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Kumar Kshitij Patel
Postdoctoral Associate, |
About Me
I am a postdoctoral associate at Yale FDS. Before joining Yale, I was a PhD student at the Toyota Technological Institute at Chicago (TTIC), where I had the privilege of being advised by Prof. Nati Srebro and Prof. Lingxiao Wang. Throughout my research career, I have explored various facets of collaborative learning, focusing on proving theoretical guarantees for optimization and ensuring the privacy of distributed algorithms amid data and systems heterogeneity. Recently, I have been interested in examining the incentives that encourage agents to initiate and sustain these collaborations (our recent workshop).
For an (mostly) up-to-date list of my publications, please visit my Google Scholar profile. You can also access my CV here.
During Summer 2023, I worked with Nidham Gazagnadou and Lingjuan Lyu from the Privacy Preserving Machine Learning team at Sony AI in Tokyo, Japan as a research intern. During summer 2020, I worked with the amazing team at Codeguru, Amazon Web Services as an applied scientist intern. And before joining TTIC, I obtained my BTech in Computer Science and Engineering at Indian Institute of Technology, Kanpur. There I was fortunate to work with Prof. Purushottam Kar on Bandit Learning algorithms. I also spent a year of my undergraduate on an academic exchange at École Polytechnique Fédérale de Lausanne (EPFL) where I worked at the Machine Learning and Optimization Laboratory (MLO) with Prof. Martin Jaggi.
I am actively looking for collaborators at Yale and beyond. Please feel free to reach out to me!
News
Really excited to be co-organizing a TTIC summer workshop with some wonderful people.
I have graduated from TTIC and moved to Yale FDS as a postdoctoral associate. My thesis, titled "What Makes Local Updates Effective: The Role of Data Heterogeneity and Smoothness," can be found here.
Research Activities
I co-organized a workshop on Incentives for Collaborative Learning and Data Sharing at TTIC this summer.
I co-organized a workshop on Theoritical Advances in Federated Learning last summer (2023) at TTIC.
I co-taught a tutorial at UAI'23 titled Online Optimization meets Federated Learning.
I served/am serving as a reviewer for STOC'21, TMLR, JMLR, ICML'21'22'24, NeurIPS'21'22'23'24, ICLR'22'23'24, AISTATS'22'23, Springer MLJ, as a session chair for ICML'22, NeurIPS'22, and as a volunteer for IJCAI'24, ICML'20, ICLR'20. I received the top reviewer award at ICLR'22, ICML'22, NeurIPS'22.
I am participating in the NSF-Simon's research collaboration on the Mathematics of Deep Learning (MoDL).
I co-organized the TTIC Student Workshop 2021, with Gene Li. We also organized a TTIC/Uchicago student theory seminar in Spring 2021. If you'd like to take over and re-start this series, please let me know.
I was a Teaching Assistant for the Convex Optmization course at TTIC during Winter'22'24 and a co-organizer for the Research at TTIC Colloquium for Fall-Winter 2021.
I participated in the Machine Learning Summer School at Tübingen, Germany during summer 2020.