Tianle Cai
Graduate Research Assistant
New Jersey, United States
Summary
🤩
Rockstar🎓
Top SchoolTianle Cai is an ML-focused PhD candidate at Princeton with nine years of research and industry experience accelerating large-model inference and co-designing model, algorithm, and systems stacks. He has driven applied research at Together AI, Google DeepMind, and Microsoft, contributed to high-impact open-source projects like FasterDecoding (Medusa, BitDelta) and LLM-ToolMaker, and interned on VisionPro at Apple. Combining dual undergraduate degrees in Computer Science and Applied Mathematics from Peking University with visiting research stints at MIT and UC Berkeley, he blends rigorous theory with systems-first engineering. A life-long learner, hacker, and angel investor, he frequently bridges prototype research and production considerations to speed real-world ML deployment.
9 years of coding experience
5 years of employment as a software developer
Visiting Researcher Deep Learning Theory, Visiting Researcher Deep Learning Theory at University of California, BerkeleyResearch Intern Learning Theory, Research Intern Learning Theory at Massachusetts Institute of TechnologyBachelor of Science - BS Computer, Bachelor of Science - BS Computer at Peking UniversityDoctor of Philosophy - PhD Search form Search Electrical and Computer Engineering, Doctor of Philosophy - PhD Search form Search Electrical and Computer Engineering at Princeton University
Github Skills (36)
pytorch10
artificial-intelligence10
python10
facebook10
adversarial-learning10
conformer10
ctc10
sequence-to-sequence10
nlp10
sequence10
transformer-architecture10
llm9
machine-learning9
image-translation9
few-shot-learning9
Programming languages (2)
Github contributions (5)
FasterDecoding/Medusa
Sep 2023 - Mar 2025
Medusa: Simple Framework for Accelerating LLM Generation with Multiple Decoding Heads
Contributions:17 PRs, 23 pushes, 6 branches in 1 year 6 months
llmllm-inference
ctlllll/SGConv
Oct 2022 - Jan 2023
Contributions:5 commits, 2 pushes, 1 branch in 3 months
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