Develop Your Data Science Fundamentals With Metis Admissions Prep

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.

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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.

Brandon McNeil, Data Science & Engineering

Brandon graduated from SUNY New Paltz with a B.A. in History. Before starting Metis, he worked in the data analytics field in both the public and private sector designing innovative data solutions and proactive SQL reports. He is based in New York City and is excited to bring his new machine learning skills into real world application!
McNeil, Data Science & Engineering
What is Your Routine?: Crowdsourcing User Reviews for a Skincare Recommendation System
Big Data/Data Engineering / Natural Language Processing / Recommender Systems
Performed NLP analysis and NMF/SVD Topic Modeling on web-scraped product user reviews to create an interactive web-app that takes in a user's comments and recommends a product based on comment similarity.

Brian Nam, Data Science & Engineering

Brian Nam is a recent graduate from University of California, San Diego and holds a B.A in Economics. During his time at UCSD, he was able to work with econometric models, which led to a passion for modeling the world with data. To expand his knowledge and skill set in data science he enrolled at Metis. In his free time, he likes to work on his car or go for a drive.
Nam, Data Science & Engineering
Predicting Housing Prices in San Diego
Price prediction model for houses in San Diego using linear regression

Chris Byrnes, Data Science

Chris Byrnes has over 20 years consulting experience advising clients of all sizes and types on complex pension actuarial topics. Working with data has always been a highly enjoyable aspect of his work, particularly mining and analyzing to understand the stories it tells. Though Chris’s career to this point has seen him taking on more management responsibilities, he's made it a point to never stray too far from the technical work which is where his true passion lies. After recently completing Metis’s data science boot camp, he is excited to dig in and focus even more on data using both old and new skill sets. Chris has a BA in Mathematics from Spring Arbor University.
Byrnes, Data Science
Lululemon NLP Analysis
Natural Language Processing
Apparel retailer Lululemon is entering the shoe market in 2022. The project uses natural language processing to analyze competitor Amazon reviews and identify primary topics discussed to guide a potential Lululemon marketing campaign.

Jonathan Wyatt, Data Science

Jonathan is a data scientist who believes in the power of data and storytelling to solve challenging problems. Prior to joining Metis, Jonathan worked for over a decade in the fast paced commodity trading space with roles in analytics, risk management, supply operations, linear optimization modeling and software project management. He prides himself on the ability to adapt to new situations, be a team player and his ability to learn on the fly. Jonathan is excited about deep learning and its potential to help businesses big and small. He earned his Bachelors from the University of Michigan with an emphasis in Finance and Economics.
Wyatt, Data Science
Predicting Hit Songs with Spotify
What to find this Summer's anthem? We've created a supervised classification model to utilize Spotify API data to help record labels identify the next big hit song

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.


Alex Katz

Alex has always loved solving puzzles and finding new ways to interact with problems. After studying mechanical engineering at UC Davis he worked briefly in the energy sector designing hydrotests for gas pipelines. Now he has found his true passion, data science. At Metis, led by a brilliant set of instructors he explored the limitless possibilities data has to offer and is eager to find new challenges to broaden his expertise.
Fifa Match Predictions
Classification / Clustering / Time Series/Forecasting
A dive into soccer match prediction utilizing solely player statistics from EA Sports Fifa video games.

Alexander Sigrist

Prior to joining Metis, Alexander was a researcher and analyst at NYC’s Taxi & Limousine Commission developing new education metrics for the licensing exam and new course content. He holds a MS in Urban Planning from New York University where his work with NYC’s Department of Transportation led him to discover his love of data. His work in data analysis, problem-solving, and project-scoping led him to pursue a full-time career in data science.
Metastatic Tumor Detection
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
To assist physician diagnosis of advancing cancer this project developed a classification model built on convoluted neural networks to detect tumors in microscopic images of lymph node tissue.

Anubhav Pareek

Anubhav is a technology consultant specializing in software application development, product and project management. After graduating as an undergraduate in Computer Science, he started as a SAP consultant with Deloitte and went on to earn an MBA(Finance) and MS in Financial Mathematics. He has worked on cross-functional technology engagements in geographies across Middle East and Asia. His analytical flair and technical background led him to pursue a full-time career in data science.
Interactive Apparel Recommendation Engine
Natural Language Processing / Neural Networks / Recommender Systems
An interactive recommendation engine which leverages the power of NLP processing and deep learning to recommend similar products based on products selected by the user.

