10/2024: My PhD student, Emre Şahinoğlu, was awarded the 2024 John and Katharine Cipolla PhD Merit Award!
08/2024: Congratulations to Dr. Yinsong Wang for successfully defending his PhD! Yinsong will start a postdoc at Georgia Tech.
06/2024: “TAP: The Attention Patch for Cross-Modal Knowledge Transfer from Unlabeled Modality” accepted in TMLR. [link]
05/2024: “Linear Convergence of Independent Natural Policy Gradient in Games With Entropy Regularization” accepted in IEEE L-CSS. [link]
05/2024: Paper on decentralized nonsmooth, nonconvex stochastic optimization accepted in ICML 24. [link]
04/2024: I gave a virtual seminar on “Coordination, Optimization and Learning in Multi-Agent Networks” at Rutgers University ISE Seminar. [link]
04/2024: I gave a seminar on “Coordination, Optimization and Learning in Multi-Agent Networks” at University of Connecticut CSE Seminar.
03/2024: I gave a seminar on “Coordination, Optimization and Learning in Multi-Agent Networks” at Boston University CISE Seminar. [link]
03/2024: I gave a seminar on “Coordination, Optimization and Learning in Multi-Agent Networks” at Tufts ECE Colloquium.
11/2023: My PhD student, Youbang Sun, was awarded the 2023 John and Katharine Cipolla PhD Merit Award!
10/2023: “Dynamic Regret Analysis of Safe Distributed Online Optimization for Convex and Non-convex Problems” accepted in TMLR. [link]
10/2023: “On the Local Linear Rate of Consensus on the Stiefel Manifold” accepted in IEEE TAC. [link]
09/2023: “Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games” accepted in NeurIPS 23. [link]
07/2023: Paper on distributed online LQR for unknown systems accepted in IEEE TAC. [link]
07/2023: “TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic 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: “A Sparse Expansion for Deep Gaussian Processes” accepted in IISE Transactions. [link]
04/2023: My PhD student, Ting-Jui Chang, was awarded the 2023 Yamamura Research Award!
12/2022: “On Centralized and Distributed Mirror Descent: Convergence Analysis Using Quadratic Constraints” 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: “ORCCA: 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]