PhD Candidate, Electrical Engineering, Stanford University
Maintainer, CVXPY
ptnobel@stanford.edu
Bio
I am a PhD candidate at Stanford University working with ProfessorsStephen Boyd andEmmanuel Candès onoptimization and its applications in statistics. I am supported by theNational Science Foundation Graduate Research Fellowship Program (NSFGRFP).
I am a maintainer and Technical Steering Council member forCVXPY. I am also the current maintainer ofCVXPYlayers anddiffcp.
In the summer of 2024, I worked atGridmatic on applyingdifferentiable optimization in energy markets. In undergrad, I internedat Apple working on embedded systems and scientific computing and at HPworking on data infrastructure.
In Winter 2023, I was the Head TA forEE364a/CME364a,Stanford’s 230 student graduate convex optimization class. During Summer2023, I was the instructor forEE364a/CME364a; as theinstructor I revised the class content and lecture slides for the firsttime in over two decades. The new slides are availablehere.
During the summer of 2022 and part-time till the summer of 2024, Iwas a Visiting Scholar at UC Berkeley working withRiley Murray, ProfessorMichael Mahoney,and Professor Jim Demmel on randomized numerical linear algebra as partof theBALLISTICProject.
Prior to attending Stanford, I was a Regents’ and Chancellor’sScholar at UC Berkeley where I earned a Bachelors of Science inElectrical Engineering and Computer Science (EECS). I worked withProfessorJaijeetRoychowdhury on system theory and numerical methods. In Spring 2021,I was the sole TA forEECS219A,Berkeley’s graduate numerical simulation and modeling class.
Publications
- Y. Chen, D. Tse,P. Nobel, P. Goulart, S. Boyd,CuClarabel: GPU Acceleration for a Conic Optimization Solver.arXiv:2412.19027[math.OC]
- P. Nobel, A. Rozenshtein, C. Sharma, Open-AccessAI: Lessons From Open-Source Software. The Lawfare Institute.https://www.lawfaremedia.org/article/open-access-ai--lessons-from-open-source-software
- 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, FastPath Planning Through Large Collections of Safe Boxes.IEEETransactions 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-AwarePlanning with Diffusion Dynamics Model.NeurIPS 2023.https://neurips.cc/virtual/2023/poster/71449
- P. Nobel, E. Candès, S. Boyd, Tractable Evaluationof Stein’s Unbiased Risk Estimate for Convex Regularizers.IEEETransactions on Signal Processing. 2023.https://doi.org/10.1109/TSP.2023.3323046
- P. Nobel, A. Agrawal, S. Boyd, Computing TighterBounds on then-Queens Constant viaNewton’s Method.Optimization Letters17,1229–1240 (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 IsingMachines.Natural Computing20, 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 OptimisationProblems Using Oscillator Based Ising Machines. UnconventionalComputation and Natural Computation (UCNC), August 2020.
- P. Nobel,
auto_diff: An AutomaticDifferentiation 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:
- Conference on the Laws of War for the CyberEra Background Guide
- Excerpts addressing power gridinfrastructure from Group of Latin American and Carribean Countries:2020 Background Guide
- US Senate: Data Privacy BackgroundGuide
I also have written quick-reference theorem lists for a coupleclasses at Berkeley. Other people have told me they’re useful. I make noguarantees about the absence or presence of typos.