I am a fourth-year Statistics PhD student, broadly interested in causal inference, network embedding methods, representation and multi-modal learning, and differential privacy. In particular, my recent work addresses the use of NLP-inspired embedding methods to estimate peer contagion effects on networks, as well as investigating the causal representation learning properties of massive vision-language models, such as CLIP and DALL-E 2.
Looking to work with massive datasets, with not-so-tidy structures, and apply cutting-edge statistical and machine learning methods, as well as develop new ones tailored to address the main business objectives. I enjoy research, brainstorming, modeling, and getting my hands dirty with writing clean, scalable, and rigorously commented code. I am looking to make meaningful contributions both individually and as part of a team. I enjoy both sharing my knowledge and insights (having demonstrated mentorship experience), and also being curious, inquisitive, asking questions in order to expand my knowledge as quickly as possible.
- United States
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