معرفی
Kevin Tian is an Assistant Professor in the Department of Computer Science at the University of Texas at Austin. His research focuses on fundamental algorithmic problems in modern data science, particularly in continuous optimization and high-dimensional statistics. He also has broad interests in trustworthy machine learning, including robustness, privacy, and fairness.
Dr. Tian completed his Ph.D. in Computer Science at Stanford University from 2016-2022, where he was advised by Aaron Sidford. Prior to that, he earned his B.S. in Mathematics and Computer Science at MIT from 2012-2015. From 2022-2023, he was a Postdoctoral Researcher in the Machine Learning Foundations group at Microsoft Research.
Dr. Tian's research spans several areas of theoretical computer science and machine learning. His work primarily focuses on developing efficient algorithms for high-dimensional statistical problems, with particular emphasis on continuous optimization methods. He has made significant contributions to areas such as robust statistics, differential privacy, and graph algorithms. His research often bridges the gap between theoretical guarantees and practical applicability, developing algorithms that are both theoretically sound and practically efficient.
His recent publications demonstrate a strong focus on developing algorithms that address challenges in modern data science, including robustness against adversarial contamination, efficient methods for high-dimensional statistics, and privacy-preserving computation. His work frequently appears in top theoretical computer science conferences such as STOC, FOCS, and COLT, as well as leading machine learning venues like NeurIPS and ICML.
Dr. Tian has received numerous honors and awards for his research, including:
- 2021 Simons-Berkeley VMware Research Fellowship
- 2021 Google Ph.D. Fellowship
- 2019, 2020, 2021 Oral presentations at Neural Information Processing Systems
- 2018 SICOMP Special Issue invite, Foundations of Computer Science
- 2016 NSF Graduate Research Fellowship
- Arthur Samuel Award for Best Doctoral Thesis in Computer Science (2022)
Dr. Tian advises multiple Ph.D., M.S., and B.S. students at UT Austin, with research spanning theoretical computer science and machine learning. His current Ph.D. students include Anming Gu, Syamantak Kumar (co-advised with Purnamrita Sarkar), Chutong Yang, and Yusong Zhu (co-advised with Eric Price). His research has been generously funded by prestigious fellowships including the NSF Graduate Research Fellowship, Google Ph.D. Fellowship, and VMware Research Fellowship.
At UT Austin, Dr. Tian teaches courses including CS 331: Algorithms and Complexity and CS 395T: Continuous Algorithms. He is actively involved in the theoretical computer science community, serving on program committees for conferences such as COLT, STOC, and ICML.
