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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.
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Abdullah Hanif

Abdullah Hanif graduated from Yale University with a BA in Economics and Global Affairs. Prior to joining Metis, he was an analyst in the Market Risk group at Goldman Sachs where he managed regulatory interactions on behalf of the Risk division and developed a risk limit-setting process that aligned with Goldman’s strategic direction. He also spent a summer interning in the corporate strategy group at Lazard. He is passionate about using data science to analyze financial markets and consumer behavior. In his spare time, he is an avid tennis player and cyclist. For his final project, Abdullah predicted the outcome of professional tennis matches and created a recommendation system that tennis coaches and enthusiasts can use to find matches of interest.
Game, Set, Match
Regression / Classification / Recommender Systems
Predicting the Outcome of Professional Tennis Matches.

Andy Tan

Andy previously worked as a physician in clinical practice. It was there he became interested in big data and its underutilized potential not just in medicine but across all industries. After taking computer programming classes while still working, he decided to follow this passion for problem solving by pursuing a career in data science.
Track My Diet
Regression / Classification / Big Data
In this project, I explore building a food image classifier using neural networks for diet tracking.

Angeline Protacio

Angeline Protacio is a Data Scientist with a background in epidemiology and genetics, with projects spanning disciplines, including health, finance, sports, music, politics, and travel. She has over seven years of experience working with the data science pipeline, from data extraction, cleaning, and exploratory analyses to data visualization and model building. She frequently shares her work with stakeholders and members of the community through conference presentations and meetups. She is passionate about mentorship and teaching, with experience supervising and guiding junior analysts, and demonstrated skill in instructing analysts and graduate students on principles of statistical programming, data visualization, and data science in R and SAS. Angeline is seeking to contribute her experience and expertise in a role where she can continue to learn and teach in a collaborative team environment.
The Album Discoverer
Classification / Recommender Systems / SQL
Recommendation system that uses principal components analysis and cosine similarity to recommend albums to users through an interactive Flask app.

Anna Bradley-Webb

Data scientist with a background in marketing and communications. Anna graduated from Bowdoin College and previously worked at a public affairs agency and an education tech startup, where she was first exposed to data analytics. Anna has several years of professional experience using SQL to build internal and client-facing dashboards and visualizations. Her projects at Metis include building a classification model to predict whether a dog on would be adopted within 30 days, using linear regression to predict college acceptance rates, and using Natural Language Processing to recommend fiction books a user might like. Seeking opportunities in Chicago or New York.
Predicting Your Next Favorite Book
Recommender Systems / Natural Language Processing / SQL
Given a bestseller fiction book that a user liked, the model recommends a lesser-known book that the user may also like. The model uses Natural Language Processing as well as meta-data.

Da Guo

Da previously worked as a professional architect specializing in retail tenant improvement, designing, drafting, modeling and selling ideas. Later he went to a coding bootcamp to learn web/software development. And now after equipping himself with data science knowledge at Metis, he's looking to start a new career as a data scientist.
Detect Anomalies in Traffic Congestions
Anomaly Detection / SQL / Big Data
Abnormal traffic congestion often disrupts our plans. This project seeks to predict congestion duration and detect if it's an anomaly by using hybrid modeling methods.

Daniel Lin

Daniel graduated from Juniata College with a BS in Biology and finished a Post-Baccalaureate in cell biology at the University of Pennsylvania. His drive for data science stems from performing data analysis while working as a research associate at Unity Biotechnology. As a researcher, Daniel was tasked to handle data derived from experiments, draw conclusions and present them to the team. Daniel enjoys solving problems, troubleshooting issues, and coming up with solutions in a timely manner. He thrives in team settings, and his skill in effectively communicating with others is what drives his ability to solve a variety of problems. For his final project, Daniel built a neural network capable of identifying the breed of dog from images of dogs.
Dog Breed Recognition System
Using neural networks to build a model that is capable of identifying the dog breed from an image of a dog.

