I’m at the moment focused on learning about AI Interpretability and Fairness.
One recent project I was involved with is Stanford’s Input on the European Commission White Paper “On Artifical Intelligence - European Approach to excellence and trust.” The paper can be found on Stanford’s Human-Centered Artifical Intelligence policy page here.
I’m currently doing research at the Stanford AI Lab, advised by Prof. James Zou. One recent project I worked on is the effect of competing multi-agent feedback on learning can be found here.(CoopAI Workshop @ NeurIPS 20’, to appear in AISTATS 21’, HAI Post).
Previously I’ve worked on projects in Deep Learning research @Stanford ML(Professor Andrew Ng’s research group, CC @ NeurIPS 19’), @World Bank Group (Famine Prediction), and Stanford Center for Biomedical Informatics Research (EHR Patient Outcomes, WiML 18’). Last summer, I worked on improving image label granularity for company-wide Vision Semantic Service @ Google (visual search applications include Lens, Vision AI, OCR). Freshman summer I worked on Genome Assembly software for Stanford Biology and worked on a deep learning project at a startup called Omniscience.
Sample Technical Courses
I proposed, designed and taught CS81SI AI Interpretability & Fairness at Stanford listed under CS81SI in Spring 2020, with the invaluable support of my faculty sponsors Professor James Zou and Professor Omer Reingold. For background on the class and similar class offerings, please check out this School of Engineering post. For more information on the course, please feel free to reach out to me.
CS 107, 109, 110, 161 - All of CS Core
CS 140 - Operating Systems
CS 144 - Computer Networking - Best TCP Sender Assignment Award
CS 205L - Continuous Methods in Machine Learning
CS 229 - Machine Learning, Project on Financial Ratio Predictor Efficacy On Long-Term Investment Outcome
CS 224N - Natural Language Processing with Deep Learning, Project on Character-Aware Direct Output Language Models
CS 231N - Convolutional Neural Networks for Visual Recognition, Project on Non-Linear Concept Vectors
CS 234 - Reinforcement Learning, Project on Intrinsic Motivation in Meta Learning
CS 236 - Deep Generative Models, Project on Entropy Regularization in Conditional GANs
CS 255 - Cryptography
MATH 63DM - Modern Mathematics: Discrete Methods
MATH 104 - Applied Matrix Theory
MATH 120 - Groups & Rings (Honours, Writing in the Major)
MATH 121 - Galois Theory
MATH 148 - Algebraic Topology
MATH 158 - Stochastic Processes
MATH 159 - Discrete Probabilistic Methods
MATH 171 - Real Analysis (Honours, Writing in the Major)
MATH 231 - Mathematics and Statistics of Gambling
MATH 233B - Topics in Combinatorics