Which Bootcamp is Right for Your Career Goals? Explore Programs

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 2021

Connie Xiao, Data Science & Machine Learning

Connie graduated from Stony Brook University with a B.S in Respiratory Care Therapy specialized in working with pediatrics and neonates. During her time at NYU Langone Health she often found herself working with data and was intrigued when she was analyzing patient data. While learning programming in her spare time, she realized how passionate and enthusiastic she became when applying data analytics to unravel patterns. Connie then made the leap of faith to transition from patient care to data science. She wants to use the power of data science and pursue a career in field for the greater good.
Xiao, Data Science & Machine Learning
Who Let The Dogs Out? - Dog Breed Image Classifier
Cloud Computing (AWS/Google Cloud) / Classification / Neural Networks
With hundreds of dog breeds, it is often difficult to distinguish what kind of breed they are. By using convolutional neural networks and transfer learning it can accurately distinguish what breed they are based on a given image.

Louisa Reilly, Data Science & Machine Learning

Louisa is a chemist turned data scientist. She earned her Bachelor of Science in Biochemistry from Gonzaga University and her Master of Science in Chemistry from the University of Washington. Throughout her education, she participated in a multitude of research projects ranging from organic to computational chemistry. As a graduate student, she joined a material science and engineering lab studying the synthesis of semiconducting polymers for organic electronics. Due to her experience in computational chemistry, she joined an NLP project where she web-scraped chemistry textbooks and built a word2vec model for later use in named entity recognition of chemicals. This piqued her interest in data science and was the reason she joined Metis. She relishes the fast-paced environment, learning new topics, and applying her knowledge to solve business problems.
Reilly, Data Science & Machine Learning
Classy DnD: Detecting Multiclass Characters
Tableau/Dashboards / Classification
A character’s class in Dungeons and Dragons is akin to a job. Sometimes characters have more than one class. Using character sheet data, can a classification model predict whether a character is multiclassing?

Max Harrington, Data Science

As a former New York City math teacher, Max Harrington constantly leveraged the connection between data and student outcomes. It was this relationship that led him to pursue data science full time. A recent graduate of Metis, Max has applied his mathematical background to address regression, classification, and natural language processing problems. Previous to teaching, Max worked in the outdoor education industry, leading corporate and school groups on wilderness expeditions. Max earned his M. Ed. in Math Education from St. John’s University and B.A. in English from Boston University. Outside of work, he loves exploring NYC through bike lanes and restaurants.
Harrington, Data Science
Learn More, Spend Less: A Regression Analysis of US College Tuition Prices
Using web scraped data for four year colleges across the US, this project used a regression model and feature engineering to accurately predict the tuition price for a university relative to other institutions.

Melissa Cooper, Data Science & Machine Learning

After taking a hiatus from engineering to raise a family, Melissa’s data science path builds upon her hardware engineering background and incorporates machine learning through an intensive and immersive data science bootcamp. She is a curious, knowledge seeking machine learning engineer, dedicated to understanding data and applying flexible approaches to modeling and algorithms that empower solutions. Melissa earned her MS in Electrical and Computer Engineering from Carnegie Mellon University.
Cooper, Data Science & Machine Learning
Eco-Acoustic Monitoring of Endangered Species
Classification / Big Data/Data Engineering / Neural Networks
Rare species detection in dense ecosystems is central to climate change and conservation monitoring. CNNs enable real-time processing to predict bird and frog species by converting the audio to Mel spectrogram images within a deep learning pipeline.

Nick Pondok, Data Science & Engineering

Prior to Metis, Nick worked as a Team Lead in the Operations department for a FinTech start-up that focused on providing retirement plans to small to medium sized businesses. In this role Nick was able to work closely with software engineers and data analysts to help problem solve issues with the product. Through this collaboration Nick developed an interest in data and decided to take on the challenge of switching career paths. With a knack for data storytelling and a passion for helping others, Nick hopes to have a meaningful impact in his next role.
Pondok, Data Science & Engineering
Natural Language Processing with Movie Reviews
Natural Language Processing
Generated a topic model surrounding audience reviews for Marvel's movie Shang-Chi in order to see which topics were being discussed for both positive and negative reviews.