Carla Moestafa

Carla Moestafa has over twenty years of experience in the oil and gas, construction and manufacturing sectors. She has worked with Total E&P in France, PTTEP (a national petroleum EP company based in Thailand), and INPEX in Perth Western Australia. She has gained strong experience in field development, cost estimating greenfield, brownfield CAPEX and M&A due diligence, procurement and project control & support. She began teaching herself Python and then after completing a Metis Beginner Python and Math for Data Science course she became more interested and enrolled in the data science bootcamp. She is now ready to pursue a career in data science. Carla is an Indonesian national fluent in Bahasa Indonesia, French and English and holds master degrees in Chemical Engineering and Industrial Project Management from Ecole Centrale de Marseille in France.
Running Course Classification
Classification / Neural Networks
Used a convolutional neural network model to classify types of running courses. The dataset was prepared from running course screenshots from websites such as Strava, MapMyRun, Great Runs, and Park Run.

Chris Chan

Chris Chan has over 15+ years of experience in various roles involving data and analytics. He possesses a hybrid of research and consulting expertise along with technical skills necessary for a spectrum of data analyses. Examples of technical skills include Python, SAS and SQL. His recent work experience was as a Senior Data Analyst with Boston Scientific where he helped build a Tableau dashboard to analyze market penetration of medical devices within hospitals. He has also co-authored several studies while with the RAND Corporation and IBM Watson Health. Chris holds a Master’s of Public Health degree with an emphasis in International Health and a Bachelor’s degree in Public Policy. He also just completed an immersive 12-week data science bootcamp at Metis. In his free time he enjoys music, sports and spending time with his family.
A Modern Jazz Discovery Tool
Natural Language Processing / Recommender Systems / Tableau/Dashboards
To create a Modern Jazz Discovery Tool using various NLP techniques on Pitchfork jazz review data. To give music-lovers a tool to explore the connections between modern jazz and other sub-genres they already enjoy.

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.

Colin Salama

Colin graduated from Swarthmore College with a B.A. in Mathematics and Economics with a focus on using Statistics to solve real-world problems. Prior to Metis, he worked in Ad Tech, consulting with Fortune 500 companies on optimizing their advertising budgets using big data. His passion for statistics and problem solving then led him into data science. Colin is a lifelong learner and uses his experience in a wide variety of subjects to find innovative solutions for problems.
Automated Technical Trading using Reinforcement Learning
Neural Networks / Recommender Systems / Time Series/Forecasting
Reinforcement learning algorithm trained to make the optimal series of buy, sell, and hold decisions from the same technical indicators a day trader may use.

Ethan Feldman

After earning his Bachelors in Mathematics from the University of Illinois, Ethan taught high school math for six years at Evanston Township High School while simultaneously earning his Masters in Math Education from DePaul University. He subsequently worked as a financial analyst and brewer for Illuminated Brew Works for three years before returning to mathematics. His love for problem solving, helping people break down big problems into smaller pieces, and belief that numerical structures underlie our world fell naturally into pursuing a career in data science.
Natural Language Processing / Neural Networks / Recommender Systems
To find a replacement for his wife's now closed favorite pizzeria, Ethan scraped Chicagoland Yelp to filter restaurants based on pizza images using Resnet weights and finalize recommendations using similarity of NMF vectors of reviews.

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.

Grettel Juarez

Grettel Juárez holds a B.S. in Computer Engineering. With over 12 years of experience in consulting, she has served a wide range of clients in the performance test and diagnostics space. Through this lens, she has experience with large volume data preparation, quantitative analysis of load test results, and performing root cause analysis on complex systems. Grettel has experience leading teams, communicating to stakeholders, and serving as a thought leader in high profile fast paced projects with compact delivery schedules. She is skilled at quickly understanding the wholistic view of a problem, thinking analytically with attention to detail, and collaborating with teams to drive to the best solution. Grettel looks forward to expanding her analytical capabilities and driving further insights in the data science space.
NLP with Schitt's Creek
Natural Language Processing
Leveraged natural language processing, topic modeling, and sentiment analysis techniques to identify topic changes over time and the impact on character growth and relationships.