Daria Morgan

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

Darien Mitchell-Tontar

Before Metis Darien was a high school math instructor for eight years, working at schools in Jordan, Chile and New York City. Teaching higher level statistics and probability, as well as basic programming to students, made him believe that a career change was possible; this is what led him to Metis. Darien completed five projects at the bootcamp, each with varying topics and requiring him to use different tools. The topics of his projects included, sports, epidemiology, history and social media. He used basic linear regression, classification models, NLP and neural nets for each of his projects. Darien looks forward to entering the new stage of his life as a data scientist!
Selfie Popularity Predictor
Classification / Natural Language Processing / Tableau/Dashboards
Used a Convolutional Neural Network to a build an application that takes in a selfie, and returns the probability of it being popular on Instagram, as well as the emotions shown in the photo.

Dayanand Shanbhag

Daya holds a masters degree in computer applications from SBMP, India. He is a hands on full stack Technical Architect with over 10 years of experience working at various banks including Barclays, Federal Reserve and UBS. He has extensive experience implementing large scale distributed systems in Reference Data, Market Risk and other financial domains. He has fine tuned performance of various applications and worked on making the applications secure from external attacks. Wanting to pursue a career in Machine Learning, he opted to enroll in Metis. Daya built his Data Science skill set by working on multiple projects at Metis including a collaborative and content based Recommender system. Daya believes that Machine learning has ushered the fourth industrial revolution and is very excited to be a part of it.
Collaborative Movie Recommender System
Regression / Recommender Systems / Natural Language Processing
A collaborative and content based recommender system capable of recommending movies or products capable of handling large scale data and use multiple ML models including NLP and Matrix Factorization models.

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.

Ed Staples

Ed is a seasoned customer-centric growth marketer that specializes in customer acquisition/retention, conversion rate optimization, data analytics and storytelling. As growth lead at Elysium Health, Ed was responsible for managing a $8m yearly budget across a dozen channels and developed data-driven strategies to reduce customer acquisition costs by half in his first year. Ed’s passion for data is rooted in looking closely enough at the numbers to reveal purpose, belonging and want. He uses data to understand customer interactions, build relationships between consumers and brands, and tell stories that motivate thought and action.
Identifying Film Stocks in Analog Photography with Neural Networks
Classification / Clustering / Regression
Trained convolutional neural networks (VGG16 and AlexNet) from scratch to predict the specific color film stock an analog photograph was taken with.

Elena Dubova

Elena holds three Master degrees in International Relations, Economics, and most recently completed an Applied Analytics masters program from Columbia University. Before making a full pivot towards data science, Elena worked at Microsoft for 10 years and was tasked with developing cloud business and transforming partner ecosystem across 33 countries in Central and Eastern Europe. In addition, she worked with key enterprise clients and leading sales teams. Elena is involved in educational programs at Columbia University and serves as an associate and an instructor. Apart from data science, she is interested in studying human psychology, languages, books, and visual arts. Elena Dubova resides in New York.
Blogger Boost: Know Yourself and Your Community
Classification / Clustering / Natural Language Processing
A web application for bloggers with three functional blocks: topic-based community visualizer, smart dictionary based on word embeddings specific to the community, and a set of dashboards that analyze emotional profile of one's writing.

Eric Larson

Eric excels at working with complexity, communicating complicated, technical concepts to a wide audience, and finding creative solutions to problems. He graduated from Northwestern, and previously worked in healthcare IT for 5 years as an engineer and a consultant. In his last position, Eric worked closely with executives to create reports and dashboards (largely in SQL) to uncover inefficiencies and help inform strategy. Eric considers himself to be a data science hybrid: he has a strong technical, statistics-driven foundation, but can also manage a project and communicates in a clear, organized, and effective manner, resulting in data that has an impact.
The Science of One-Hit Wonders
Classification / Natural Language Processing / SQL
Predicting if a song will be a one hit wonder using classification models, NLP, and 4 different datasets.

