cinephile and hosts a film podcast, "That Brooklyn Film Show," with her brother.
Moodsic: A Mood Based Music Recommender System
Natural Language Processing / Recommender Systems
Developed a Flask app that recommends a music playlist to a user based on a mood. Recommendations made by using key audio features and song topics identified through NLP and LDA.

Solomon Klein

Solomon is a graduate of Brown University with a BA, double majoring in mathematics and geology. His technical background is a combination of multiple semesters of study, as well as self-driven projects. His interest in data science was spurred by a pandemic project modeling baseball seasons to determine how often the best team won.
Forecasting El Nino
Regression / Time Series/Forecasting
Using Facebook's time-series model, I attempted to predict the El Nino cycle (ENSO) using ocean buoy data from the Pacific Ocean.

Sunna Jo

Sunna Jo studied Biology at Cornell University, went on to medical school at the Albert Einstein College of Medicine, and trained in pediatrics. She has been conducting scientific and clinical research for over ten years and has increasingly recognized the importance of using data to inform decisions, which led her to the field of data science. She is passionate about mining actionable insights from data and leveraging these insights on a larger scale to enact change.
Predicting Bone Age Using Deep Learning
Big Data/Data Engineering / Neural Networks / Regression
Determine which factors may be important to consider in using deep learning to predict bone age.

Taeuk (Allen) Kim

Taeuk is passionate about using data to help inform better decision making, which was the primary driver for his decision to join Metis. Prior to Metis, Taeuk studied at Carnegie Mellon University in NY where he obtained a B.S. in Economics and Statistics. Taeuk’s work experience includes cleaning, analyzing and generating insights from a variety of financial datasets within his previous Venture Capital Firm. By combining a strong foundation in machine learning with his background in statistics, Taeuk seeks to bring data-driven perspectives to big-picture strategic thinking.
Taeuk (Allen)
MODERN: Image-Based Furniture Recommendation
Classification / Neural Networks / Recommender Systems
MODERN is an app that generates furniture suggestions based off user provided images by analyzing its design features using convolutional neural network.

Timothy Dooley

Tim is a Data Scientist with a passion for telling stories and providing actionable intelligence from overlooked details. He used innovative methods as a PhD researcher at King's College London to compile novel research in linguistics and ancient languages. As a teacher for 5 years in New York City, he communicated complex ethical and philosophical concepts. Tim’s motivated by turning the mundane into the exciting and providing state of the art solutions to problems. With a background in linguistics, his work includes NLP using neural networks (GPT-2, Transformers) and topic modeling. Equipped with both supervised and unsupervised learning techniques, he enjoys building models to find signals in noise. Tim holds a PhD from King’s College London, graduate work from Oxford, and an honors BA from Schreyer Honors College Penn State.
“I’ll Bake What She’s Having!“: Recipe Recommender System
Natural Language Processing / Neural Networks / Recommender Systems
Two products built upon ~5,000 recipes and images from A Computer Vision Reverse Image Search recipe recommender and An NLP model that recommends a recipe based on user text.

Vanessa Hu

Vanessa graduated from a top school in Beijing, majoring in Economics. After relocating to the US to earn a Master's degree in management from Pratt Institute in New York City, she began her career in Finance. Working as a sales and marketing manager for an international finance firm, she negotiated terms on multimillion-dollar partnerships to bring overseas investment to commercial real estate developments in the US. Performing her own due diligence analysis on development deals, Vanessa realized the critical importance of data-driven decision making first hand and began her education in data science. Prior to Metis, Vanessa worked in Bank of America as a Relationship Manager. While at work, she started her education with Harvard Business Analytics Program. She also holds an Advanced Web and Interactive Design certificate from UCLA.
New Vaccine Adoption Probability Prediction
Classification / Natural Language Processing / Tableau/Dashboards
Built an app to predict new vaccine adoption based on opinion-orientated questions data from the H1N1 pandemic. Created SQL database and a model pipeline with Logistic Regression, Native Bayes, KNN, RandomForest, SVM, AdaBoost, and CatBoost.

Vincent Thompson

Vincent graduated from Duke University in 2018 with a B.S.E. in Mechanical Engineering, and spent two years in various rotations as an engineer in the Global Product Group at General Motors. He developed a strong interest in data science, having seen the value of machine learning in tech innovations in his own field, as well as the growing need for data products and insights across industries. Driven to make the jump into data science, Vincent self-taught python and chose to augment his technical background with the project-based curriculum of Metis. By combining a strong foundation in machine learning with his diverse experience in quantitative roles, Vincent is excited to use his skillset to contribute to data-driven innovations at his next company.
Eye Condition Detection With Deep Learning
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Created a CNN, built and trained on Google Cloud Platform, leveraging transfer learning to detect symptoms of various eye conditions from image data.

Zachary Brandt

Zachary Brandt is a music psychologist turned data scientist. He holds degrees in both music and psychology, and acquired a data science skillset to enhance his ability to solve research problems. His passion lies in merging the fields of music and data science by studying and analyzing how people and computers interact with music. Coming from an academic background, he is an effective educator, communicator, and problem-solver. He possesses a project-driven mindset and incorporates ideas from his diverse backgrounds, with an output including a podcast and website geared towards aspiring music psychologists, an MA dissertation on musical memory, and independent projects on music usage data and trends. Zachary is a motivated researcher, constantly seeking to learn more and improve the wellbeing of others through music and data science.
Topic Modeling Classical Music:Introducing the Song-Chord Matrix
Clustering / Natural Language Processing / Neural Networks
Created a model for harmonic complexity using NLP to topic model classical music based on musical chords and cluster composers by harmonic topics, and a neural network to generate original music based on MIDI files.

Zach Schaeffer

Zach is a recent graduate from the University of Florida where he studied International Business, Information Systems and Sustainability. He hopes to use data to inspire sustainable practice across the value chain, thereby saving resources, improving reputation, and ensuring long term business success. With a diverse background in finance, marketing and operations he is equipped to work in a cross-functional setting and hopes to make a tangible impact early and often.
Meeting European Energy Demand with Renewables
Neural Networks / Regression / Time Series/Forecasting
Forecasting energy demand, solar energy production, and wind energy production for 10 European nations using LSTM and ARIMA, aggregating information to aid decision making surrounding the global green energy transition.

