Work Experience
• Sumitomo Pharma America, New York, NY. Senior Manager, Digital Innovation (Feb. 2020 - Present)
Sumitomo Pharma (formerly Sumitovant) is a pharmaceutical company. I work in Digital Innovation, a team of business-facing
data scientists and software engineers. Some projects I have worked on include:
• Search and visualization of document embeddings generated from large corpora of scientific abstracts
• Applying sequence classification to information extraction from scientific abstracts
• an OCR tool enabling scientists to extract scientific data from publications/reports
• an application that enables reporting of clinical trial progress on a regular basis to senior management,
and generation of up-to-date interactive reports
• SimpleBet, New York, NY. Data Scientist (Jan. 2019 - Jan. 2020)
SimpleBet is a startup developing predictive analytics for the sports betting industry. As a Data Scientist,
I developed statistical models that predicted in-play (e.g. per at-bat/drive) and game outcomes in professional sports games,
along with tools and techniques for evaluating the models in various settings.
• Harvard University, Cambridge, MA. Teaching Fellow, Data Science 1 (Fall 2018)
CS109a is a comprehensive introduction to: Python for data science; regression and classification
(linear regression, nearest neighbors regression, logistic regression,
discriminant analysis, bagging/random forest/boosting, SVM); feature selection; dimensionality reduction; and
an introduction to neural networks.
As a Teaching Fellow, I held weekly office hours, provided feedback on student homeworks, and
mentored four final project groups working with data from the Alzheimer's Disease Neuroimaging Initiative. The Fall
2018 course was taught by Pavlos Protopapas and Kevin Rader.
• Perceptive Automata, Somerville, MA. Computer Vision and Data Science
Intern (Summer 2018)
Perceptive Automata was an autonomous driving startup that developed predictive models of
pedestrians and cyclists. As an intern, I implemented an augmentation step in the
training pipeline of a neural network for image classification, that corrected inconsistent distortions arising
from the various cameras the company used to collect training data.
I also examined the effects of the corrections on the predictive performance
of the classification model on subgroups of the data.
• DecisionQ, Arlington, VA. Software Engineering Intern (Summer 2017, Winter 2018)
DecisionQ is a predictive analytics company in the healthcare and public sectors. During my internships,
I implemented graph algorithms and data structures in C++ and integrated them into the company's
analytics products.
• Morgan Stanley, New York, NY. Technology Summer Analyst (Summer 2015, Summer 2016)
As an intern for two summers, I worked in the Fixed
Income Risk Infrastructure and High Performance Engineering teams. My projects
dealt with enhancing evaluation tools implemented in Scala and Python
for internal trading and risk computation systems.
• PDT Partners, New York, NY. Summer Technology Analyst (Summer 2014)
PDT is a quantitative hedge fund. As an intern, I worked on a tool enabling their traders to
perform daily maintenance activities more efficiently.
Education
• Harvard University
Master of Science '19, Computational
Science and Engineering
• Columbia University
Bachelor of Science,
magna cum
laude '17, Applied Mathematics with Minor in Computer Science
• Thomas Jefferson High School for Science and Technology
Advanced Studies Diploma '13, Computer Systems Lab