Erick Walker

Erick graduated Cum Laude from Lehigh University with a B.S. in Economics and Finance. Following school, he worked in the financial industry, most recently as an Equity Research Associate. In this role, he researched macro and industry trends of the oil & gas industry, built and maintained financial forecasts of oil & gas companies’ performance, and summarized these findings in notes to investors. While this role was heavy on data, he found himself pining for more rigorous analytical tools, and that’s where data science entered the picture. After spending several months teaching himself the basics of Python, he committed to learning the trade full-time.
Predicting Recessions: Can Machine Learning Improve Asset Allocation Strategies?
Time Series/Forecasting / Classification
Built a classification model that predicts the onset of recessions 12-months in the future. Using this model’s scores, develop trading rules that shift portfolios out of the stock market before recessions hit.

Erik Janér

Erik is a native New Yorker with a B.A. in Mathematics from Hobart and William Smith Colleges. After graduation he worked as a Risk Reporting Analyst for Citigroup's Franchise Risk Architecture division where he automated the aggregation and production of the Citigroup Fundamental Credit Review report. More recently, he was a Product Development Analyst for the financing and securitizations group. Here he developed solutions for many internal Citi clients related to balance sheet management, future income projections, human capital management, and regulatory review requirements. Wanting to leverage his math degree more, Erik enrolled in Metis. In his spare time, he works on developing trading strategies on cryptocurrencies with one strategy currently in production. He intends on leveraging many of the skills gained from his Metis experience to produce others.
Classifying Cryptocurrency Price Movements
SQL / Tableau/Dashboards / Time Series/Forecasting
Developed a classifier model to predict whether or not the price of a cryptocurrency would move up by more than one percent using technical analysis and historical data features.

Ethan Liwanag

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

Fisher Moritzburke

Fisher graduated from University of California at Santa Cruz with a B.S. in neuroscience and a minor computer science in December 2019. Instead of continuing his job search through the pandemic, he decided to build his skills and portfolio through Metis. Data science represents the intersection of several of Fisher’s passions: programming and problem solving. He hopes to continue learning and solve hard problems, and if possible utilize his background in biology. For his final project Fisher predicted whether genes were associated with diseases using a graph neural network. In his spare time he enjoys mountain biking and rock climbing.
Predicting Disease-Gene Associations
Regression / Classification / Cloud Computing (AWS/Google Cloud)
Predicting whether genes are associated with diseases using a graph neural network. This work has potential to reduce disease treatment research costs by focusing disease-gene association experimental validations on genes predicted to be associated.

Fong Wa C. Mui

Fong Wa brings to data science an enterprising sense for how to use data to navigate the business landscape, within companies both large and small. She is respected for her ability to capture the big picture alongside the details and mobilize stakeholders to engage with the mission at hand. Her career prior to Metis includes meaningful roles in corporate and entrepreneurial organizations including financial services and conversational AI. In her professional and personal life, Fong Wa is a charismatic go-to person for her colleagues, friends and family when seeking practical answers and tackling challenging problems. She holds an MBA from the University of Chicago and a BA with honors from the University of Pennsylvania.
Fong Wa C.
Clairvoyant Clustering of Consumer Complaints
Classification / Clustering / Natural Language Processing
A multi-faceted approach to analyzing text and clustering companies identified in consumer complaints filed with the Consumer Financial Protection Bureau. Tools and techniques include NLP (SpaCy and CorEx) and unsupervised machine learning (K-Means and PCA).

Jackson Honis

Jackson is as an Engineer with four years experience in the construction industry and a data scientist with almost two years of project-based learning, he leverages a unique experience that allows him to identify and solve problems faced in a fast-paced business setting. During his engineering career, he had a multitude of tasks ranging from implementing a project safety program from the ground-up to leading key processes in the turnover and recent opening of the LaGuardia Airport Headhouse Project in collaboration with a multidisciplinary team. Jackson is excited to refocus his career in data science.
Coordster - The Artist Collaboration Recommender
Clustering / Natural Language Processing / Regression
Coordster is a recommender system and machine learning model that identifies candidates to collaborate with a chosen artist, and predicts the popularity of the songs that they would create.