Sam Reiff, Data Science

Sam Reiff is a data scientist with prior work experience in business valuation, equity research, corporate strategy, and product conceptualization. Combining non-technical business experience and analytical programming/machine learning, Sam seeks to leverage his programming skill set to approach business problems as both a technical and financial stakeholder. Sam holds a BBA with a concentration in Finance from the University of Notre Dame, and is a Chartered Financial Analyst and member of the Chicago CFA society.
Reiff, Data Science
Finding Diamonds in the Rough
Regression / SQL / Tableau/Dashboards
Quantified and modeled the relationship between publicly-traded company fundamentals (eg revenue, profitability, market capitalization) and sell-side Wall Street analyst coverage, in order to identify 'undercovered' companies for investment opportunities.

Varsha Garla, Data Science

Varsha graduated from Vanderbilt University with a degree in Neuroscience. Prior to joining Metis, her research experience working in an Autism lab and her formal coursework in Statistics and Computer Science helped her discover a passion for using technical skills and tools to find insights that drive impact. As a creative person and fan of the arts, Varsha enjoys data visualization and storytelling. She is excited to leverage her data science toolkit to tackle challenging problems, work on a team, and continue to learn.
Garla, Data Science
Pricing Airbnb Stays in NYC for Hosts
Big Data/Data Engineering / Regression / Anomaly Detection
Built a regression model to predict the price of Airbnb listings in New York City based on features of the property and online listing to determine a reasonable price for host’s profitability and customer affordability.


Aiman Chughtai, Data Science

Aiman graduated with a B.S in Biology from Macaulay Honors College at CUNY. Prior to Metis, she worked as a researcher in a neuroscience lab, analyzing the results of multiple rehabilitative therapies on skilled motor performance in mice with bilateral and unilateral spinal cord injuries. With an initial interest in pursuing a career as a physician, many of Aiman’s prior roles were intimately involved in recommending workflow optimizations at hospitals, effectively tackling the problem of long patient wait times and overworked doctors at once. It was here that Aiman discovered a passion for using data analytics and machine learning in order to drive meaningful change in real world situations.
Chughtai, Data Science
Need Car Insurance? - Using Classification Algorithms to Predict Cross-Buying
Classification / Regression
In order to predict whether a health insurance policy-owner would be interested in cross-buying car insurance from their insurance company, multiple classification algorithms, including Sklearn’s Logistic Regression, Decision Tree, Random Forest, XGBoost models were leveraged.

Alexandra Garney, Data Science & Machine Learning

Prior to Metis, Alexandra was working as a Civic Engagement Program Manager in higher education, where her role focused on creating educational experiences for students to learn about voting, social justice, and community partnerships. Her love of assessment and data within education led her to pursue data science and machine learning. Alexandra is fascinated by how data can be leveraged to create a more equitable society.
Garney, Data Science & Machine Learning
Reducing Echo Chambers Through an Anti-Recommendation System
Big Data/Data Engineering / SQL / Recommender Systems
A web-based recommendation system app aimed at providing news article recommendations from a variety of different perspectives. The recommendation system leveraged a fully connected data pipeline, Word2Vec, and VADER sentiment analysis.

Andrew Wong, Data Science & Engineering

Born and raised in the San Francisco Bay Area, Andrew graduated from Santa Clara University with a BS in Psychology in 2016. Andrew has over 5 years of experience working in tech, most recently working in the FinTech industry. During his time at Metis, Andrew has developed a new interest in Data Science, Analytics, and programming and is looking forward to applying his skills into the tech industry.
Wong, Data Science & Engineering
Natural Language Processing for Amazon Video Game Reviews
Big Data/Data Engineering / Natural Language Processing / SQL
Built interactive web app for Amazon Video Game Reviews integrating Natural Language Processing and Topic Modeling. The app visualizes the positive/negative review topics to provide insight to game developers.