Ryan Lewis

Currently located in Berkeley, Ryan holds a Bachelor's degree in Mechatronic Engineering and has primarily held positions at companies developing robotics and automation engineering products. His passion is for discovering how new technologies can be applied to existing problems in order to find simple and elegant solutions. His positive outlook and ability to convey complex principles in a digestible manner make him a joy to work with!
Using Reinforcement Learning to Train Autonomous Vehicles
Anomaly Detection / Big Data/Data Engineering / Neural Networks
Used Deep Reinforcement Learning to train and model autonomous vehicles interacting with human drivers in a simulated urban environment.


Adam Rauckhorst

Adam Rauckhorst is a Data Scientist with a background in business and operations. Prior to Metis, he oversaw operations for Greenback Expat Taxes, which earned a place on the Inc 5000 fastest growing companies during his tenure. He is passionate about using technology to achieve substantial business results. Adam earned a Bachelor's in Economics with high honors from the University of Texas at Austin.
Who Should I Follow? Recommending Twitch Streams Using Collaborative Filtering
Recommender Systems
In this project, Adam utilized multi-threading to gather data from the Twitch public API to create a collaborative filtering recommender system.

Allen Chen

Allen Chen is an actuary and data scientist. His data science journey began with a degree in Statistics from UC Berkeley. He then enjoyed a career as an actuary working in the health insurance industry. Allen used his time at Metis to gain additional skills and experience in the full stack data science workflow. He is excited to continue on his data science journey, where there is always more learning ahead.
Body Fat Estimation through Image Recognition
Cloud Computing (AWS/Google Cloud) / Neural Networks / Regression
Deep learning and image recognition are adapted to a regression task to estimate body fat percentage. A web app allows a user to upload a picture and receive their estimate.

Allen Ni

An inquisitive and explorative person by nature, Allen is interested in making solutions that improve people's lives. Experienced in biomedical research, social outreach, and startups/small businesses, he aims to make a lasting mark by helping others. A love for complex multidisciplinary problems and a wide ranging knowledge on various subjects, allows Allen to adapt easily to any situation.
Cross Media Recommendations
Big Data / Natural Language Processing / Recommender Systems
A recommender that can suggest books similar to a movie or tv show and vice versa, to expand the user's palette and prevent oversaturation of a specific medium

Andrew Underwood

Air Force veteran with five years of data analysis and project management experience. Skilled in working with cross-functional teams, implementing Agile and Scrum methodologies, and leveraging data to make critical decisions in challenging environments. Recently transitioned out of the military and keen to leverage leadership/technical expertise.
Make Your Move: Image Detection with Neural Networks
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Combined computer vision techniques and convolutional neural networks to accurately classify chess pieces and identify their location on a chessboard.

Andrew Wu

Andrew Wu, is a recent graduate of UCSD with a degree in Chemical Engineering. His passions are coding, data science, data analytics. He has worked in the Biomedical field in the past as an analytical chemist and recently in a non-profit political organization as a data lead. In his free time, he likes to play music. Andrew played the violin for almost 10 years and also plays several other instruments as well. He is also very into fitness and works out regularly. Andrew is friendly and a people-oriented person. He has held leadership positions in student organizations and is good at bringing people together. Furthermore, he has a curious mind and considers himself a lifelong learner!
Wildventures: A Photo-Based Travel Recommender System
Classification / Neural Networks / Recommender Systems
Wildventures, a photo-based travel recommender system that makes travel destination recommendations based on the user's previous travel photos.

Benjamin Shaiman

With a bachelors in Economics and History, Ben Shaiman is constantly driven by the desire to understand how the modern world works, but also just how it came into being. After graduating college Ben worked as an intern for the Freakonomics Radio podcast. With an academic background ranging from the history of antiquity to advanced statistics and economic theory, it was data where he found a calling. The unique challenge of communicating complexity to a lay audience, so critical in podcasting, drove Ben to pursue more technical experience.
Machine Learning Sommelier
Classification / Natural Language Processing / Natural Language Processing
Trained a neural network on over 250,000 wine reviews to classify wine varieties.

Binh Hoang

Binh is a data scientist transitioning from finance. He worked on the trading floor of D. E. Shaw & Co., a global investment and technology development firm, as a member of the portfolio financing team. His penchant for data analysis and visualizations led him to transition to data science. Outside of work, he is a community organizer in the queer and trans Asian Pacific Islander community. Binh graduated from Yale University with a B.A. in humanities.
A User-Empowered Anime Recommender System
Cloud Computing (AWS/Google Cloud) / Recommender Systems
Built an Anime recommender system based on how adventurous users want their recommendations to be.

Brian Nguyen

Brian obtained his master's degree at the University of Illinois studying physical chemistry. After graduation, he spent the following two years working in process sciences at a pharmaceutical company where he supported the release of a new drug to market from the data end. He has worked on generating and analyzing data for nearly ten years and joined Metis looking to expand his tool kit for data-driven insights. Brian looks forward to taking the next step as a data scientist.
Tunable Movie Recommender
Natural Language Processing / Recommender Systems
Created a recommender system using three content filters and a collaborative filter. The recommender allows the user to tune recommendations based on the interest of the synopsis, theme, crew, and ratings.

Bryan Ross

Bryan graduated with an M.A. in Clinical Psychology from California State University, Northridge. Having strong interests in quantitative methods and consumer behavior, much of his research concerned predicting these behaviors and consumer-brand relationships. He pursued these interests professionally at a market research firm in Los Angeles. He worked with the Chief Innovation Officer, helping to develop insightful tools for use in company studies. In this role, he assisted with the design of a variety research and development studies, in addition to being the primary analyst. Growing more passionate about machine learning and programming, Bryan decided to attend Metis to strengthen these skills and pursue a career in data science.
Reddit on Relationships
Natural Language Processing / Neural Networks / Recommender Systems
Used a combination of Latent Dirichlet Allocation and BERT embeddings to create a content-based recommender system. Users can enter their relationship problem(s) into a Flask app and receive relationship advice posts from Reddit.

