Operations Research and Information Engineering
Damek Davis received his Ph.D. in mathematics from the University of California, Los Angeles in 2015. In July 2016 he joined Cornell University’s School of Operations Research and Information Engineering as an Assistant Professor.
Damek is broadly interested in the mathematics of data science, particularly the interplay of optimization, signal processing, statistics, and machine learning.
- Data Science
- Signal and Image Processing
- Statistics and Machine Learning
- Data Mining
- Stochastic model-based minimization of weakly convex functions. Damek Davis, Dmitriy Drusvyatskiy. SIAM J. Optim., 29(1), pp. 207–239. (33 pages)
- Stochastic subgradient method converges on tame functions. Damek Davis, Dmitriy Drusvyatskiy, Sham Kakade, Jason D. Lee. Foundations of Computational Mathematics, Vol. 20, pp. 119–154 (2020).
- The nonsmooth landscape of phase retrieval. Damek Davis, Dmitriy Drusvyatskiy, Courtney Paquette. IMA Journal of Numerical Analysis (2017).
- Active strict saddles in nonsmooth optimization. Damek Davis, Dmitriy Drusvyatskiy.
- Low-rank matrix recovery with composite optimization: good conditioning and rapid convergence. Vasileios Charisopoulos, Yudong Chen, Damek Davis, Mateo Díaz, Lijun Ding, Dmitriy Drusvyatskiy.
- A three-operator splitting scheme and its optimization applications. Davis, Damek, and Wotao Yin. Set-valued and variational analysis 25.4 (2017): pp. 829-858.
Selected Awards and Honors
- Sloan Research Fellowship in Mathematics (2020)
- INFORMS Optimization Society Young Researchers Prize (2019)
- Finalist for the Best Paper Prize for Young Researchers in Continuous Optimization (2019)
- Finalist for A. W. Tucker Prize for outstanding doctoral thesis (2018)
- NSF Math Postdoctoral Fellowship (2015)
- Pacific Journal of Mathematics Dissertation Prize (2015)
- INFORMS Optimization Society Student Paper Prize (2014)