Bernard Opoku, Data Science & Engineering

Bernard started his career in Philadelphia, PA where he obtained his Bachelors degree in Computer and Information Science. While in college, he participated in various summer research ranging from computational Biology to pure machine learning. After graduation, Bernard worked in sales and hospitality; wanting to return to his roots and pursue a more technical role, Bernard thought back to his research experience in undergrad where he worked on a massive data set using machine learning. Reinspired, Bernard enrolled in Metis' Data Science & Engineering track where he enjoyed using data, computing and mathematics to positively impact everyday lives of people. In his free time, Bernard enjoys playing table tennis and soccer.
Opoku, Data Science & Engineering
Auto FAQ Answering Machine
Recommender Systems / Big Data/Data Engineering / Natural Language Processing
Have you scrolled through a company's site to find an answer to an FAQ? Well not anymore with HDFC Bank. In this project, I used natural language Processing techniques to answer customer queries based on our FAQ database which contains the sites Frequently asked Questions and answer pairs.

Crystal Huang, Data Science & Machine Learning

Crystal holds a BSc in Exercise Science from Rutgers University and is a registered sonographer in four specialties. With 3 years of working experience in the medical field as a sonographer, Crystal found her way to pursue data science full-time through the application of deep learning in medical images. She loves learning new skills and seeks to uncover hidden meanings and patterns. More recently, she sees the value of data and is keen to utilize it to capture elusive insights about the world.
Huang, Data Science & Machine Learning
If You Must Mask: Face Mask Detection
Neural Networks / Classification / Cloud Computing (AWS/Google Cloud)
An interactive web app for face mask detection leveraging pre-trained Haar cascade classifier with openCV and custom-trained mask detector with convolutional neural network using cloud computing on Google Colab.

Diego Duque Marquez, Data Science

Diego earned a Bachelor in Business Administration from Universidad Católica del Táchira (UCAT) and a Master in Finance from Instituto de Estudios Superiores de Administración (IESA). Prior to the Metis bootcamp, he served as an account executive at Yelp using SalesForce tools and data to target small businesses and provide different solutions to boost their sales. With an interest in statistics and analysis, some of Diego's prior roles were always focused on doing financial analysis and business development, using data to provide solutions based on models, and scientific evidence.
Duque Marquez, Data Science
Apple on Twitter
Natural Language Processing / Tableau/Dashboards
Sentiment analysis about Apple on Twitter.

Dong Zhen, Data Science

Dong transitioned from marketing to data science to work closer with data. He's curious about what insights data could reveal and stringent on the ethical use of data to ensure that a deployed model is adding value to the world. At Metis, he identified opportunities in a government agency and media, food, airline, and sports companies and presented scalable data solutions.
Zhen, Data Science
A Topic Model's Beginner Guide to Fishing in New York
Natural Language Processing
Answer commonly asked fishing questions with local insights found from topic models on posts from New York's two largest fishing forums.

Hernan Trujillo, Data Science

Hernan is a Business Administrator with extensive experience in Market Research. Charged with building industry trends and analytic reports for past clients, Hernan's interest was sparked and his desire to work with more advanced data analytic tools and machine learning served as the catalyst for him to pursue a full-time career in data science. His versatile background provides him with a familiarity of analyzing massive amounts of data while holding a keen business perspective.
Trujillo, Data Science
Bank Marketing Prediction
Tableau/Dashboards / Big Data/Data Engineering / Classification
Based on a Bank marketing phone calls campaign, built a classification model to predict which clients will subscribe a term deposit investments account

Jason Kim, Data Science & Engineering

While completing his BA in Economics at New York University, Jason became interested in the world of data analytics. He graduated in 2020, and began learning Python and Spark in his spare time but wanted to gain more tangible industry skills and knowledge. He decided to join Metis to increase his abilities in programming and develop new ones in professional communication. During his time here, he learned critical skills in data preprocessing, analysis, visualization, and machine learning augmenting his further increasing passion for data science.
Kim, Data Science & Engineering
Sepsis Classification
Classification / Regression / Time Series/Forecasting
Built a classification model to try to predict if a patient will develop sepsis.

