I work at BlackRock AI Labs. 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 domain-specific languages for optimization, matrix-free optimization, and computational imaging, as well as other applications of convex optimization.
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 Performance
Technical Computing in Dynamic Languages, 2014.
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.
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. ACM
Transactions 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: Optimizing Image Classification Architectures for Raw Sensor Data. S. Diamond, V. Sitzmann, S. Boyd, G. Wetzstein, and F. Heide, Preprint, 2017.
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.
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.
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.
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.
Convex Optimization I, Stanford University (EE364a), Sum 2019.
Convex Optimization Short Course, ShanghaiTech, Shanghai, Spr 2016.
Convex Optimization Short Course, IMT, Lucca, Spr 2016.
Convex Optimization Short Course, CUHKSZ, Shenzhen, Spr 2016.
Convex Optimization II, Stanford University (EE364b), Spr 2019.
Convex Optimization I, Stanford University (EE364a), Win 2019.
Artificial Intelligence, Stanford University (CS221), Fall 2018.