Cianan Murphy

Cianan recently graduated from UCLA with a BS in Biophysics. He has worked in several research labs ranging from Neurophysics to genetics to molecular biology. He also interned at a startup medical device company and helped with experiment design and overview. His passion for data science stems from these research experiences where his favorite part of the research was examining the collected data and drawing conclusions from the experiments. Cianan loves problem-solving and coming up with unique insights through thorough investigation. He works great in teams, loves learning, and is always looking to improve.
Wildfire Size Prediction
Anomaly Detection / Classification / Time Series/Forecasting
Cianan's project is about making a model that predicts how large a wildfire would be, if one were to start throughout California.

Cynthia Wang

Cynthia holds a B.S. in Joint Economics and Math and a B.S. in Clinical Psychology with honors from University of California, San Diego. Her education in math and statistics and experience in psychological research in decision-making sparked her interest in data science. After she graduated from college, she earned a Master’s degree in Quantitative Methods in Social Sciences from Columbia University where she practiced data science in social science disciplines and harnessed programming skills in Python, R and SQL. Cynthia has a strong curiosity and is passionate about using data science to understand human behavior and offer solutions to the real-world challenges. With her knowledge in machine learning, statistical analysis and psychology, she’s excited to start her career as a data scientist.
Classical Music Generation with Deep Learning
Big Data / Cloud Computing (AWS/Google Cloud) / Neural Networks
This project uses deep learning to explore the intersection between arts and data science. It utilizes a model architecture with GRU and self-attention to generate classical piano music.

David Weon

David is a Data Scientist based in New York City, working on several types of machine learning projects during his time at Metis. These projects cover linear regression, classification, natural language processing, and deep learning. David's goal as a data scientist is to ask the right questions and craft stories out of the raw data. His previous experiences include a variety of roles in the healthcare/technology industry at Komodo Health, Gerson Lehrman Group, and Columbia University Irving Medical Center. David graduated from Columbia University with a master's degree in bioethics, focusing on healthcare data and data privacy.
Interpreting Speech Emotion for the Hearing-Impaired
Classification / Neural Networks / Tableau/Dashboards
Some of the greatest challenges as a hearing impaired individual or in communicating with a hearing impaired individual arise when trying to start a conversation and when conveying emotion or intent. To address this challenge David built a convolutional neural network model to classify speech emotion and the gender of the speaker. He also created an app that processes input audio and notifies the user of the speaker's gender and the sentiment of the speech.

Edith Johnston

Edith is a creative thinker with a passion for learning new and diverse skills and information. A Bay Area native, she graduated from the University of New Mexico in Spring 2019 with a B.S. in Applied Mathematics. Edith has a wide variety of interests, ranging from astrophysics and chemistry (how the world works) to evolutionary anthropology and psychology (how people work). She is an adaptive problem solver with dogged persistence in the face of challenges, and thrives in the analytic, logic based environment of data science. In her free time, Edith enjoys creating beautiful things, such as embroidery, painting, weaving and crochet, and caring for her many houseplants.
Classifying Stellar Spectra
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Using data from stellar spectrographs, build a model to classify stars by Morgan-Keenan spectral type. Successfully implemented both a gradient boosted classifier and a neural network model.

Eliza Eshet

Eliza graduated from Indiana University in 2017 with a B.A. in cognitive science. Since then she’s traveled extensively and worked in a variety of industries. Returning to her undergrad roots of science and math, Eliza began coding again and decided to pursue more technically focused roles. To close the gap, she applied to Metis and threw herself into four independently designed projects from Natural language processing of comedy podcasts to classifying chest X-rays using a neural network.
Chest X-ray Classification
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
With the intent to lighten the load for medical field workers. This project utilized a convolutional neural network to examine and classify normal, pneumonia, and corona chest X-ray images.

Emily Ng

Emily graduated from the University of Illinois at Urbana-Champaign with a double major in Physics and Astronomy. She has experience in data processing and analysis while working with a wide range of astronomical data. After dipping into computer programming and machine learning concepts in undergrad, Emily developed an interest in leveraging data science for everyday technology and business applications. She is driven by her deep curiosity of the world and is quick to learn new things. Emily is optimistic about diving into the world of data and is most excited to apply her strong analytical skills to solve complex real-world problems.
What to Wear: A Clothing Classifier
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Uses a neural network and transfer learning to classify images of clothing pertaining to different styles.

Emmanuel Rodriguez

Born and raised in the New Jersey/ New York City area, I have always been passionate about learning new things and solving big problems. I have a B.S. in Computer Science, a minor in Mathematics, and three years of experience teaching the two subjects at the high school level. Currently, I am excited about exploring the world of Data Science and progressing in my career by continuing to infuse my passions for Math and Computer Science. For fun, I love to write and record music, spend time playing through my board game collection, and find new ways to maintain my athleticism and good health.
Podcast Recommender
Natural Language Processing / Neural Networks / Recommender Systems
This project offers guidance for people new to podcast listening. The Flask app stores user likes and uses a Word Embedding Neural Network to generate search results and recommendations from "This American Life."

Esteban Zuniga

A former History teacher, Esteban holds a B.A. in History from The City College of New York with several courses in economics and jazz studies. Prior to joining Metis he was a law student, but it was his fascination with business efficiency and problem solving that led him to pursue a more technically focused, full-time career in data science. Outside of data science Esteban enjoys playing tennis and has been a guitar tutor for 20 years.
Dog/Cat Audio Classification
Classification / Cloud Computing (AWS/Google Cloud) / Cloud Computing (AWS/Google Cloud)
Took audio files of dogs barking and cats meowing and converted them into spectrograms and then built a neural network from scratch to classify and predict if the image was a dog or a cat.

Faustina Maria Giaquinta

Formerly an Agricultural Engineer, Faustina’s passion for data was sparked by her experience in soil analysis and statistics. Realizing that data-informed decision-making could, for example, increase crop yield in her native Argentina, she transitioned to full-time work in data science. Faustina works side-by-side with individuals and organizations to solve problems using Machine Learning solutions.
Faustina Maria
Controlled Chaos: A tool for Taming Digital Clutter
Clustering / Natural Language Processing / SQL
A web application running locally and automatically that organizes text files based on its content. Controlled Chaos maps and classifies files, builds file clusters, and indexes them for easy application-based access.

