![[Image (JPEG): Arman Zharmagambetov, Half Moon Bay, CA, USA, July 2023]](./files/profile3.png)
Arman Zharmagambetov 
Research ScientistMeta AI (FAIR)
Menlo Park, California, USA
Email: armanz [at] meta [dot] com
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Arman Zharmagambetov
Research Scientist |
I am a Research Scientist in the Fundamental AI Research (FAIR) team at Meta . My research primarily focuses on machine learning and optimization, with recent projects exploring their application in enhancing the security and robustness of AI models. Before that, I was appointed as a Postdoctoral Researcher at FAIR, where I was fortunate to work with Yuandong Tian on 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).
] A. Zharmagambetov*, A. Paulus*, C. Guo, B. Amos**, and Y. Tian** (2024): "AdvPrompter: Fast Adaptive Adversarial Prompting for LLMs".
(* = Equal 1st authors, ** = Equal advising)
[arXiv]
[code]
A. Zharmagambetov, A. Guo*, I. Evtimov*, M. Pavlova, R. Salakhutdinov and K. Chaudhuri: "AgentDAM: Privacy Leakage Evaluation for Autonomous Web Agents".
(* = Equal contribution)
[arXiv]
[code]
[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".
Advances in Neural Information Processing Systems, 2023.
[arXiv]
[code]
[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, 2022.
[external link]
[paper preprint]
[short video]
[poster]
[ICML] A. Ferber, A. Zharmagambetov, T. Huang, B. Dilkina, and Y. Tian (2023): "GenCO: Generating Diverse Designs with Combinatorial Constraints".
International Conference on Machine Learning, 2024.
[external link]
[arXiv]
[ICML] A. Zharmagambetov and M. Á. Carreira-Perpiñán: "Smaller, More Accurate Regression Forests Using Tree Alternating Optimization".
International Conference on Machine Learning, 2020.
[external link]
[paper preprint]
[supplementary material]
[slides]
[video]
