• Analyzing large neural datasets to understand information processing in mouse vision

• Spearheading series on the history of AI and computing [series]

• Developing guidelines for the governance of AI targeted at children

• Author multimedia blog posts with compelling, original conent to give thousands of prospective students insight to my experience at MIT [blog]

• Engineer data visualizations to quantify and graphically illustrate aspects of MIT student life, academics, and traditions

• Posts have over 50,000 unique views and have been featured on the mit.edu homepage and MIT Daily

• Implemented bookmarking and sorting features to data annotation GUIs using PyQt5 and a distributed dataservice to optimize segmentation proofreading of neural imaging data

• Developed an open-source, end-to-end pipeline for differentially private ML/AI model creation [GitHub] [website]

• Implemented three differentially private models: linear regression (using the covariance method) compatible with scikit-learn, logistic regression (integrated using an IBM model), and stochastic gradient descent

• Used NLP to analyze the effect of stimulus-directed hypnagogic dreams on post-sleep creativity to prove efficacy of Dormio, a dream-interfacing tool developed by Adam Horowitz [article]

• Created a novel approach to computational creativity by applying GloVe vector representations and measuring the volume of convex hull across response words

• Awarded a Simons Summer Research Fellowship to spearhead a computational neuroscience research project under the mentorship of Dr. Il Memming Park

• Used ML to study information processing in the mouse visual cortex; examined population decoding of spatiotemporal features from the Allen Brain Observstory calcium imaging dataset

• Published a first-author paper in eNeuro and presented a poster at the 2018 Computational and Systems Neuroscience Conference

• Research featured in "Data Stories" video, a mini-documentary series made by the Allen Insitute