Frederick Lam

Frederick Lam has a B.A. from Roosevelt University, majoring in Actuarial Science and a minor in Finance. During his studies, he found interest in the data manipulation and analysis side of Actuarial Science and it inspired him to pursue more data-focused roles after graduation, where he found out about the Data Science field. After searching for a year, he was pointed to the Metis Data Science bootcamp by a fellow Data Scientist. As a student at Metis, he took the opportunity to combine his passions, such as anime and video games, with complex machine learning algorithms and modeling. As a result, he created an anime recommender, based on the collaborative filtering technique, as his final project at Metis. His motivation for this recommender was not only to present the skills he'd learned at Metis but also to showcase how data science can be applicable to a general level of interaction and understanding.
Satisfying Your Anime Needs
Recommender Systems
This project is a take on Collaborative Filtering Recommender systems and how it works on a simple user-based level. This project also focuses on taking data from a reputable source, MyAnimeList, and creating an interactive recommender app using it.

Giovanni DeLisa

Gio DeLisa has a BA in Economics with a minor in web development from New York University. Prior to Metis he helped to build and grow a small New York based apparel brand with a focus on local manufacturing. Always interested in learning, Gio is looking forward to being part of the data science community and its continual developments.
Forecasting for Fantasy Birding
Big Data / Neural Networks / Time Series/Forecasting
Developed convolutional sequence-to-sequence neural network to forecast expected amout of recorded bird ovservations in over 4,000 east coast locations. Used dask to process 100M+ bird obsrevations. Used the H3 library for geospacial indexing.

Harry John Shephard

Harry John Shephard is a theatre nerd and board game geek who has worked in the nonprofit sector for more than a decade and has seen many opportunities for institutions to use data science but haven't incorporated it into their services and organizational processes. Transitioning into data, he is eager to work in an business with a strong data culture where he can learn from experienced and skilled leaders who are enthusiastic about using data in creative and surprising ways. He is particularly interested in NLP focused projects. If you know of a nonprofit, community organization, or small independent business that could use some pro-bono data work, please don't hesitate to pass on his information to them.
Harry John
The Language of Leftovers: Exploring Communication In Eating Disorder in Online Communities
Classification / Cloud Computing (AWS/Google Cloud) / Natural Language Processing
Explored trends and looked for connections in the words used between different Reddit sub-forums within communities directly or indirectly related to eating disorders.

Isaac Wang

Isaac graduated from UT Austin with a BBA in Management Information Systems. Prior to joining Metis, Isaac was a data analyst at United Airlines, where he oversaw Voices, and internal app for flight attendants to report operational issues. Isaac built dashboards in Palantir for various departments to identify and track key trending issues. Utilizing many different data science concepts, he performed logistic regression to predict flight attendant churn and natural language processing to perform sentiment analysis on newly offered United in-flight products. Isaac also has Tableau experience, building an end to end fully interactive, functional dashboard for operational teams to track productivity. Now, Isaac is looking to build off of his strong analytical background and pursue a career in data science.
Generating Lo-Fi Music with Neural Networks
Big Data / Cloud Computing (AWS/Google Cloud) / Neural Networks
Utilizing deep learning, applied knowledge of neural networks, specifically LSTM and self-attention to generate Lo-Fi music (chill, distorted short jazz loops with a beat).

Jacky Lu

Before joining Metis, Jacky was a Life Science Product Specialist at Quartzy. He assisted customers with technical questions about products, curated a database of scientifically equivalent product recommendations, and maintained the integrity and clarity of data for a catalog of over 10 million life science products. He wants to combine his interests in operations, logistics, and technical support with his skills in data analytics, project design, and programming in his pursuit of a full-time career in data science. Jacky holds a Bachelor of Science in Molecular Biology from UC San Diego.
Exploring U.S. National Parks
Neural Networks / Recommender Systems / Time Series/Forecasting
Jacky used a SARIMA time series model to predict monthly visitor counts for national parks. Additionally, he built a content based national park recommender using a topic activity cosine similarity matrix.

Jimmy Blezin

Jimmy graduated from Clark Atlanta University obtaining a B.A. in Finance & Computer Science. The find a way or make one approach, contributes to his passion for providing voice to data through storytelling that cultivates action. Prior to being a Metis alumnus, his career spans across multiple industries such as Banking, Investment Management, and Healthcare. These acquired skill sets positions him to close gaps amongst technical & non-technical stakeholders. Overall, the enthusiasm about embedding his entrepreneurial spirit to create innovative solutions as a data scientist is invaluable.
Classification / Neural Networks / Recommender Systems
Developed a sneaker recommendation system. Pictures were leveraged to train a convolutional neural network (VGG16) which identified & classified sneakers by brand.

John Guinn

Recent Data Scientist bootcamp graduate with design background in architecture; fascinated by the possibilities of machine learning and big data. Prior to the Data Science Bootcamp, John was the Technical Director and a Project Manager at a creative/design oriented architecture + real-estate development practice.
Architectural Form Generation using LiDAR Surveys of Architectural Interiors
Cloud Computing (AWS/Google Cloud) / Neural Networks
3D Form generation using VAE + CNF and LiDAR Surveys of Building Interiors.

Jon Lindenauer

Jon is a long-time Statistician and in recent years has transitioned into Data Science. He has an MS in OR & Statistics and has worked in both academia and business settings. The main tools he has used throughout his career are SAS, Simca and R for analysis and visualization. His main focus was design of experiments, predictive models, statistical process control and multivariate analysis. Most recently Jon was consulting through is own business, but faced some challenges in maintaining his client base when the coronavirus pandemic hit. Jon wanted to attend Metis to expand his analytical toolkit and felt like doing so during the pandemic was good timing. He feels his skills using Python and SQL, as well as AWS have improved greatly. He now has a more robust skillset when it comes to machine learning and natural language processing and is ready to use these skills to contribute to an organization as a data scientist.
Predicting Loan Profit Margin
Regression / SQL / Tableau/Dashboards
A finance company is concerned that their profit margins are declining. They want to create a prediction of the profit margin target using various loan and customer features.