Jesus (Aaron) Barela, Data Science & Engineering

Aaron is a Data Engineer with a lifelong passion for gaming and data. He holds a Bachelor's in Game Design and has worked as a Product Manager for the last 6 years. During his role, he interacted closely with developers, marketers, and clients in the Health & Wellness industry. Having to frequently use SQL in his role as a PM and formerly as a Data Specialist he decided to pursue data engineering. During his program, he discovered two new passions in the form of automation and the creation of data pipelines.
Jesus (Aaron)
Barela, Data Science & Engineering
Product Drops Tracker
SQL / Big Data/Data Engineering / Cloud Computing (AWS/Google Cloud)
Scraping multiple sites to generate push notifications via (Discord) webhooks, for products which are in demand and/or sold out nearly instantly.

Jonathan Yu, Data Science & Engineering

Jon holds a BS in Biomedical Engineering from University of Illinois at Urbana-Champaign. Following a brief stint in the manufacturing industry, he held a tenured career with a software driven medical device company in various roles to further his curiosity. His responsibilities soon expanded from mere product support & troubleshooting to trend analysis, quality auditing, training, regulatory research, PHI maintenance, and eventually machine learning / NLP of Salesforce data. With each endeavor, his yearning to transform objective data into impactful outcomes only grew until he decided to pursue a formal education in Data Science & Engineering through Metis. Jon is eager to leverage data to help make sense of an ever more complex world.
Yu, Data Science & Engineering
Deriving Hidden Airbnb Metrics through Natural Language Processing
Tableau/Dashboards / Clustering / Natural Language Processing
Discover unclassified metrics driving Airbnb guest satisfaction through topic modeling. Supplement with sentiment analysis and clustering model to derive topic weights for visualizing an adjusted and filtered rating system deployed via Tableau.

Juliana McCausland, Data Science & Machine Learning

Juliana is currently completing her Master of Science in Computational Linguistics at the University of Washington. For the last three years, she has worked alongside machine learning engineers to improve the linguistic capabilities of artificial agents, and most recently held a position as an Engineering Linguist at LinkedIn. Her professional experience has pushed her interests beyond the realm of language. She hopes to pursue a career that will allow her to explore the more creative facets of artificial intelligence.
McCausland, Data Science & Machine Learning
An Exploration of NFT Artworks
Recommender Systems / Big Data/Data Engineering / Neural Networks
A web application that explores non-fungible token artworks that were scraped from Twitter. The application contains an artwork recommendation engine and an NFT artwork generator.

Lin Mei, Data Science & Engineering

Lin Mei holds a MS in Electrical Engineering from Polytechnic University, Brooklyn NY. Prior to studying Machine Learning techniques at Metis, he spent two decades developing and trouble shooting software for telecom systems. He enjoys detective type work to gain better understanding of the inner working of many different things. The many powerful new tools provided by ML leads him to Data Science as a way to resolve important real life problems.
Mei, Data Science & Engineering
Review Filtering Using NLP
Clustering / Natural Language Processing / Big Data/Data Engineering
Using Nature Language Processing techniques, filter product reviews to a much smaller set of distinct reviews, to remove reviews too similar to each other.

Luong-Steven Truong, Data Science & Machine Learning

Luong-Steven Truong is a Data Scientist who is highly motivated to learn and grow within the advanced analytics field. Prior to joining Metis, Steve was a student at the University of Washington, Seattle and graduated with a Bachelor of Mathematics in 2020. While supporting himself through college, Steve worked in the hospitality and entertainment industry, honing customer service and client focused skills will easily translate into industry. Steve loves using his skills in analytics and programming to build web applications and deploy them into production. In his free time, Steve invents new cocktails and practices his DJ skills.
Truong, Data Science & Machine Learning
Drinks and Cocktails Recommendation System
Big Data/Data Engineering / Natural Language Processing / Recommender Systems
An end-to-end, interactive drinks and cocktails recommendation system web application using Natural Language Processing techniques. The application takes in users' input and returns the most similar products.

