Songtao Lu

Senior Research Scientist Mathematics and Theoretical Computer Science Department Thomas J. Watson Research Center & MIT-IBM Watson AI Lab IBM Research, Yorktown Heights, New York 10598, USA E-mail: songtaoibm@gmail.com Google Scholar

About me

I am a senior research scientist at the Thomas J. Watson Research Center, an IBM principal investigator at the MIT-IBM Watson AI Lab, and an IBM PI of the RPI-IBM AI Research Collaboration program. Also, I serve as senior personnel at the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE)

Previously, I was an AI resident and then a research scientist with the Mathematics and Theoretical Computer Science Department at the Thomas J. Watson Research Center. Before that, I was a postdoc associate with the Department of Electrical and Computer Engineering at the University of Minnesota Twin Cities working with Mingyi Hong. I received my Ph.D. from the Department of Electrical and Computer Engineering at Iowa State University advised by Mingyi Hong and Zhengdao Wang.

Recent Representative Works

• SLM: A Smoothed First-Order Lagrangian Method for Structured Constrained Nonconvex Optimization
  Songtao Lu
  NeurIPS, 2023.

• Bilevel Optimization with Coupled Decision-Dependent Distributions
  Songtao Lu
  ICML, 2023.

• A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization
  Songtao Lu, Siliang Zeng, Xiaodong Cui, Mark S. Squillante, Lior Horesh, Brian Kingsbury, Jia Liu, Mingyi Hong
  NeurIPS, 2022.

• A Single-Loop Gradient Descent and Perturbed Ascent Algorithm for Nonconvex Functional Constrained Optimization
  Songtao Lu
  ICML, 2022.

• Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning
  Songtao Lu, Kaiqing Zhang, Tianyi Chen, Tamer Basar, and Lior Horesh
  AAAI, 2021.

• Linearized ADMM Converges to Second-Order Stationary Points for Non-Convex Problems
  Songtao Lu, Jason Lee, Meisam Razaviyayn, Mingyi Hong
  IEEE Transactions on Signal Processing, 2021.

• Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems
  Songtao Lu, Meisam Razaviyayn, Bo Yang, Kejun Huang, Mingyi Hong
  NeurIPS, 2020.

• Hybrid Block Successive Approximation for One-Sided Non-Convex Min-Max Problems: Algorithms and Applications
  Songtao Lu, Ioannis Tsaknakis, Mingyi Hong, Yongxin Chen
  IEEE Transactions on Signal Processing, 2020.

• PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization
  Songtao Lu, Mingyi Hong, Zhengdao Wang
  ICML, 2019.

Recent news

• 04/03/2024 Our work on "Toward Byzantine-robust decentralized federated learning" is accepted by ACM CCS 2024.

• 03/27/2024 I will serve as an Area Chair for NeurIPS 2024.

• 01/16/2024 Our work on min-max optimization for policy evaluation with nonlinear function approximation is selected as a spotlight presentation by ICLR 2024.

• 12/13/2023 I am thrilled to have received an IBM Research Accomplishment Award for my contributions to advancing optimization techniques for next-generation distributed intelligence.

• 12/13/2023 Four papers were accepted by ICASSP 2024.

• 11/02/2023 It is my great pleasure to have received an IBM Plateau Invention Achievement Award.

• 08/21/2023 I have been elevated as an IEEE Senior Member.

• 07/03/2023 I am truly honored to receive an IBM Entrepreneur Award.

• 12/02/2022 Our work on conditional moment alignment for improved generalization in federated learning received the FL-NeurIPS Outstanding Paper Award!

• 07/29/2022 Our work on distributed adversarial training to robustify deep neural networks at scale received the UAI 2022 Best Paper Runner-Up Award!