Jonathan Kang

With over a decade of experience in the management consulting industry, Jonathan primarily focused on Business Intelligence and Visualization and is looking to leverage those skills with full data science tool kit. Jonathan is an avid traveler of the world, a car enthusiast, a husband and a father of 3.
Language Recognition via Neural Net
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Harnessed the power of a Neural Network to detect and predict language spoken from audio clips

Joseph Grovers

Former graduate of UC Davis with a degree in Economics and Specialty in data analytics & economic analysis. After graduating, Joseph worked 2 years for YouGov as a research analyst, before ultimately wanting to take his skillset further to become a data scientist and enrolling in Metis.
COVID-19's Impact on the 2020 Election
Classification / Regression / Time Series/Forecasting
Looking into COVID-19, polling, and past voting datasets to make findings about how COVID-19 has affected the 2020 political landscape.

Jung-a Kim

Jung-a Kim is a data scientist with background in statistics. Previously, she worked as a contractor in Intuit where she demonstrated benchmarking a pre-published deep kernel learning model and she worked as a web-programmer in a database company. Her problem-solving skills have been endorsed by colleagues and she has a strong leadership in projects. She earned a M.S. in Statistics from San Jose State University and two years of coursework in Computer Science at De Anza College.
Review-Based Search Engine
Natural Language Processing / Neural Networks / Recommender Systems
A review-based search engine outputs optimal products whose reviews match key qualities from queries. It computes 'Positive Similarity score' which is a combination of positive sentiment score, similarity matrix, and weights on the keywords.

Justin Chan

Justin is a Bay Area native with a B.S in Accounting from Santa Clara University. His previous role was in Non-Profit Finance at the Kenneth Rainin Foundation. During that time, he was tasked to create various dashboards that were used for investment decisions. Most importantly, he scrutinized and ensured the accuracy of any report or dashboard so that the investment team can be 100% confident in their decision making. He's a highly focused individual, making sure there's no stones left unturned on whatever task he seeks out to finish. On his free time he enjoys golfing!
Handgun Detection
Classification / Neural Networks
Using neural networks for handgun detection within a video.

Kelsey Glenn

Kelsey graduated from the University of California, Davis in 2017. While pursuing his degree in linguistics, he developed an initial interest in data science through coursework in computational linguistics and NLP. After gaining experience in political research, he spent the past two years teaching high school speech and debate in the San Francisco Bay Area. But, despite a passion for helping his students find success, a growing desire to further his journey into the world of data and A.I. drove him to enroll at Metis. By combining a strong foundation in machine learning with his background in education and qualitative research, he seeks to bring data-driven perspectives to big-picture strategic thinking.
AniMaker: AI-Generated Story Concepts from Anime Plot Synopsis
Natural Language Processing / Neural Networks / Regression
Generation and curation of unique story concepts through training GPT-2 and regression models on Anime series plot synopsis and community ratings.

Krystyna Metcalf

Krystyna Metcalf holds a Bachelor of Science in Finance and International Management from Boston University. Prior to joining Metis, she was a Senior Equity Research Associate for nearly seven years covering the Household & Personal Care industry. Here she developed a passion for analyzing big data to identify patterns and predict trends. Outside of work, Krystyna is a passionate equestrian who has successfully competed on the international level.
Winston Churchill: A Study in Oratory Excellency
Cloud Computing (AWS/Google Cloud) / Natural Language Processing / Tableau/Dashboards
Through the use of Natural Language Processing, Krystyna analyzed Winston Churchill's speeches to discover major themes and how they changed over the years. She then compared the topics to current events at the time.

Lauren Faulds

Lauren was drawn to data during her time as a massage therapist and sole proprietor of her business. Realizing the value in working with the health care data created at her work she began studying data analysis in R. Learning fundamentals of statistics became an interest of hers at the San Francisco City College. Python is now the language she is most fluent in in her work as a data scientist. In her free time she enjoys pattern making and bird watching. She appreciates the chances to observe unseen aspects of daily life during her time with data.
Bird Counts of Costa Rican
Big Data / SQL / Time Series/Forecasting
Bird Counts of Costa Rica are forecasted using Vector Auto Regression algorithm. These predictions are made from citizen science project eBird data. Google Cloud Platform's Spark Data Proc Cluster facilitates cloud computing needs.

Lindsay Read

After graduating from Colorado State University in 2018 with degrees in Microbiology and Spanish, Lindsay worked as a Microbiologist and shift lead, training new employees and assuring quality and timely results to customers. Considering a career in Epidemiology, she became fascinated by the integral part data played in highlighting how disease outbreaks could be mitigated and/or prevented. This passion, along with a lifelong admiration for statistics and data visualization, led Lindsay to embark upon her journey to data science. For her final project, she used NLP and feature engineering techniques to create an interactive recommendation system for hotels in Puerto Vallarta, Mexico.
Hotel Recommender for Puerto Vallarta, Mexico
Natural Language Processing / Recommender Systems / Tableau/Dashboards
Combined NLP and feature engineering techniques to create a search engine that applies transfer learning from word embeddings to recommend the most (cosine) similar hotel(s) for a user based on his/her text input and selection of features.

Louis Sagan

Louis has never had a problem asking questions- his curiosity seemingly knows no bounds- and data science allows him to put that curiosity into practice. From coming up with questions to finding insights in the results, there is no shortage of interesting discoveries to be made along the way. In a field that is continuously expanding, Louis finds himself well-prepared and excited to get started.
Visualizing Policing in Chicago
Classification / Tableau/Dashboards
Can we predict if a reported crime in Chicago will result in an arrest based on surrounding area of the crime?

Manuel Ledo

Originally from Spain, Manuel has lived in New York for the last 6 years working in a neuroscience research laboratory at SUNY Downstate studying perception. His background pivots between Optometry and Physics. Before moving to the US he ran his own business, but always had a special interest in science, which is what brought him to the US. Now, looking for a career change, data science is the perfect match, a balance between continuous learning, business perspective, and applying his experience to new challenges.
Happy Flights
Big Data / Cloud Computing (AWS/Google Cloud) / SQL
Inspired by the business traveler, used Neo4j to gain a deeper understanding of the US airports network structure applying different centrality, clustering and path finding algorithms.

