Niranjan Hasabnis

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niranjan[dot]hasabnis[at]intel[dot]com

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About me

I am currently a Principal Research Scientist at Code Metal, where we are developing and applying AI and formal method techniques for code migration and optimization. My research interests are at the intersection of application of artificial intelligence, machine learning, and formal method techniques to problems in compilers, HPC, software systems, and software engineering. For past few months, along with my collaborators, I have been also exploring AI-driven techniques to automatically parallelize serial code for shared-memory and distributed-memory systems.

Before joining Code Metal, I was a Research Scientist at Machine Programming Lab in Intel Labs. I was also part of a research project exploring an MLIR-based compiler for high-performance deep learning on Intel Xeon platforms. Orthogonally, I also implemented and published an autonomous system, named ControlFlag, that learns to detect programming errors in code. ControlFlag is open-source now, and has been covered by several news outlets such as Communications of ACM, Venturebeat, ZDNet, TechRepublic, etc. Additionally, as a part of my interest, I was also collaborating with Prof. Alvin Cheung and his group at UC Berkeley on the problem of code translation with formal verification.

Prior to joining Intel, I was a PhD student at Secure Systems Lab at Stony Brook University, and I was advised by Prof. R. Sekar. At Stony Brook, I conducted research in program analysis, symbolic execution, machine learning techniques to learn code translators, and binary analysis.

(Some of the) Publications

(Complete list is in Google Scholar)

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Awards