Michael Harnett, Data Science & Machine Learning

For the past eleven years, Michael has been working in the service industry. Holding a variety of positions from server to manager, working in hospitality instilled a strong sense of customer service, attention to detail, and communication. Deciding he wanted a greater challenge, Michael began teaching himself various programming languages, and fell in love with data science. Manipulating data and finding actionable insights was challenging, but rewarding. After learning a whole new set of skills with Metis, Michael is excited to turn this new passion into a career.
Harnett, Data Science & Machine Learning
I'll Have What She's Having - A Cocktail Classifying Application
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
An interactive application that classifies a user-submitted cocktail photo. Backed by a convolutional neural network built using Google Colab, the application returns the highest probable match, along with the cocktail's ingredients and recipe.

Prasuna Mannava, Data Science & Machine Learning

Prasuna Mannava holds a Bachelors in Biotechnology from India and Certificate in Health Informatics and Health Information Management from University of Washington, Seattle. Prior to Metis, she worked at UCSF Medical center for 4 years focusing on improving clinic performance metrics and patient satisfaction. During her time in Metis, Prasuna completed 7 projects which concentrated on concepts such as web scraping, business fundamentals, regression, classification, deep learning, data engineering and NLP. She believes in building technology with a purpose and was inspired by the endless possibilities of machine learning in improving the quality of lives across different domains. She enjoys working in challenging roles and fast paced environments.
Mannava, Data Science & Machine Learning
Predicting Rain in Australia
SQL / Classification / Tableau/Dashboards
Built a classification model which can predict if it’s going to rain the next day or not. Utilized feature importance to understand the key factors contributing towards the prediction.

Sabrina Yang, Data Science & Machine Learning

Sabrina Yang is a recent graduate of Metis’ Data Science Bootcamp. Originally from Taiwan, she started off her career working as a sales data analyst at Procter and Gamble and later as an account executive at Ogilvy and Mathers. Prior to leaving for New York, she also had the opportunity to work as an FX trader at a bank, gaining insight into the financial services industry. In New York, she completed a Masters in Data Science and Machine Learning and found herself working in multiple data analyst roles at retail and real estate firms in the city. Through these experiences, she recognized the importance of data science in all industries and hopes to align her personal passion for data science with a career in the field.
Yang, Data Science & Machine Learning
Bag Hunter - Luxury Bags Brands Image Classification App
Cloud Computing (AWS/Google Cloud) / Neural Networks / Classification
Bag Hunter is an image classification app that is designed to identify luxury bag brands using only their images. This app will help retailers identify luxury brands quickly without needing human verification.

Sheralee Lovejoy, Data Analytics

Sheralee Lovejoy earned her B.S. in Nutritional Science from Texas A&M University in 2016, who then went on to experience life unconventionally. It was through these hurdles and unexpected opportunities that she discovered her interest for data analytics. This began when she was put in charge of keeping track of a new product line. She used Excel analyze and identify shopping trends. With theses findings, she was able to tailor her surveys in order to collect and deliver better feedback to the corporate offices. Since her new discovery, she has taken on a fellowship with a venture capital firm to do market research. In addition, she has completed the data analytics curricula at Metis to acquire skills in data exploration, visualization, web scraping, regression modeling, and business analysis.
Lovejoy, Data Analytics
Predicting Opening Gross
This project developed a regression model to predict the opening gross income of a film, in order to help producers determine the success of their film, calculate a reasonable budget, and improve negotiation deals.

Valentina Rizzati, Data Science & Machine Learning

Valentina is a data scientist with in-depth experience in driving business decision-making through quantitative analysis, modeling, and data storytelling. She graduated from MIT with a Masters Degree in Economics. Prior to Metis, Valentina built her business acumen as a management consultant at Oliver Wyman and PwC. She also helped build some of the fastest growing tech companies including HelloFresh and Knotel; where she led performance marketing and marketing analytics teams. Valentina’s robust business background combined with her strengthened technical skills and passion for mathematics has helped propel her career in data science. In her free time, Valentina enjoys a good fencing match, perfecting her secret ramen recipe and time with her family.
Rizzati, Data Science & Machine Learning
Play Something by Spotify
Recommender Systems / Tableau/Dashboards / Big Data/Data Engineering
A mood-based recommender system that offers a personalized playlist for each user at the right time. Gathered data from the Spotify API, mapped mood to genre from a study by Adrian C. North, and deployed the app on Streamlit.