Maryam Ghaseri

Maryam has a strong background in mathematics and statistics and holds an M.S. in Applied Mathematics (Actuarial Science) from the University of Illinois at Urbana Champaign. Following grad school, she joined PricewaterhouseCoopers (PwC) in the insurance and M&A industry. She worked as a manager of Actuarial Science, supervised her teams to perform actuarial valuations, and consulted clients on pension plan designs and risk mitigation, often tackling multiple projects and deadlines at the same time. She enjoyed the heavily analytical and data-oriented nature of her work and was often using SAS, Alteryx, and Tableau. Following that passion for data analytics and machine learning, she decided to pursue a career in data science.
AI Generated Image to Audio Captions
Cloud Computing (AWS/Google Cloud) / Natural Language Processing / Neural Networks
Generated audio captions for images using Neural Networks to help people with visual impairments. Analyzed images using CNN and text data using LSTM, and then converted captions to audio using WaveNet.

Max Currier

Max Currier is a data scientist, analyst and experienced manager of data-driven teams. Previously, he served as a Senior Project Manager at Gartner and as Analytics Manager at China Beige Book International where he managed large teams of analysts and data collection specialists through numerous large-scale, fast-paced projects that were central to their firms’ core business. Max’s natural affinity for problem solving has lead him from a BA in Chinese language to working in the data and analytics space, and now ultimately to pursuing a career in data science.
SoftCopy: Image-based Book Recommendation
Natural Language Processing / Neural Networks
SoftCopy is an app that generates book suggestions based off images of a user’s book collection by performing optical character recognition and employing a collaborative filtering recommender system.

Michael Paig

Michael graduated from the University of Maryland, College Park with a degree in Economics. After graduating, he worked for a public health organization for five years where his most recent role was a production coordinator. In his role, he got exposed to learning and using data visualization with Tableau. By learning about data visualization, it peaked his interest in data science where he wanted to learn more about analyzing data and machine learning. For his final project, he used deep learning to see if he could detect anger in text.
Angry Text Detector
Classification / Natural Language Processing / Neural Networks
Michael set out to analyze the sentiment of text. He wanted to predict whether a text was angry or not. He did topic modeling, created a recurrent neural network model, and created a Streamlit app.

Nick Horton

Nick is motivated, competent, and hard working and holds a bachelors in Anthropology from UCSB. Nick believes that his background as a technical writer, where he consulted with engineers and a high school tutor, where he was recognized for reaching difficult students provides him with a rich experience where he can contribute meaningfully to a team.
Image Creation with Generative Adversarial Networks
Neural Networks
3 web apps for manipulating images in different ways - style transfer, face swap, and latent representation blending.

Paul Chung

Before enrolling in Metis, Paul worked in the commercial real estate industry for 15 years performing transaction and asset management advisory services for institutional investors. As he started dealing with larger sets of data on a daily basis, he felt that he needed more than VBA in MS Excel to get the most out of his analyses. Given his education in Electrical Engineering, moving into the data science field seemed to be next sensible step.
Nudge Your Run Pace with Music
SQL / Tableau/Dashboards / Time Series/Forecasting
Coded a model that predicts a runner's pace during various race segments and adjusts the runner's music to either dial up or pull back their effort.

Ramon Martin

Ramon served in US Army for 10 years as infantry squad leader and special operations engineer. After his discharge he received Bachelor's degree in Robotics/Mechatronic Engineering and worked as Datacenter Technician, Manufacturing Engineer and a technical cofounder for Aviation eLearning App. With a strong engineering background, Ramon is leveraging his previous experience with data science.
Automatic Mask and Temp Station
Anomaly Detection / Classification
Automatic contact free station that detects a person's temperature and if they are wearing a mask.

Raymond Yang

Raymond graduated with a BA in Computer Science from UC Berkeley. Prior to Metis, he was a quantitative researcher at Old Mission Capital where he led the research efforts on their options market making desk and managed risk across two low-latency strategies. At Berkeley, he has done research under several professors in computational game theory and latency arbitrage. He co-founded Fluint while in school, an angel-backed startup providing an online marketplace for peer-to-peer foreign currency exchange. Raymond enjoys taking on challenges in the areas of quantitative finance, behavioral sciences, computer vision, and game theory. Outside of work hours, he’s an avid poker player and travel photographer. Ask him for international travel and food recommendations or tips on collecting credit card bonuses.
AttackGAN: Adversarial Attacks Using GANs
Classification / Neural Networks
Researched new methods to generate adversarial examples using generative adversarial nets to fool deep image classifiers.

Rudy Wang

Rudy holds a BS in Finance and has three years of experience in financial risk and regulation. His curiosity for combing through Big Data and delivering innovative solutions and strategies combined with his analytical and quantitative skill sets is the perfect match for a career in data science. With data processing and visualization tools , Rudy looks to help businesses drive strong results-oriented solutions.
Mask On: A Push for Social Change
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Combining computer vision with social media to promote mask wearing as a social norm.

Ryan Richbourg

Ryan Richbourg earned a BBA from Baylor University with dual majors in Entrepreneurship and Supply Chain Management. Prior to Metis, he worked for three years in sales and marketing roles across several industries including research technology, outdoor recreation, and e-commerce. He transitioned to data science because he loves continuously learning, solving unique challenges, and using programming to extract insights. Ryan's mission as a data scientist is to help organizations translate their business problems into technical solutions using data. His final project at Metis uses natural language processing techniques to analyze topics and create a post recommender for Seth Godin's blog on marketing and growth.
Text Analysis on Seth Godin's Marketing Blog
Clustering / Natural Language Processing / Recommender Systems
Scraped 7500 posts from Seth Godin's marketing blog and clustered them into 7 main topics using NLP topic modeling. Additionally created a content-similarity based recommender system inside an interactive web app to suggest further reading.

Sam Mize

Sam began his career as a mechanical/controls/robotics engineer in the aerospace manufacturing industry. He encountered data analysis and statistics in the context of measurement, inspection, alignment, compensation, and qualification of parts and machinery and developed an interest in building further machine learning and programming skills at Metis to take part in the ongoing new industrial revolution. His areas of interest include manufacturing, industry, mechanical design, robotics, signal processing, sustainability, and environmental policy.
Happy Little Convolutions
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Sam adapted a published photo-to-Monet CycleGAN to train on web-scraped Bob Ross fan art instead, as a learning exercise for neural nets with TensorFlow and cloud computing with GCP and Vast.AI.

