Bio

I am a PhD candidate at Stanford University working with Professors Stephen Boyd and Emmanuel Candès on optimization and its applications in statistics. I am supported by the National Science Foundation Graduate Research Fellowship Program (NSF GRFP). In Winter 2023, I was the Head TA for EE364a/CME364a, Stanford’s 230 student graduate convex optimization class. During Summer 2023, I was the instructor for EE364a/CME364a; as the instructor I revised the class content and lecture slides for the first time in over two decades. The new slides are available here.

In the summer of 2024, I worked at Gridmatic on applying differentiable optimization in energy markets. In undergrad, I interned at Apple working on embedded systems and scientific computing and at HP working on data infrastructure.

During the summer of 2022 and part-time till the summer of 2024, I was a Visiting Scholar at UC Berkeley working with Riley Murray, Professor Michael Mahoney, and Professor Jim Demmel on randomized numerical linear algebra as part of the BALLISTIC Project.

Prior to attending Stanford, I was a Regents’ and Chancellor’s Scholar at UC Berkeley where I earned a Bachelors of Science in Electrical Engineering and Computer Science (EECS). I worked with Professor Jaijeet Roychowdhury on system theory and numerical methods. In Spring 2021, I was the sole TA for EECS219A, Berkeley’s graduate numerical simulation and modeling class.

Papers

P. Nobel, D. LeJeune, E. Candès, RandALO: Out-of-sample Risk Estimation in No Time Flat. arXiv:2409.09781 [math.ST]
T. Marcucci, P. Nobel, R. Tedrake, S. Boyd, Fast Path Planning Through Large Collections of Safe Boxes. IEEE Transactions on Robotics. 2024. https://ieeexplore.ieee.org/document/10612232
J. Sun, Y. Jiang, J. Qiu, P. Nobel, M. Kochenderfer, M. Schwager, Conformal Prediction for Uncertainty-Aware Planning with Diffusion Dynamics Model. NeurIPS 2023. https://neurips.cc/virtual/2023/poster/71449
P. Nobel, E. Candès, S. Boyd, Tractable Evaluation of Stein’s Unbiased Risk Estimate for Convex Regularizers. IEEE Transactions on Signal Processing. 2023. https://doi.org/10.1109/TSP.2023.3323046
P. Nobel, A. Agrawal, S. Boyd, Computing Tighter Bounds on the n-Queens Constant via Newton’s Method. Optimization Letters 17, 1229–1240 (2023). https://doi.org/10.1007/s11590-022-01933-2
T. Wang, L. Wu, P. Nobel, and J. Roychowdhury, Solving Combinatorial Optimisation Problems Using Oscillator Based Ising Machines. Natural Computing 20, 287–306 (2021). https://doi.org/10.1007/s11047-021-09845-3
[Invited Paper] T. Wang, L. Wu, P. Nobel, and J. Roychowdhury, Solving Combinatorial Optimisation Problems Using Oscillator Based Ising Machines. Unconventional Computation and Natural Computation (UCNC), August 2020.
P. Nobel, auto_diff: An Automatic Differentiation Package for Python, SpringSim’20, May 2020. https://dl.acm.org/doi/10.5555/3408207.3408219

Miscellaneous Other Writings

I occasionally did significant writing for the UC Berkeley Model UN. That work is provided below:

I also have written quick-reference theorem lists for a couple classes at Berkeley. Other people have told me they’re useful. I make no guarantees about the absence or presence of typos.