Lakshya Agrawal is an AI PhD student at UC Berkeley and an OlMo research intern focused on making code generation by large language models more reliable and practical for real-world software engineering. With a decade of experience spanning research roles at Microsoft Research, EPFL, and IIIT-D, he blends systems-level expertise—language runtimes, IDE/debugger tooling, and transpilers—with cutting-edge work on long-context reasoning, tool use, and decoding strategies for LLMs. His Monitor-Guided Decoding (NeurIPS'23) and open-source projects like multilspy and GEPA reflect a bias toward engineering solutions that let even smaller models produce correct code at repository scale. He has a track record of shipping lasting artifacts (e.g., a Maxima→Python transpiler now in Maxima) and thrives at the intersection of programming languages, developer tooling, and generative AI.
10 years of coding experience
3 years of employment as a software developer
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at University of California, Berkeley
AISSCE Science, AISSCE Science at Delhi Public School, Dwarka
Bachelor of Technology - BTech Computer Science and Applied Mathematics, Bachelor of Technology - BTech Computer Science and Applied Mathematics at Indraprastha Institute of Information Technology, Delhi
Software for conducting live lectures and recording to as small file size as possible
Contributions:64 commits, 21 PRs, 25 pushes in 1 month
file-sizepythonconductingrecording
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