Sasha Prokhorova

Sasha Prokhorova holds a B.A. in Linguistics from Smolensk State University and a B.S. in Electrical Engineering from San Francisco State University. Her love for programming, problem-solving and data visualization led her to pursue a full-time career in Data Science. For her final project at Metis she analyzed data obtained through The New York Times Archive API to derive insights about gender representation in the media throughout the past 70 years. She hosts a podcast on YouTube called SashaTalksTech, where she interviews a variety of data science professionals from all over the world, sharing their knowledge, expertise and success.
Women Through the Lens of The New York Times
Natural Language Processing / Tableau/Dashboards
Data journalism research on gender equality and representation in The New York Times. Data collection through the Archive API for 1950 - 2020, topic modeling using SpaCy, gensim and Latent Dirichlet Allocation technique. Frequent term visualization based on the keywords and the timeframe. Sentiment analysis using NLTK Vader.

Sean Sisler

Sean graduated from University at Buffalo with a Bachelor's in English Literature. His data science skill set was developed at Metis where he defined, executed and presented five full scale projects based on regression, classification, and unsupervised learning techniques. Prior to Metis, Sean worked in the hospitality industry and wrote creatively. He looks forward to leveraging his passion for storytelling in a data driven role.
Tremendous Patterns in Speech: An analysis of the Trump Administration
Natural Language Processing / Tableau/Dashboards / Time Series/Forecasting
An analysis of the President and his press secretaries using transcripts from the White House Press Briefing and Press Releases.

Sophia Li

Sophia holds a Masters degree in Accounting Science and Finance from the University of Illinois at Urbana-Champaign. Prior to joining Metis, she spent years at Ernst & Young as a Consultant and a Project Manager before transitioning to Merrill Lynch where she worked closely with the executives to deploy market wide strategic proposals. After spending nearly a decade in the financial services industry, Sophia wanted a new challenge where she could utilize her ability to capture the big picture concepts and her analytical nature. A career in data science was the perfect progression. Through Metis, she learned new tools and gained hands on experience solving real world problems.
Supervised Image Classification: Let's Get Toned!
Big Data / Classification / Neural Networks
Created a streamlit app designed to detect a person’s skin undertone using a supervised image classifier built with convolutional neural network (CNN) and transfer learning.

Stephen Ianno

After graduating from Wheaton College with a Physics major and Mathematics minor, Stephen has spent the past seven years overseas working as a professional educator of both Science and English, accumulating both a Teaching Certification and CELTA in the process. Since encountering computer programming in early 2020, he has become enthralled with the field and the abstraction and critical thinking demanded of it. For his final project, Stephen manipulates an autoencoder neural network in order to generate new and unique works of art. In his personal life, Stephen is an avid traveller and adventure seeker as well as a loving husband and father.
Machine Art: Generating images with a Convolutional Neural Net
Clustering / Neural Networks / Recommender Systems
Convolutional Neural Nets are frequently used to process and classify images. This project takes that function one step forward by using a neural net to create new and unique artistic images.

Stephen Kaplan

Stephen is a graduate of the University of California - Berkeley, where he obtained a B.S. in Mechanical & Nuclear Engineering. After graduating in 2014, he spent a couple of years at two Bay Area startups in the green-tech space as a Software Engineer. Most recently he worked as an engineer at a utility-scale solar panel manufacturer, researching and developing algorithms to predict the performance of solar power plants, analyzing power plant performance via high resolution time series data, and developing software in Python to enhance the company's analytics tools. His fascination with machine learning and artificial intelligence grew over the past few years until he decided to enroll at Metis. Stephen is now based in Denver, Colorado. While he is particularly interested in finding ways to apply machine learning to renewable energy and music technology, he is open to a wide range of opportunities.
Object Detection for Autonomous Snow Grooming Applications
Big Data / Classification / Neural Networks
A proof of concept for autonomous snow grooming vehicles at ski resorts. Trained a Faster R-CNN object detection model in PyTorch to detect/classify obstacles in dash cam footage from vehicles moving through ski resort.

TJ Burleson

TJ (he/him) has developed an interest in how we look for patterns and construct meaning, based on his studies of Linguistics and Theatre at Rice University. He has worked in the theatre industry for the past seven years in New York; mainly backstage and on the design teams for immersive experiences like Third Rail Projects’ Then She Fell and Behind the City, as well as Broadway and off-Broadway shows like Moulin Rouge!, The Band’s Visit, The Lucky Ones, and KPOP. TJ sees data science as a broadening of the scope of his analytic endeavors beyond the worlds of linguistics and theatre. Well-designed visualizations and engaging topics are important to his projects. He looks forward to learning more and continuing his data science journey.
An Ode to Data Science: Generating Poetry with Neural Networks
Cloud Computing (AWS/Google Cloud) / Cloud Computing (AWS/Google Cloud) / Neural Networks
Trained Markov chains via markovify, an LSTM model, and GPT-2 (124M) on three poetry datasets to study the effects of text size and homogeneity on text generation and the strengths and weaknesses of each model.

Will Stith

I'm from Chicago, where I'm also currently based. Before coming to Metis, I was a research student, most recently using neuroimaging to study the link between temporal lobe epilepsy and mood disorders. I graduated from Notre Dame in 2015, where I studied biology, and I earned my master's from Rush University in 2018. While neuroscience remains a topic of great interest to me, the data science skills I've developed at Metis have led me to expand my horizons to new fields. Apart from data science, I enjoy board games, live music, reading, cooking, and trivia. I love animals, especially dogs and birds.
Bird Image Classification Using CNNs and Transfer Learning
Classification / Neural Networks
Using convolutional neural networks and transfer learning, created a program to identify the species of a bird from a photo and then sort that photo based on the species.


Daria Morgan

Daria holds a degree in Economics from Siberian Federal University. She previously worked as a Credit Analyst at Sberbank, Russia’s largest bank. During her time there, she worked on approving ~$100M of corporate loans. Additionally, Daria worked as a data and analytics intern for, a venture backed ecommerce startup, which ignited her passion about data and its ability to help drive key business decisions. While at Metis she has used this motivation to create projects with specific business insights – including helping an emerging bank make better loan decisions and improving an online clothing brand make actionable insights form their reviews. In her free time, she loves hiking (and plans to go to every National Park), running, and travelling.
Optimizing Online Advertisements with Convolutional Neural Networks
Classification / Natural Language Processing / SQL
Helping companies improve ad efficiency by matching their advertisements to website content using a convolutional neural network model to classify images and choose the closest to reflect what is on each webpage.