James Blau

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

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.

John Lee

John is passionate about using data to help inform better decision making, which was the primary driver for his decision to join Metis. John's work experience includes cleaning, analyzing and generating insights from a variety of datasets for several different stakeholder groups within his previous investment bank firm. Through Metis, John dedicated his time to working on several machine learning projects, leveraging new tools along the way. Prior to his career, John studied at Stony Brook University in NY where he obtained a combined Bachelors/Masters Degree in Applied Math and Statistics. Outside of work, John enjoys fitness, travel and camping. He's travelled to 20 countries within a span of four years!
Forecasting Citi Bike Demand
Classification / Natural Language Processing / Regression
Forecasted the daily demand for Citi Bike usage with a 365-day horizon using Facebook Prophet and WaveNet models. Developed a web app with Dash to visualize forecasted results for all stations with map navigation.

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.

Julian Manguinao

Julian graduated from Northeastern University with a BS in Finance and a minor in sustainable business practices. Prior to joining Metis he worked in consulting as a project manager delivering reporting, analytics, project management, and innovation capabilities for financial services clients. During his time as a consultant he began to work with cross functional teams to deliver operational insights through dashboards and reports where he found his passion for working with data. Julian enrolled in Metis to continue to develop his data skills through coding and modeling.
City of Bikes
Regression / SQL / Tableau/Dashboards
Predicted hourly Citi Bike traffic in New York City in order to improve bike station balances by utilizing a clustering and regression.

Kayla Starmer

Kayla is a Brooklyn native and has an innate curiosity and passion for getting to the crux of how things work. That passion is what attracted Kayla to neuroscience and ultimately obtaining her masters in clinical neuroscience in London. After graduation, she left the world of academia and landed her first job in sales strategy for a life sciences consultancy. In that role, Kayla discovered how much she enjoyed using her creativity to solve business problems analytically. Early on in her career, Kayla's strong communication skills were valuable when it came to bridging the gap between technical and non-technical stakeholders, which she continues to hone.
MedRec: A Drug Recommender System
Natural Language Processing / Recommender Systems / Tableau/Dashboards
Drug recommender system that uses NLP techniques and NMF. User enters symptoms in conversational English as input. Recommendation system gives symptom-relevant, highly rated drug recommendations along with relevant positive reviews as output.

Laura Lopez Cruz

Laura recently graduated from the CUNY Graduate Center with a Ph.D. in pure math. She spent the last five years studying algorithmic and model-theoretic properties of monoids and groups. She co-authored two publications on her results. Towards the end of her program, she was tasked with implementing a group theory algorithm (Magnus Breakdown) into a Python program. This sparked her interest in coding, and her desire to apply the knowledge and skills she acquired as a researcher in math led her to pursue a career in data science.
Lopez Cruz
A Book Recommendation System to #Decolonize your Bookshelf
Clustering / Natural Language Processing / Recommender Systems
To help readers "decolonize their bookshelves". Built a book recommendation system for Goodreads subscribers that recommends books written by authors of color.

Leonid Sim

Leonid was born in Uzbekistan and immigrated to the United States when he was 12. He graduated Magma Cum Laude from Boston University with a Bachelor of Science in Business Administration & Management. With a triple concentration in Finance, Information Systems, & Strategy, Leonid became a Financial Consultant and worked in the Investment Banking industry for the past three years. Leonid's passion for Data Science came after he realized the outdated technologies the Financial Industry utilized and wanted to discover how the world is utilizing Big Data & Neural Networks, in order to better himself as a consultant.
Citi Bike Station Demand & If Neural Networks can Predict the Unpredictable
Big Data / Natural Language Processing / Time Series/Forecasting
Utilized Google DeepMind WaveNet Neural Network in order to predict Hourly Citi Bike station demand.