Yordanos Woldebirhan, Data Science

Yordanos has an MBA from Lincoln University, California. She believes working with data is a very powerful way of identifying and coming up with solutions for real world problems. Joining Metis was her important steps to develop the necessary skills as a data scientist and she will continue to learn more and more about the field each day.
Woldebirhan, Data Science
Depression Drug Reviews with Topic Modeling
Tableau/Dashboards / Natural Language Processing
This project analyzes the issues that concern patients on depression medications using the reviews they provide about the drugs they are taking.


Chuck Cao

Chuck has a B.A. in Economics from the University of Virginia and an MBA from the University of Maryland. Prior to Metis, he worked as a project manager on a government contract with the FDA’s Center for Drug Evaluation and Research’s Data Standards team. During his support of FDA’s activities to create standards for study data and electronic submissions, he would be exposed to the terms and techniques of data science. Having a growing interest and wanting to be more hands-on, Chuck has continued to pursue and develop his passion for data science. Wanting to explore and answer unique questions and challenges and be able to communicate his discoveries to others. Chuck is a lifelong learner and a believer in continuous self-improvement.
Do You Want to Play Something Else?
Natural Language Processing / Recommender Systems
A content-based board game recommender based on game features (ex. type, mechanics, players) and user comment sentiment and types.

Gabriel Equitz

Born, raised, and based in the San Francisco Bay Area, Gabriel Vieira Equitz earned a BSc in Computer Science from San Francisco State University in 2019. Afterwards, Gabe chose to follow his ardor in machine learning and joined Metis, desiring the up-to-date instruction and tools they offer. During this time, he learned technical and communication skills critical for data scientists. Gabriel appreciates the empirical insights data science brings to the world and wants to make a positive difference with his skills. In his personal life, Gabriel likes studying history is an animal lover.
Sovereign Risk Model
Classification / Regression / Time Series/Forecasting
This model calculates the probability of future sovereign default for more than 200 countries using macroeconomic data. Performance is evaluated using different machine learning techniques. Web app functionality is built for the model.

Garreth Cline

Before Metis, Garreth spent his time obtaining his bachelors in biology and working as a pharmacy technician at Kroger Pharmacy. From Toledo, Ohio - Garreth started his path in bioengineering, then later changed his passion to biology/bioinformatics. It was in bioinformatics where he discovered the growing field of data science and quickly fell in love. During his time at Metis he worked on projects mainly focusing on natural language processing to provide insights on a large amount of data, learning new technologies and methods along the way. Another interest of his is public speaking and the presentation of data. The world is absolutely packed with information; Garreth’s goal is to leverage communication and data science to tell the story the data holds.
Hacking the Data Science Job Description
Classification / Clustering / Natural Language Processing
A joint supervised and unsupervised learning approach to analyzing data science job descriptions using natural language processing. Based on 40,000 data science jobs posted on Indeed.

J. Garrecht Metzger

Built a customizable, content-based Spotify song recommender to direct song recommendations based on what song you're currently hearing. Using data from the official API, he generated song recommendations based on the audio content of each song as described by Spotify's proprietary 'audio features' (e.g., energy, acoustics, instrumentals, danceability).
J. Garrecht
Content-based Spotify Recommender
Big Data/Data Engineering / Clustering / Recommender Systems
A content-based recommender for Spotify that utilizes song audio characteristics (e.g, key, tempo, loudness) to recommend similar tracks.

