![[Image (JPEG): Arman Zharmagambetov, Half Moon Bay, CA, USA, July 2023]](./files/profile3.png)
Arman Zharmagambetov 
Postdoctoral ResearcherMeta AI (FAIR)
Menlo Park, California, USA
Email: armanz [at] meta [dot] com
azharmagambetov [at] ucmerced [dot] edu
|
Arman Zharmagambetov
Postdoctoral Researcher |
I am a postdoctoral researcher in Fundamental AI Research (FAIR) at Meta 
, where I am fortunate to work with Yuandong Tian, Brandon Amos and Chuan Guo. My research is broadly on machine learning and optimization, recently involving AI-guided optimization, reinforcement learning and combinatorial optimization.
I completed my Ph.D. at University of California, Merced (UCM) advised by Miguel Á. Carreira-Perpiñán where I primarily studied learning algorithms for decision trees and tree-based models (see TAO). Check out my [CV].
[NeurIPS] A. Zharmagambetov, B. Amos, A. Ferber, T. Huang, B. Dilkina, and Y. Tian (2023): "Landscape Surrogate: Learning Decision Losses for Mathematical Optimization Under Partial Information".
[arXiv]
[code]
A. Ferber, A. Zharmagambetov, T. Huang, B. Dilkina, and Y. Tian (2023): "GenCO: Generating Diverse Solutions to Design Problems with Combinatorial Nature".
[arXiv]
[NeurIPS] A. Zharmagambetov and M. Á. Carreira-Perpiñán (2022): "Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization".
Advances in Neural Information Processing Systems (NeurIPS 2022).
[external link]
[paper preprint]
[short video]
[poster]
[ICML] A. Zharmagambetov and M. Á. Carreira-Perpiñán (2020): "Smaller, More Accurate Regression Forests Using Tree Alternating Optimization".
International Conference on Machine Learning (ICML 2020), Jul. 13, 2020.
[external link]
[paper preprint]
[supplementary material]
[slides]
[video]
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