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I am a fifth year Ph.D. student at Carnegie Mellon University advised by Giulia Fanti.
I work on large language models (LLMs), learning-to-rank (LTR), and privacy/security. Some research highlights: (1) I used LLMs to generate privacy-preserving synthetic data from private users that is high quality enough to replace on-device training (2) I developed a way for rankers (in recommendations, relevance) to learn from unlabeled data.
Before I started at CMU, I did my undergrad at Princeton University, where I worked with Yuxin Chen and Miklos Racz.
Google Scholar, Github, LinkedIn, CV
Contact me at [charlieh at andrew dot cmu dot edu].
I work on large language models (LLMs), learning-to-rank (LTR), and privacy/security. Some research highlights: (1) I used LLMs to generate privacy-preserving synthetic data from private users that is high quality enough to replace on-device training (2) I developed a way for rankers (in recommendations, relevance) to learn from unlabeled data.
Before I started at CMU, I did my undergrad at Princeton University, where I worked with Yuxin Chen and Miklos Racz.
Google Scholar, Github, LinkedIn, CV
Contact me at [charlieh at andrew dot cmu dot edu].
Work Experience
| Meta, AI Research Intern. Topic: Privacy-preserving synthetic data generation using large language models (LLMs). Redmond, WA, Summer 2023 |
| Meta, AI Research Intern. Topic: Federated Learning for large language models (LLMs). Redmond, WA, Fall 2022 |
| Amazon, Applied Science Intern. Topic: Self-supervised learning for Learning-to-rank (LTR). Palo Alto, CA, Summer 2022 |
| Amazon, Applied Science Intern. Topic: Reinforcement learning for sub-same day delivery optimization. Seattle, WA, Summer 2021 |
| Uber, Research Intern. Topic: Deep Radar Simulation. San Francisco, CA, Summer 2019 |