Humza Khan

Humza has a bachelor's in business administration with a concentration in management. After college, Humza began his career as an analyst working in various departments at a health system. Here, he used tools such as Excel and PowerPoint to effectively provide and present data insights. After sometime with productivity analytics, Humza moved to Chicago to begin a role in healthcare consulting. Although he enjoyed this, Humza wanted to do more in healthcare like predictive analytics such as predicting patient outcomes. He found Metis to gain the necessary skills for his new goal.
Car Sales Analysis
Classification / Clustering / Tableau/Dashboards
Multi-class classification on what car make will be sold. Cluster analysis to understand the different types of customers. Tableau dashboard for data analysis and insights.

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.

Jillian Etheredge

Jillian is a data scientist excited about the applications of data to further environmental conservation efforts. Prior to joining Metis, Jillian graduated from Clemson University in 2019 with a B.S. in Animal Science, much of the course work she most enjoyed involved the applications of probability and statistics. During the final semester of her undergraduate degree she developed a growing interest in data science and its potential applications in healthcare and ecology. At Metis her desire to learn about and apply machine learning algorithms has grown with each new project. Leaving Metis she is excited for the chance to apply what she has learned to real-world problems. In her free time she enjoys she enjoys listening to mystery podcasts and running or playing tabletop role-playing games.
Identifying Wildlife in Camera Trap Images
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Snapshot Serengeti's camera trap images are currently labelled by volunteers. The goal of this project was using CNNs to automate this labeling process to make these labelled images more readily available for further research endeavors.

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.

Joshua Mailman

Joshua graduated from University of Chicago with a B.A. in Philosophy and then went on to earn a Ph.D. in Music Theory from the Eastman School of Music (University of Rochester) after which he taught at Columbia University. He’s written numerous blind-peer-reviewed articles and book chapters which present the music-analysis equivalent of feature engineering. He’s appeared on national television (ABC News Nightline in 2014), presented research at IRCAM in Paris in 2019. He has also engineered interactive audio-visual algorithmic music technology which he demonstrated in his 2016 performance at the New York Philharmonic Biennial / NYC Electroacoustic Music Festival. He’s energized about now applying his problem solving skills and analytical approach to evolving technologies and trends in industry.
Experiments in Image Captioning, Wit a twist: The Amusemater Captioner
Classification / Natural Language Processing / Neural Networks
The Amusemeter Captioner is a web app that lets machine learning dip its toes into the waters of witty wordplay by inventing image captions that make you smile. To do this, a Neural Nets image classifier joins hands with NLP to algorithmically dance right to the verbographic punchline.

Katie Huang

Katie holds a PhD in physics from Harvard University where she studied how superconductivity gets even stranger in lower dimensions. Skilled in project design and capable of diving deep into the root of problems, she is a passionate researcher fascinated by the complexity of abstract phenomena, e.g. things that can’t be explained by physics. She loves learning new tools and seeks to uncover hidden messages and patterns. More recently, she sees the power of data and is eagerly looking forward to harnessing this power to capture elusive insights about the world.
Picture Purrfect: Capturing the Best Moment with Computer Vision
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
Having trouble taking a good picture for your furry friend while its cutely frolicking around? Upload a video and this computer vision algorithm will select your next album-worthy picture.

Mariam Yousuf

Mariam Yousuf is an Economics Ph.D. candidate at the University of California-Davis. After completing her B.A. in Applied Math and Economics from University of California-Berkeley, she worked in education for two years before beginning her Ph.D. During her time at Davis, Mariam continued her role as an educator and worked as a teaching assistant for many different economics classes. Her specialties in economics include macroeconomics, economic history, and international economics. Mariam is looking to transition from academia to the private sector. In addition to her interest in economics, she is also interested in education, media, advertising, and entertainment.
Analyzing the narrative of George W. Bush and Hillary Clinton
Clustering / Natural Language Processing / Neural Networks
Using article abstracts from the NYTimes, tracked the narrative changes of George W. Bush and Hillary Clinton over the last 20 years. Topics generated using non-negative matrix factorization track closely with their respective career trajectories.

Matt Segall

Matt Segall grew up in Palo Alto, CA and currently resides across the Bay in Oakland. He studied jazz saxophone and economics at Oberlin College & Conservatory of Music, and worked in nonprofit finance for five years before transitioning into data science. He has been passionate about statistics and data since his AP Statistics course in high school, and was inspired to transition into data science after realizing how closely it was linked to his intellectual interests. Outside of his professional life, Segall is an avid cyclist, runner, jazz saxophonist, and cook - particularly of Southwestern Chinese cuisine.
Personalizing Reading Practice to Enhance Student Learning
Clustering / Natural Language Processing / Recommender Systems
Designed a tool to promote childhood literacy by allowing a teacher to assign practice reading passages based on specific prioritized factors.