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

Mary-Stewart Wachter

Mary-Stewart Wachter is a recent Geoscience Master’s graduate with a concentration in professional meteorology with an undergraduate degree in mathematics. Her academic background lends itself to a strong research, analytical, and problem-solving foundation, with a publication in the Alabama Journal of Mathematics and successfully defended her master’s thesis involving geospatial and climatological statistical analysis using R and ArcGIS. The introduction to data science provided by Mary-Stewart's academic endeavors paired with a curious mind propelled her to expand her knowledge immensely and attend Metis. Applying techniques and expertise learned through Metis, be it classifying or predicting weather conditions or creating character embeddings for StarWars characters, she is excited to further her career path as a data scientist to help provide solutions to today and tomorrow’s questions.
Temperature Range Prediction
Classification / Natural Language Processing / Time Series/Forecasting
Constructed a time series WaveNet style convolutional neural network, using historical temperature daily maximum and minimum temperatures for 240 locations in the northeast, to predict the following fourteen days temperature range for each location.

Merve Bas

Merve is a Data Scientist and Data-Driven Product Manager with a background that includes Banking, Payment Systems and Consumer Packaged Goods (CPG). Completed an accredited data science bootcamp to improve data science skills. Leverage communication and interpersonal strengths to collaborate across diverse groups and partner with stakeholders at all levels. Led highly complex projects in cross-functional teams during definition, development and delivery. Highly experienced in building product strategy and vision using SQL and machine learning logarithms with a proven leadership and management capabilities. Fully authorized to work in the US without sponsorship. Merve holds a Bachelors in Chemical Engineering and an MBA.
Music Recommendation System
Big Data / Regression / SQL
Trained CNN model as a classifier to identify music genres from the spectrogram images of Mp3 files.

Mitch Brinkman

Raised in Minnesota, educated in Chicago, Mitch now calls the City of Big Shoulders his home. It's been a wonderful path to the present, as his previous experience has developed a myriad of skills to bring him to data science. From account management, medical education, running a marketing department and video marketing production has left him with a humming curiosity that previously had nowhere to go. Data science allows Mitch to understand problems from the ground up and lets him communicate the story with gusto. Mitch relishes working within teams and can't wait to unveil more data stories.
The "Great Deliberators": Senate Speeches on Education, Banking & Healthcare
Natural Language Processing / Tableau/Dashboards / SQL
Exploring how the Senate has changed over the last four decades through NLP analysis of the topics covered during Senate floor speeches on education, healthcare and banking from 1980-2016.

Natalie Paley

Natalie is a graduate of St. John's College, an accomplished launch operator on the Antares Rocket Program, holds a degree in Mechanical Engineering, and is ready to make the world a better place with Data Science. Over her time at Metis, she has worked on diverse projects, including programming a bot on Discord (a chat service), creating a similarity model of the works of Shakespeare using Natural Language Processing, and investigating the relationship between borough income, budgets, crime rates, geography and complaints against the NYPD. Natalie is looking to work with people who see data science as a means to better the world, and wants to use her training to make sure that we have a more equitable future.
Visualization of NYPD Incidents in New York City
Classification / Natural Language Processing / Tableau/Dashboards
A visual exploration in Tableau of the relationship between borough income, geography, annual budget, crime rate, and complaints against the NYPD.

Nicole Semerano

Nicole earned a BA from Mary Washington College and a Masters in Teaching Social Studies from SUNY New Paltz. She is dual certified in Mathematics and Social Studies with over a dozen years of teaching experience. 21st Century education is full data analysis on student progress. Thus it has been a natural transition for her in becoming a Data Scientist. At Metis Nicole has showcased her skillset in fields related to her background and interests. She programmed supervised models for predictions on movie longevity and Baseball All-Star classification. Nicole additionally utilized Natural Language Processing to analyze presidential speeches throughout history. For her final project, Nicole is building a Neural Network to identify prescriptions from their photo.
Identifying Prescriptions with Convolutional Neural Networks
Classification / Natural Language Processing / Tableau/Dashboards
With the Baby Boomer generation at retirement and the opioid crisis in full swing, prescription medicine is all around us. Created a pill identifier using convolutional neural networks and other machine learning processes.

