Steven Diamond
Time 2022-10-18 10:00:37Web Name: Steven Diamond
WebSite: http://www.stevendiamond.me
ID:357101
Keywords:
Steven,DiamondDescription:
Steven Diamond
1500 E Hamilton Ave · Campbell, CA 95008 · diamond@cs.stanford.eduI work at Gridmatic. I did my Ph.D. in Computer Science at Stanford University under Professor Stephen Boyd. I was co-advised by Professor Gordon Wetzstein and by Professor Chris Ré. My research interests include software for optimization and applications of optimization to financial planning, as well as other use cases.
Publications
Thesis: Domain-Specific Languages for Convex and Non-Convex Optimization
Domain-specific languages for optimization
Embedded Code Generation with CVXPY. M. Schaller, G. Banjac, S. Diamond, A. Agrawal, B. Stellato, and S. Boyd, IEEE Control Systems Letters, 2022.
Log-Linear Dynamical Systems. S. Diamond, Preprint, 2020.
Differentiable Convex Optimization Layers. A. Agrawal, B. Amos, S. Barrat, S. Boyd, S. Diamond, and J. Z. Kolter, Advances in Neural Information Processing Systems, 2019.
Disciplined Geometric Programming. A. Agrawal, S. Diamond, and S. Boyd, Optimization Letters, March 2019.
A Rewriting System for Convex Optimization Problems A. Agrawal, R. Verschueren, S. Diamond, and S. Boyd, Journal of Control and Decision, 5(1):42–60, 2018.
A General System for Heuristic Minimization of Convex Functions over Nonconvex Sets. S. Diamond, R. Takapoui, and S. Boyd. Optimization Methods and Software, 33(1):165–193, 2018.
Disciplined Multi-Convex Programming. X. Shen, S. Diamond, Y. Gu, and S. Boyd. Proceedings of Chinese Conference on Decision and Control, 2017.
SnapVX: A Network-Based Convex Optimization Solver. D. Hallac, C. Wong, S. Diamond, R. Sosic, S. Boyd, and J. Leskovec. Journal of Machine Learning, 18(4):1−5, 2017.
A New Architecture for Optimization Modeling Frameworks. M. Wytock, S. Diamond, F. Heide, and S. Boyd. Proceedings of the Workshop on Python for High-Performance and Scientific Computing, 2016.
Disciplined Convex-Concave Programming. X. Shen, S. Diamond, Y. Gu, and S. Boyd. Proceedings of CDC, 2016.
CVXPY: A Python-Embedded Modeling Language for Convex Optimization. S. Diamond and S. Boyd. Journal of Machine Learning Research, 17(83):1-5, 2016.
Disciplined Convex Stochastic Programming: A New Framework for Stochastic Optimization. A. Ali, Z. Kolter, S. Diamond, and S. Boyd. Proceedings of the Conference on Uncertainty in Artificial Intelligence, 2015.
Convex Optimization in Julia. M. Udell, K. Mohan, D. Zeng, J. Hong, S. Diamond, and S. Boyd. Proceedings of the Workshop for High PerformanceTechnical Computing in Dynamic Languages, 2014.
Financial planning
Optimal Claiming of Social Security Benefits. S. Diamond, S. Boyd, D. Greenberg, M. Kochenderfer, and A. Ang. In Submission, 2021.
Matrix-free optimization
Stochastic Matrix-Free Equilibration. S. Diamond and S. Boyd. Journal of Optimization Theory and Applications, 172(2), 436-454, 2016.
Matrix-free Convex Optimization Modeling. S. Diamond and S. Boyd. In: Boris Goldengorin (Ed.). Optimization and Its Applications in Control and Data Sciences: in Honor of Boris T. Polyak’s 80th Birthday. Springer Optimization and Its Applications, Vol. 115, Pages 221-264, Springer, New York, 2016.
Convex Optimization with Abstract Linear Operators. S. Diamond and S. Boyd. Proceedings of ICCV, 2015.
Computational imaging
Non-line-of-sight Imaging with Partial Occluders and Surface Normals. F.Heide, M. O’Toole, K. Zang, D. Lindell, S. Diamond and G. Wetzstein. ACMTransactions on Graphics, 2019.
End-to-end Optimization of Optics and Image Processing for Achromatic Extended Depth of Field and Super-resolution Imaging. V. Sitzmann, S. Diamond, Y. Peng, X. Dun, S. Boyd, W. Heidrich, F. Heide, and G. Wetzstein, ACM SIGGRAPH, 2018.
Sub-picosecond photon-efficient 3D imaging using single-photon sensors. F. Heide, S. Diamond, D. Lindell, and G. Wetzstein, Scientific Reports, 2018.
Unrolled Optimization with Deep Priors. S. Diamond, V. Sitzmann, F. Heide, and G. Wetzstein, Preprint, 2017.
Dirty Pixels: Towards End-to-End Image Processing and Perception. S. Diamond, V. Sitzmann, F. Julca-Aguilar, S. Boyd, G. Wetzstein, and F. Heide, ACM Transactions on Graphics, 2021.
Reconstructing Transient Images from Single-Photon Sensors. M. O'Toole, F. Heide, D. Lindell, K. Zang, S. Diamond, and G. Wetzstein. Proceedings of CVPR, 2017.
ProxImaL: Efficient Image Optimization Using Proximal Algorithms. F. Heide, S. Diamond, M. Niessner, J. Ragan-Kelley, W. Heidrich, and G. Wetzstein. Proceedings of ACM SIGGRAPH, 2016.
Other optimization applications
Network Optimization for Unified Packet and Circuit Switched Networks. P. Yin, S. Diamond, B. Lin, and S. Boyd. Optimization and Engineering, 21(1):159–180, 2020.
Multi-Period Trading via Convex Optimization. S. Boyd, E. Busseti, S. Diamond, R. Kahn, K. Koh, P. Nystrup, and J. Speth. Foundations and Trends in Optimization, 3(1):1–76, 2017.
Software
CVXPY
CVXPY is an open-source modeling framework for convex optimization in Python, with tens of thousands of individual users.
Major corporate CVXPY users include Tesla, Netflix, BlackRock, Two Sigma, and Intuit.
CVXPY has also been used to teach classes at Stanford, CMU, MIT, Berkeley, UCLA, and other universities.
Other software
DCCP, a CVXPY extension for difference-of-convex programming.
NCVX, a CVXPY extension for heuristic solution of nonconvex problems.
DMCP, a CVXPY extension for multi-convex programming.
ProxImaL, a domain-specific language for image optimization.
dcp.stanford.edu, an online visualization tool for disciplined convex programming.
Teaching
Head instructor
Convex Optimization I, Stanford University (EE364a), Sum 2019.
Convex Optimization Short Course, ShanghaiTech, Shanghai, Spr 2016.
Co-instructor
Convex Optimization Short Course, IMT, Lucca, Spr 2016.
Convex Optimization Short Course, CUHKSZ, Shenzhen, Spr 2016.
Teaching assistant
Convex Optimization II, Stanford University (EE364b), Spr 2019.
Convex Optimization I, Stanford University (EE364a), Win 2019.
Artificial Intelligence, Stanford University (CS221), Fall 2018.
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