Michael Jehl

Mike studied marketing, psychology, and philosophy at Washington University in St. Louis, where his education in business fundamentals inspired him to pursue a career using data to tell stories, solve interesting problems, and help teams collaborate more effectively. He began his career as an operations analyst where he got exposure to applied machine learning at the forefront of sports analytics. He later joined bswift, an HR and benefits administration software company, as a key member of the client services team before transitioning into product management. Mike excels at communicating data-driven insights in an accessible way and loves to discuss how the ever-growing suite of data science tools can be used to improve organizations and better society.
Forecasting Electricity Demand in Seattle
Regression / Tableau/Dashboards / Time Series/Forecasting
Time series analysis and advanced statistical modeling to predict electricity demand in Seattle using historical hourly demand data from July 2015 through early March 2021.

Nathaniel Speiser

Prior to transitioning into data science, Nathaniel received his MS in physics from the University of Colorado Boulder, where he also conducted experimental condensed matter physics research, fabricating and analyzing mesoscale superconductor devices. His experimental physics background has given him a strong mathematical foundation, technical problem solving skills, and the flexibility to pick up new tools quickly. Nathaniel is passionate about leveraging data science and machine learning techniques to solve problems in science, education, technology, and media.
Predicting In Game Win Probabilities in Super Smash Bros. Melee Matches
Classification / Neural Networks / Tableau/Dashboards
Developed several classification models to predict in game win probabilities in SSB Melee matches using frame by frame game data, as well as a web app to display statistics for any uploaded game.

Nicholas Kinnaird

Nicholas Kinnaird is a data scientist with a background in experimental particle physics. Prior to Metis, he earned his doctorate at Boston University working on a large-scale physics experiment located at Fermilab outside Chicago. After earning his doctorate he spent a year as a postdoctoral research associate on the same experiment, wrapping up his research work for the publication of the experiment’s first results. Now he has transitioned into data science where he is looking forward to applying his problem-solving, programming, and mathematical skills to real-world problems.
Mapping Mangrove Deforestation and Growth
Classification / Cloud Computing (AWS/Google Cloud) / Neural Networks
A convolutional neural network was trained on mangrove forests from year 2000 Landsat 7 satellite imagery. By applying the trained model to later years, deforestation and growth can be mapped.

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.

Patrick Bovard

Patrick has a working background in the medical device industry as a quality assurance engineer, after graduating from Washington University in St. Louis with a BS degree in biomedical engineering. During this time, he was able to begin working with data, which led to a passion for utilizing data as a way to understand and tell the story of what is happening in the world around us and find actionable solutions. Transitioning to a career focused on data science, Patrick enrolled at Metis to strengthen the programming skillset and gain formal data science training. Upon leaving Metis, he is hoping to use these skills to continue to find creative solutions to real-world problems, using data science as a backbone.
Predicting MLB Pitches
Classification / Regression / SQL
Knowing what pitches a batter will face is a powerful tool. This project aims to utilize machine learning to predict what type of pitch a batter will see, and where it will be thrown.

Rachel Dilley

An ISE graduate with 2+ years of industry experience in data analysis and system optimization. Skilled in coding (Python) and query-based languages (SQL) with a love for data mining, problem solving, and programming that has led to a pursuit of a full-time career in data science/analytics.
Lets Take a Trip - A US Attraction Recommendation System
Natural Language Processing / Neural Networks / Recommender Systems
Attraction recommender using an input image. Attractions clustered using NLP. Input images classified using a CNN with transfer learning. Closest images within classes are found by comparing color distributions with the respective attraction recommended to the user.

Rhys Carter

Rhys Carter is an accomplished analytics manager with 12 years of data analytics, management consulting, IT support, project management, and analytics-operations expertise. Leveraging a deep understanding of the intersection between technology and mission support, his experience includes delivering machine learning (ML) anti-fraud capabilities in Federal Agencies; designing and deploying accompanying operations and performance monitoring systems; and leading large cross-functional (e.g. operations, process engineering, and data science) teams to deliver new operating capabilities for clients.
Veteran Forum Insights Dashboard
Clustering / Natural Language Processing / Tableau/Dashboards
Create a rapidly updatable dashboard for ongoing Veteran needs and concerns from social media and forum posts.

