Santosh Pandey
PhD Candidate, Rutgers University

Room 535, CoRE Building
Piscataway, NJ
I am a PhD candidate in Computer Engineering at Rutgers University, supervised by Prof. Hang Liu. I completed my undergraduate from Tribhuvan University, Nepal where my undergraduate research advisor was Prof. Subarna Shakya. During my PhD, I interned at Google, Lawerence Berkeley National Lab and Brookhaven National Lab.
My research interests lie at the intersection of systems and ML/HPC applications, focusing on full-circle innovation–using ML/HPC to design better systems (more current focus) and building optimized systems for ML/HPC applications. Notably, I have worked on developing accurate and reusable ML-based microarchitecture simulators while also accelerating and scaling these simulation models with hardware-software co-design, demonstrating the synergy between system optimization and ML/HPC.
My long-term focus is on automating the design and optimization of computing stacks (programs, compilers, and microarchitecture), leveraging generative models for design generation and deep learning models for performance modeling/optimization.
I enjoy hiking and playing soccer in my free time.
ExchangeMeet: Diverse social between two labs from any field, something I started in our lab (learnings/blogs).
[ I am actively recruiting 2-3 highly motivated and dedicated PhD students to join my research group during the Fall 2025 and Spring 2026 cycle. These positions will be fully funded (tuition waiver + competitive stipend). My lab will focus on Machine Learning(ML)-driven computing systems, bridging high-performance computing, ML, and computer architecture.
If you have some background in one of the above areas and are interested in shaping the future of computing systems, please get in touch with me at santoshlab2025@gmail.com, including “Prospective PhD student-[Your name]” in the subject along with your CV, transcript, and your research interest. ]
News
Mar 21, 2025 | One paper Concorde: Fast and Accurate CPU Performance Modeling with Compositional Analytical-ML Fusion, led by Arash, accepted at ISCA’25. A great collaborative work during my time at Google. ![]() |
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May 15, 2024 | I will be joining Google for the summer as a student researcher working on ML methods to accelerate architecture simulation. ![]() |
Apr 11, 2024 | TAO: Rethinking DL-based microarchitecture simulation is accepted at SIGMETRIC’24 journal (Acceptance rate ~11%). A great collaboration with Amir from Google DeepMind. ![]() |
Mar 07, 2024 | TEA+, led by Dr. Chengying Huan, accepted at TACO’24 journal. ![]() |
Sep 15, 2022 | One paper accepted at SC’22 conference on accelerating ML-based microarchitecture simulation. ![]() |