11/2023: My PhD student, Youbang Sun, was awarded the 2023 John and Katharine Cipolla PhD Merit Award!
10/2023: Paper on safe distributed online optimization for convex/non-convex problems accepted in TMLR. [link]
10/2023: Paper on the local linear rate of consensus on the Stiefel manifold accepted in IEEE TAC. [link]
09/2023: Paper on provably fast convergence of independent natural policy gradient for Markov potential games accepted in NeurIPS 23
07/2023: Paper on distributed online LQR for unknown systems accepted in IEEE TAC. [link]
07/2023: Paper on real-time density tracking using kernel density estimation accepted in IEEE TPAMI. [link]
06/2023: NSF Award on “Consensus and Distributed Optimization in Non-Convex Environments with Applications to Networked Machine Learning”! [link]
04/2023: Paper on sparse expansion of deep Gaussian processes accepted in IISE Transactions. [link]

04/2023: My PhD student, Ting-Jui Chang, was awarded the 2023 Yamamura Research Award! 

01/2023: Paper on stability analysis of open federated learning accepted in ACC 23. [link]
12/2022: Paper on convergence analysis of centralized and distributed mirror descent with QC approach accepted in IEEE TAC. [link]
11/2022: My PhD student, Yinsong Wang, was awarded the 2022 John and Katharine Cipolla PhD Merit Award! 
10/2022: Paper on random-feature based Newton method accepted in IEEE TSP. [link]
08/2022: Paper on dynamic Gaussian distribution tracking accepted in DDDAS 22. 
07/2022: Paper on distributed online system identification accepted in CDC 22. [link]
05/2022: Paper on generalized sliced probability metrics won the Best Paper Award in ICASSP 22. [link][Twitter]
04/2022: Paper on optimal recovery for learning from non-random data accepted in Sampling Theory, Signal Processing, and Data Analysis. [link]
10/2021: Paper on optimal randomized canonical correlation analysis accepted in IEEE TNNLS. [link]
07/2021: Paper on the linear convergence of distributed mirror descent accepted in CDC 21. [link]
07/2021: I joined the MIE department at Northeastern University!
07/2021: I will be on the program committee of “International Workshop on Federated Learning for User Privacy and Data Confidentiality” in ICML 21. [link]
06/2021: Paper on detection of driver distractions using multi-modal data accepted in IEEE THMS. [link]
05/2021: Paper on decentralized Riemannian gradient descent on the Stiefel manifold accepted in ICML 21. [link]
05/2021: Invited talk (virtual) in the Wireless Systems Lab at Stanford University.
03/2021: Invited talk (virtual) at CISS 21.
01/2021: Paper on distributed online LQR accepted in ACC 21. [link]
01/2021: Paper on distributed non-convex optimization accepted in IEEE TAC. [link]
12/2020: Paper on dynamic regret analysis of strongly convex, smooth online optimization accepted in AAAI 21. [link]
11/2020: Paper on asymptotic convergence of distributed mirror descent with integral feedback accepted in IEEE L-CSS. [link]
10/2020: Invited talk (virtual) in IE department at University of Pittsburgh.
10/2020: Invited talk (virtual) at Mitsubishi Electric Research Laboratories (MERL).
09/2020: Paper on statistical and topological properties of sliced probability divergences accepted in NeurIPS 20 (Spotlight). [link]
08/2020: Elevated to IEEE Senior Member.
08/2020: Two papers accepted in Asilomar 20.
08/2020: NSF Award on “Real-Time Learning and Control of Stochastic Nanostructure Growth Processes Through in situ Dynamic Imaging”! [link]
07/2020: I will be on the program committee of “International Workshop on Federated Learning for User Privacy and Data Confidentiality” in ICML 20.[link]
06/2020: Paper on generalization bounds for entropic optimal features accepted in ICML 20. [link]
02/2020: Invited talk at ITA 20 Workshop.
01/2020: Paper on distributed parameter estimation in randomized shallow networks accepted in ACC 20. [link]
12/2019: Congrats to my student, Yinsong Wang, for earning the 1st place in poster session for ISEN’s graduate students. He presented our work on general scoring rules for sampling random features. [link]
12/2019: Paper on online mechanism for resource allocation in networks accepted in IEEE TCNS. [link]
10/2019: Chair of the session “limits of large-scale statistical learning” at INFORMS 19.
10/2019: Paper on Byzantine-resilient distributed state estimation accepted in IEEE TAC. [link]
09/2019: “NSF Awards $1.5 Million TRIPODS Institute to Texas A&M to Bolster Data-Driven Discovery”. Excited to be part of this team! [link]
08/2019: NSF Award on “Collaborative Online Optimization for Efficient Model-Based Learning” ! [link]
05/2019: Lili Su presented a poster about our work on Byzantine-resilient distributed state estimation in the inaugural L4DC workshop.
05/2019: Award from Texas A&M Institute of Data Science! [link]
04/2019: Presented a talk on data-dependent kernel approximation in the Model Reduction Workshop at TAMU.
03/2019: Presented a talk on “generalization bounds for learning from batch and streaming data” in the ECE, CS, and STAT departments at TAMU.
02/2019: Invited talk at ITA 19 Workshop.
01/2019: Paper on distributed network localization accepted in ACC 19. [link]
01/2019: Our work on kernel approximation featured in Texas A&M Today. [link]
12/2018: Our project on “trade-offs between approximation and generalization in learning systems” is funded by Texas A&M Triads for Transformation!
11/2018: Invited talk at INFORMS 18 Annual Meeting.
09/2018: Paper on data-dependent random features accepted in CDC 18. [link]
09/2018: Paper on learning bounds for greedy approximation accepted in NeurIPS 18. [link]