Dmitri Gourianov

Dmitri graduated with a M.S. in Physics from Moscow University and MBA in Finance from St. John’s University. Passionate about applying scientific methods and creating methodology to systematically improve decision making, Dmitri spent last 10 years in commercial real estate focusing on market analysis, consulting, investment analysis, and cash flow projections. Strong believer in technology, Dmitri utilized his background in solving business problems or accomplishing desired business outcomes. Through that interest in data science insights, Dmitri decided to pivot his career and joined Metis.
Food Image Classification Using Deep Learning
Classification / SQL / Cloud Computing (AWS/Google Cloud)
Created a web app to classify food images. Convolutional Neural Network model was designed based on transfer learning framework, and trained on Google Cloud Platform.

Ethan Liwanag

A data scientist and Stanford graduate focused on leveraging data to help guide business decisions. A quick learner who utilizes a balance of analytical interpersonal and skills to problem solve and consistently deliver on goals.
Predicting the Winner of Counter Strike Rounds
Time Series/Forecasting / Natural Language Processing / SQL
Used Random Forest and extensive feature engineering to correctly classify the winner of CS:GO rounds.

Jeff Obhas

Jeff is a military combat veteran with a background in financial services. He holds an MBA from Baruch College and a B.A in Psychology from Binghamton University. His love for data science stems from his time deployed overseas where he used data analysis and problem-solving to recognize enemy attack patterns and predict their movement. In his most recent role, Jeff led business advisory projects at EY, using statistics to help multinational banks optimize their research and development credit. Along with his passion for data analysis, Jeff voluntarily prepares tax returns for low income families. He also enjoys the outdoors and has summited the tallest mountains in the US. For his final project, Jeff is building a neural network to identify automobiles to facilitate a safer autonomous driven world.
Image Classification with Convolutional Neural Networks
Big Data / Classification / Natural Language Processing
Created convolutional neural network model to classify images with high accuracy and facilitate autonomous vehicle operations.

James Blau

Before studying data science, James studied physics at Harvey Mudd College, worked in biomedical research, tutored math and science, and worked as a software engineer designing big data tools, for applications such as product recommendation for retail, and entity recognition for the financial sector. He's excited to leverage his math and science background to help businesses make smart decisions as a data scientist.
Recognizing Game States in Super Smash Bros. Melee
Recommender Systems / Cloud Computing (AWS/Google Cloud) / Big Data
Trained a neural net to recognize the state of a character in a fighting game from still frames of videos of matches.

Joshua N. Kendrick

Joshua is a life long lover of learning, and he greatly enjoys the deeper understanding of complex systems. Joshua is interested in the practical application of data science to business decision-making. He practiced law happily in Florida for some years but decided he wanted to begin pursuing data analytics by pursuing an MBA with a Business Analytics Concentration. Subsequently, Joshua continued to teach himself independently online, and now he has completed the Data Science Boot Camp at Metis. Joshua would like to be a balanced contributor in a collaborative team environment, and he hopes to offer a bit of everything, i.e. data gathering and exploration, analysis and interpretation, and communication and insight. Hopefully, his non-traditional background in getting here will complement your team and provide a helpful perspective.
Joshua N.
Mining the Cart: Customer Segmentation Analysis
Time Series/Forecasting / Classification / SQL
The customer segmentation of retail consumers via the K-means algorithm using a large transactional retail dataset, and the interpretation of these clusters characteristics and implications for the business.

Luke LaJoie

Luke is a native Californian and has lived most of his life in Silicon Valley. He served a few years in the US Army and enjoyed jumping out of perfectly good airplanes. Luke studied math as an undergrad and bioinformatics as a graduate student. He's always been interested in math, science, and technology, so he looks forward to working as a data scientist as it combines all those disciplines.
Human Protein Image Classification
Regression / Classification / Natural Language Processing
The goal of this project was to develop a binary classification model to distinguish between proteins in microscope images.

Max Garber

Max attended the University of California Santa Barbara where he obtained a Bachelor's degree in Physics. After college he worked at a startup, Wyatt Aerosol Systems, developing a system for aerosol particle characterization. Max moved to Chicago with his significant other where he has been continuing to expand his knowledge in data science and analytics. In addition to competing and finding joy in Kaggle competitions, Max decided to enroll at Metis to advance his knowledge in data science. He looks forward to pursuing a career in the analytical space.
Custom PC Build Recommender
Clustering / Recommender Systems
Developed a recommender system for custom PC-builds using data scraped from

Rajkumar Katta

Experienced data scientist with a background in Finance. Interested in all things data, from building an end to end data pipeline to ETL, data exploration and analysis. Particularly interested in gaining insight from data by building and deploying machine learning models and strategizing based on those insights.
Garbage Classification using CNNs
Cloud Computing (AWS/Google Cloud) / Regression / SQL
Identifying pieces of trash as either organic or recyclable using CNNs.

Paul Chang

Paul graduated from NYU with a degree in Economic Policy. He was a Data Analyst at Digitals where he worked with Comcast Residential and Comcast Business. Prior to Digitas, he served as a Business/Analytics Analyst for Mansueto Ventures. Paul is excited about leveraging his past experience towards a full-time career in data science.
Medicine Review Classification
Classification / Natural Language Processing / Tableau/Dashboards
Built a text (sentiment) classification model and applied NLP topic modeling techniques using medicine reviews.

Xin Cheng

Xin Cheng holds a MSc in Biological engineering from the University of Georgia. Prior to joining Metis he was a scientist at Synthego doing gene editing in production group. His love of data mining, problem-solving, and programming led him to pursue a full-time career in data science. For his final project he is analyzing depressive posts on social media and made a joke generator with LSTM to make people happy.
Depression Posts Analysis with NLP
Clustering / Natural Language Processing
Analyzed depression sub-reddit posts with natural language processing. Also made a joke generator in hope to benefit those depressed.