Bowen Li
Ph.D. PreliminarySep 05, 2025, 10:00 am - 11:30 am
LSC Room 380
Estimation Algorithms and Performance Bounds for Adaptive Sensing Problems
Abstract: The main contributions of this dissertation are threefold, focusing on estimation algorithms and performance bounds for adaptive sensing problems. First, we propose novel estimation algorithms that exploit sparsity to estimate time-varying channel parameters. Both theoretical analyses and simulation studies are conducted to demonstrate the effectiveness of the proposed algorithms. Second, we leverage the concept of submodularity - i.e., the diminishing returns property of discrete set functions - to quantify the performance of greedy algorithms in adaptive sensing tasks, such as multi-agent sensor coverage. We theoretically improve and generalize an existing performance bound for greedy algorithms established in a seminal work on submodular optimization. Third, we develop a computable bounding scheme for approximate dynamic programming (ADP) in stochastic optimal control, a fundamental framework underlying many adaptive sensing and sequential decision-making problems.
Adviser: Edwin Chong
Co-Adviser: Ali Pezeshki
Non-ECE Member: Ander Wilson, Statistics
Member 3: J. Rockey Luo, ECE
Addional Members: N/A
Co-Adviser: Ali Pezeshki
Non-ECE Member: Ander Wilson, Statistics
Member 3: J. Rockey Luo, ECE
Addional Members: N/A
Publications:
B. Van Over, B. Li, E. K. P. Chong and A. Pezeshki, “An improved greedy curvature bound for finite-horizon string optimization with application in a sensor coverage problem,” in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), Singapore, December 13-15, 2023, pp. 1257-1262.
B. Li, B. Van Over, E. K. P. Chong and A. Pezeshki, “On bounds for greedy schemes in string optimization based on greedy curvatures,” in Proceedings of the 63rd IEEE Conference on Decision and Control (CDC), Milan, Italy, December 16-19, 2024, pp. 5367-5372.
B. Li, S. Wu, E. E. Tripp, A. Pezeshki and V. Tarokh, “Recursive Least Squares with Minimax Concave Penalty Regularization for Adaptive System Identification,” IEEE Access, vol. 12, pp. 66993-67004, 2024.
B. Van Over*, B. Li*, E. K. P. Chong and A. Pezeshki, “A Performance Bound for the Greedy Algorithm in a Generalized Class of String Optimization,” IEEE Transactions on Automatic Control, 2025, to appear. (*contributed equally)
B. Van Over, B. Li, E. K. P. Chong and A. Pezeshki, “An improved greedy curvature bound for finite-horizon string optimization with application in a sensor coverage problem,” in Proceedings of the 62nd IEEE Conference on Decision and Control (CDC), Singapore, December 13-15, 2023, pp. 1257-1262.
B. Li, B. Van Over, E. K. P. Chong and A. Pezeshki, “On bounds for greedy schemes in string optimization based on greedy curvatures,” in Proceedings of the 63rd IEEE Conference on Decision and Control (CDC), Milan, Italy, December 16-19, 2024, pp. 5367-5372.
B. Li, S. Wu, E. E. Tripp, A. Pezeshki and V. Tarokh, “Recursive Least Squares with Minimax Concave Penalty Regularization for Adaptive System Identification,” IEEE Access, vol. 12, pp. 66993-67004, 2024.
B. Van Over*, B. Li*, E. K. P. Chong and A. Pezeshki, “A Performance Bound for the Greedy Algorithm in a Generalized Class of String Optimization,” IEEE Transactions on Automatic Control, 2025, to appear. (*contributed equally)
Program of Study:
ECE 514
ECE 516
ECE 556
ECE 656
ECE 614
CS 542
STAT 600
STAT 740
ECE 514
ECE 516
ECE 556
ECE 656
ECE 614
CS 542
STAT 600
STAT 740