Jason Zhang

Data scientist with advanced degree in Mathematics and Statistics, and strong experience in analyzing and interpreting data for driving business solutions. Advanced proficiency in multiple programming languages and deep understanding of applied analytic.
AirBnb Recommendation System
Natural Language Processing / Recommender Systems / SQL
Deployed a machine learning algorithm to create a recommendation system on airbnb listings based on user's previous airbnb.

Josef Seemayer

Josef Seemayer is a business-oriented problem solver. Studying chemical engineering, he was able to learn effective communication and problem-solving skills while completing a rigorous curriculum. Working previously in biotechnology, IoT product development, and marketing, Josef learned many new things, quickly, in order to keep the business moving. From automation to dashboard maintenance to direct client relationships, Josef jumps into any task handed to him with fervor and an eye on the target. Always strong with quantitative analysis, data science was a perfect career field for him to transition to. A naturally curious person who loves to listen and share stories with others, Josef finds his home in a team environment that pushes each other to excel.
Predicting NFL Games
Regression / SQL / Tableau/Dashboards
Predict the winner of an NFL game using adjusted Elo rating system. Explore individual, team, and situational insights using an interactive Tableau dashboard.

Patrick Norman

Patrick is a recent graduate from Western Washington University's distinguished Environmental Science program, giving him a solid foundation in experiment design and the scientific method. During his program, he realized that understanding and modeling the world was his real passion, and turned to data science to expand on his skills. He has a passion for modeling complex systems using diverse data sources and creative feature engineering.
Modeling Forest Fire Risk in the Western US
Classification / Regression / SQL
Created a tool that modeled forest fire location and intensity in the western United States. Includes multiple data sources, regression to predict fire size, and classification of small vs large fire risk per county.

Satenik Safaryan

Satenik holds bachelor’s and master’s degrees in Business Management. She has more than six years of experience working in operations management, data analytics, finance and marketing. Prior to the bootcamp in her most recent role as an Operations Manager she led a team of up to 30 people and was tasked with conducting analytical experiments to help solve various business problems. As part of her transition into data science Satenik completed multiple online courses on Statistics, Data Analytics and Machine Learning, and enrolled into the Metis. She thrives in fast-paced environments, loves working with people and is most passionate about creating business value using data.
Self-Supervised Voice Emotion Recognition using Transfer Learning
Classification / Natural Language Processing / Neural Networks
Built a self-supervised voice emotion classifier using transfer learning. The model classifies audio clips of human voice into positive or negative emotion classes.

Shannon McDonnell

Shannon received her undergraduate degree from the University of Michigan in Ann Arbor, and has since gathered over 6 years experience in the startup industry across various departments. She is Co-Founder and COO of a ‘500 Startups’ SaaS company which sparked her interest in big data and data science. She’s an accomplished leader and self-driven individual with an entrepreneurial mindset and strong business acumen. Her passion for data science is fueled by her love of strategic problem solving and growing small businesses through the power of machine learning technologies. She is especially interested in natural language processing and machine learning architectures, and is excited to continue learning and take these new skills and apply them to the data scientist field––eager to help shape the future.
Scotch Whisky Recommender
Natural Language Processing / Neural Networks / Recommender Systems
Created a Scotch Whisky recommendation system and interactive web app that uses NLP and neural networks from online review sources to recommend and predict similar Scotch preferences based on user input.

Young Suh

Young Suh graduated from UCSD with B.S in Cognitive science with specialization in machine learning and neural computation and a minor in mathematics. Prior to Metis, he worked at a bio-tech startup as a computer engineer, analyzing genotypes and phenotypes of DNA sequences, along with developing covid-19 detection kit. Young is interested in applying different types of machine learning and deep learning models to solve real life problems and exploring. In his free time, he enjoys dancing, brewing coffee and experimenting using raspberry pi.
Reci-py Recommender: Minimizing Food Waste
Recommender Systems / Natural Language Processing
Recommending recipes to users using ingredients provided along with user preferences to minimize food waste.

FALL 2020

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.

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.

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.

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.

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.

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.

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.

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.

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.


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

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.

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.

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.

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.

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

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.

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?

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.

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.

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.

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.