Steven Diamond

Steven Diamond Ph.D. Candidate Department of Computer Science Stanford University Advisor: Professor Stephen Boyd Co-Advisors: Professor Gordon Wetzstein, Professor Chris Ré CV

Contact

Email: diamond [at] cs (dot) stanford (dot) edu
Office: Packard 243, 350 Serra Mall, Stanford, CA 94305

Research Interests

• Domain-specific languages for optimization
  

• Matrix-free optimization
  

• Computational imaging
  

Papers

• Google Scholar profile
  

• Disciplined Geometric Programming. A. Agrawal, S. Diamond, and S. Boyd, Optimization Letters, March 2019.
  

• Network Optimization for Unified Packet and Circuit Switched Networks. P. Yin, S. Diamond, B. Lin, and S. Boyd, To appear, Optimization and Engineering, 2019.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• 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.
  

• Disciplined Convex-Concave Programming. X. Shen, S. Diamond, Y. Gu, and S. Boyd. Proceedings of CDC, 2016.
  

• Stochastic Matrix-Free Equilibration. S. Diamond and S. Boyd. Journal of Optimization Theory and Applications, 172(2), 436-454, 2016.
  

• Convex Optimization with Abstract Linear Operators. S. Diamond and S. Boyd. Proceedings of ICCV, 2015.
  

• CVXPY: A Python-Embedded Modeling Language for Convex Optimization. S. Diamond and S. Boyd. Journal of Machine Learning Research, 17(83):1-5, 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.
  

• 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.
  

Projects

• CVXPY, a Python-embedded modeling language for convex optimization.

• ProxImaL, a domain-specific language for image optimization.

• dcp.stanford.edu, an online visualization tool for disciplined convex programming.

• A matrix-free version of CVXPY to accompany the paper “Matrix-free Convex Optimization Modeling”.

• A matrix-free version of SCS to accompany the paper “Matrix-free Convex Optimization Modeling”.

• A matrix-free version of POGS to accompany the paper “Convex Optimization with Abstract Linear Operators”.

• A package that integrates matrix-free CVXPY and the matrix-free solvers.

• 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.

• cvxflow, a TensorFlow backend for CVXPY.

• Find more projects on my Github page.