Kanan Gupta
कनन गुप्ता کنن گپتا
Email: kanan.g@pitt.edu
Office: 623 Thackeray Hall
Department of Mathematics
University of Pittsburgh
I am a final-year PhD student in the Department of Mathematics at the University of Pittsburgh. My advisor is Dr. Stephan Wojtowytsch. My research interests lie broadly in the mathematics of machine learning, and specfically I have been focusing on optimization algorithms for machine learning. My work has been on proving accelerated convergence rates for gradient-based first-order algorithms in settings that more closely resemble neural network training, and improving those algorithms in the process. I am also broadly interested in the other areas at the intersection of mathematics and machine learning, like the use of ML for solving PDEs (scientific machine learning) and automated theorem proving.
I interned at Amazon Web Services as an Applied Scientist in summer 2025, where I worked on improving Large Language Models' agentic reasoning and theorem proving capabilities by training the model to use Lean as a tool during inference-time. Please see my resume for more information.
I started my PhD at Texas A&M University in January 2021 before moving to Pitt with my advisor. Before Texas A&M, I was at Ashoka University where I completed my undergraduate studies with a major in Mathematics and a minor in Computer Science. Afterwards, I did a year-long postgraduate diploma in research at Ashoka University, for which I wrote an expository thesis on K-Theory and C*-Algebras.
Publications
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Nesterov acceleration despite very noisy gradients
Kanan Gupta, Jonathan Siegel, and Stephan Wojtowytsch.
Advances in Neural Information Processing Systems (NeurIPS) 2024. arXiv:2302.05515 -
Nesterov acceleration in benignly non-convex landscapes
Kanan Gupta and Stephan Wojtowytsch.
International Conference on Learning Representations (ICLR) 2025, Spotlight (top 5.1% of all submissions). arXiv:2410.08395 -
Tool-Assisted Multi-Turn Theorem Proving with LLMs
Kanan Gupta, Jannis Limperg, and Udaya Ghai.
MATH-AI Workshop at NeurIPS 2025. -
Momentum-based minimization of the Ginzburg-Landau functional on Euclidean spaces and graphs
Oluwatosin Akande, Patrick Dondl, Kanan Gupta, Akwum Onwunta, Stephan Wojtowytsch.
Preprint, 2025. arXiv:2501.00389
Service
- Reviewer, ICLR 2025.
- Reviewer, Journal of Machine Learning Research (JMLR).