Rajkumar Katta

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

Paul Chang

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

Paul Giesting

A data science his entire career, Paul is a geologist by training with a thorough background in physics, chemistry, and mathematics. He graduated summa cum laude from Washington University in St. Louis and obtained a PhD from the University of Notre Dame with a dissertation focused on analyzing crystal structures from a large database. Paul worked for the State of Indiana, obtained his professional license, then returned to research and completed two postdocs. For the second postdoc Paul created a complex regression model to understand meteorite mineral chemistry. After teaching for several years, Paul decided to change course and became an independent consultant while working to transition to data science full-time.
Trouble in Paradise: Hawai'i in true and false colors
Big Data / Clustering / Regression
Principal component analysis, false color imaging, unsupervised spectral classification, and landform cluster analysis of multispectral image data from Hawai'i Island.

Samir Thanedar

Samir graduated with a BBA from the business school at the University of Michigan. In college, he founded a startup with friends and built a real-time reviews app for nightlife venues. Despite not being an engineer, he loved the technical process of building the app and moved to the Bay Area to keep learning. He joined Branch (now a unicorn startup) as an early sales employee and later worked in sales/customer success at two AI startups. Discovering the many problems data science can solve reignited his desire to build and led him to join Metis. At Metis, he built a linear regression model to predict how long of a career MLB players will have and for his final project built a local politician recommendation engine based on Twitter profiles.
Recommending Local Politicians using Twitter
Regression / Classification / Natural Language Processing
For my final project at Metis, I built a recommendation engine that recommends local politicians you should vote for in the Bay Area based on your Twitter profile. Check it out here:

Sonali Dasgupta

Sonali holds a BA in Biology and Psychology from Northwestern University and a MS in Pharmacology from the University of Tennessee Health Science Center. She began her career at Unity Biotechnology as a member of the preclinical in vivo pharmacology team, where she was responsible for designing experiments to test clinical hypotheses and led the team towards both a publication and a clinical candidate that has now proceeded into FDA trials. At Unity, she regularly used statistical and analytical tools which inspired her to build skills to work with big data, and this led her to pursue further training in programming, data science, and machine learning at Metis. For her final project, Sonali used image analysis and deep learning tools to build a convolutional neural network to detect pneumonia in chest X-rays.
Detecting Pneumonia with Deep Learning
Classification / Natural Language Processing / SQL
Classification model using a convolutional neural network to distinguish between x-rays of patients with and without pneumonia.

Tamara Skootsky

Tamara earned her BA&Sc in Environmental Science from McGill University in 2014. After working four years in the outdoor and active travel industries, she returned to academia to pursue her passion for behavioral research. Most recently, she served as a Social Science Research Coordinator at the Stanford Graduate School of Business where she collaborated with faculty members in Marketing and Organizational Behavior to run in-lab, online and field experiments. During this time she used her daily commute to prepare for Metis and apply to graduate school. This fall she will begin an MS in Industrial/Organizational Psychology at San Francisco State University. She currently seeks part-time and internship opportunities in data science and looks forward to combining her content knowledge with her technical expertise as a people scientist someday.
Get What You Need - A Reddit Thread Recommender
Regression / Classification / Natural Language Processing
Using data acquired with the Reddit API, this project utilizes natural language processing, topic modeling and cosine similarity to create a dynamic recommendation engine deployed as a Flask application.

Terry Prokop

Terry is an experienced professional in the areas of finance and education. He has a variety of interests, and is especially drawn to the tech space. Terry is a hands-on person by nature; appreciates knowing how things work from the ground up. One of his strongest skills is the value he places on collaboration. Terry is not afraid to go it alone, but most outstanding achievements are the result of many contributors. Terry makes a conscious effort to include everyone in the pursuit of excellence.
Predicting Pediatric Pneumonia using Machine Learning
Classification / Cloud Computing (AWS/Google Cloud) / Big Data
For this project, Terry built three models (RandomForest, XGBoost, and a 3-layer CNN) which predict if a pediatric chest x-ray is normal or indicates a pneumonia infection.

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.