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.

Warren Lee

An excitable nerd with a passion for all things economics, data, and interactive storytelling, Warren worked as an economic consultant, preparing statistical models for expert reports and trial testimony and communicating with multiple stakeholders across matters in antitrust, intellectual property, and class action litigation. Warren studied at Pomona College in Claremont, CA, where he graduated Cum Laude in economics. Warren's schoolwork was concentrated in econometrics, receiving a minor in mathematics and the Brystine prize for finance. He also played in multiple musical ensembles, performing on the clarinet with the Pomona College Orchestra for four years. In his free time, Warren continues to keep up his chops on the piano and clarinet, works on his single axel on-ice, and streams video games with his community on
Modeling Neighborhoods in Vancouver, BC by Cuisine
Anomaly Detection / Clustering / Natural Language Processing
Using clustering algorithms to identify areas concentrated by restaurants, cuisines found in Vancouver are modeled into groups using Scikit-learn's NMF algorithm. Areas are then clustered again by this grouping to identify areas concentrated by cuisine.

Wei Zhao

Prior to Metis, Dr. Wei Zhao conducted research in computational biomechanics as an assistant research professor at Worcester Polytechnic Institute. His projects were focused on computational modeling of traumatic brain injury and neuroimaging. With 14 years of research experience, Dr. Zhao has developed a strong skill set in project development, independent research, scientific writing, and teamwork, resulting in a strong publication record. His particular interest in data science led him to enhance the knowledge at Metis. Being a quick learner, he is now equipped with data science techniques including machine learning, natural language processing, and neural network. Dr. Zhao is interested in applying data science to his research field, and he is also passionate about bringing his expertise to a business setting.
Investigation of US Traffic Accidents and Instantaneous Prediction of Accident Severity
Big Data/Data Engineering / Classification / Neural Networks
This project investigated the relevance of US traffic accidents to time and weather conditions and built a deep neural network, in order to assist police department and companies in instantaneous prediction of traffic accident severity.

Will Nobles

Will Nobles is a data scientist who believes the years of diverse professional experiences have all lead to this goal of being able to use the tools gained at Metis, along with those gathered previously, to tell stories with data that positively impact the lives of others.
Batman Themes: Text Mining Screenplays from Burton and Nolan
Natural Language Processing
This project explores themes and topics in the Batman franchises directed by Tim Burton and Christopher Nolan.

Will Moore

After working in and around craft beer for over 10 years, Will came to Data Science through his interest and experience in the beer business, markets, and darts.
Unlocking The Flavor Network
Clustering / Natural Language Processing
Using shared flavor compounds in different foods to add novel ingredient pairings to common food items.

Wonjae Lee

Wonjae Lee is a data scientist with 7 years of experience in the financial services industry where his major accomplishment was to build a web portal managing millions of rows of data and to reduce customer churn by identifying KPIs. He completed the intensive data science boot camp at Metis to further strengthen his analytical capabilities and to look for new interesting problems to solve with data.
Korean Movie Recommender
Natural Language Processing / Recommender Systems / Tableau/Dashboards
This project recommends Korean movies based on another movie that user likes. The recommendations are based on similarities between the synopses and these recommendations are further improved with feature engineering.

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.

Yuwen Huang

Yuwen is a data scientist with a passion for learning new and using technology to achieve business goal. She graduated from the University of California Los Angeles in 2017 with a B.S. in Statistic. Prior to Metis, she had 3 years working experience for purchasing strategy analysis in trading firm. Returning to her undergrad roots of statistic, she began coding again to pursue more technically focused roles in data science. To close the gap, she applied Metis to build solid technical skill. Now, Yuwen is excited to take her skills in machine learning, deep learning and domain knowledge to her next data analysis journey. Outside of data science, Yuwen enjoys hiking, camping, play basketball, and swimming.
Animal Adoption- Find a New Home
Classification / Neural Networks / Recommender Systems
Classification model to predict if animal in shelter will be adopted or euthanized. And build an adoptable pet recommender system based on image.

FALL 2020

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 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.

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.

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.

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.

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.


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 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.

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.

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.

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.

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.

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.

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.

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.

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.


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.

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.

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.

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.