Tianyi Chen

Tianyi Chen Assistant Professor Department of Electrical, Computer, and Systems Engineering Rensselaer Polytechnic Institute Office: Room 6036, Jonsson Engineering Center Address: 110 8th Street, Troy, New York, 12180-3590 Tel: 518-276-3173 E-mail: chent18 [@] rpi.edu;  chentianyi19 [@] gmail.com (preferred) Google Scholar ResearchGate

About me

Tianyi Chen has been with Rensselaer Polytechnic Institute (RPI) as an assistant professor since August 2019. In RPI, I am also an active member in the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC). Prior to joining RPI, he received the doctoral degree from the University of Minnesota under the supervision of Prof. Georgios B. Giannakis.

Dr. Chen is the inaugural recipient of IEEE Signal Processing Society Best PhD Dissertation Award in 2020 and a recipient of NSF CAREER Award in 2021. He is also a co-author of the Best Student Paper Award at the NeurIPS Federated Learning Workshop in 2020 and at IEEE ICASSP in 2021.

Research interests: theoretical and algorithmic foundations of optimization, machine learning, and statistical signal processing, with applications in networked systems such as wireless and energy systems.

Notice: I am always looking for self-motivated students and visitors working in the areas of machine learning, signal processing, optimization, and wireless networks. Please drop me an email if you are interested.

Education

• University of Minnesota, Twin Cities, USA: Ph.D, Electrical and Computer Engineering, 2014.9 - 2019.6

• University of Minnesota, Twin Cities, USA: M.S., Electrical and Computer Engineering, 2014.9 - 2016.6

• Fudan University, China: B.S., Communication Science and Engineering, 2010.9 - 2014.7

Press coverage

• Intelligence-Sharing Tools Will Enable Smarter Devices

  Rensselaer News, June 22, 2021

• Alumnus Tianyi Chen Receives IEEE SPS Best Dissertation Award

  UMN ECE News - Feature stories, December 18, 2020

• Experts Join Rensselaer-IBM Artificial Intelligence Research Collaboration

  Rensselaer News, January 22, 2020

Recent news

• Sept 2021: Two papers have been accepted to NeurIPS 2021 with one being selected as a spotlight presentation:

  Tighter Analysis of Alternating Stochastic Gradient Method for Stochastic Nested Problems (spotlight).
  CAFE: Catastrophic Data Leakage in Vertical Federated Learning (poster).

• Sept 2021: Our paper was accepted in IEEE Transactions on Trans on Pattern Analysis and Machine Intelligence:

  Adaptive Temporal Difference Learning with Linear Function Approximation.

• Sep 2021: I am invited to the Panel Session of the "Distributed ML workshop'' co-located with ACM CoNEXT 2021.

• Sep 2021: With Derya Malak, we are organizing a workshop on "Wireless Distributed Computing and Learning'' at WiOPT 2021.

• Aug 2021: I am excited to collaborate with Rongjie Lai (RPI) and Jie Chen (MIT-IBM AI Lab) on the NSF SCALE MoDL Program.

• Jul 2021: Our paper was accepted in IEEE Transactions on Signal Processing:

  LASG: Lazily Aggregated Stochastic Gradients for Communication-Efficient Distributed Learning.

• Jun 2021: Our paper was recognized as the Outstanding Student Paper Award for ICASSP 2021:

  A Stochastic Compositional Optimization Method with Applications to Meta Learning. [UCLA Math News]

• Jun 2021: Two papers were accepted in IEEE Transactions on Signal Processing:

  Solving Stochastic Compositional Optimization is Nearly As Easy As Solving Stochastic Optimization.
  Byzantine-Resilient Decentralized TD Learning with Linear Function Approximation.

• May 2021: I am honored to receive the NSF CAREER Award on:

  The co-design of distributed machine learning algorithms and wireless systems. [RPI News]

• Apr 2021: Our paper was accepted in IEEE Transactions on Control of Network systems:

  Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning.

• Feb 2021: I am organizing the special session ``Reinforcement learning over networks'' at Asilomar 2021.

• Feb 2021: New paper on the first single-timescale SGD algorithms for stochastic bilevel optimization:

  A Single-Timescale Stochastic Bilevel Optimization Method.

• Jan 2021: Our papers were accepted in ICASSP 2021:

  A Stochastic Compositional Optimization Method with Applications to Meta Learning
  Byzantine-Resilient Decentralized TD Learning with Linear Function Approximation

• Jan 2021: Our paper was accepted in AISTATS 2021:

  CADA: Communication-Adaptive Distributed Adam.

• Jan 2021: My Ph.D dissertation was recognized as the inaugural IEEE SPS Best PhD Dissertation Award:

  Efficient Methods for Distributed Machine Learning and Resource Management in IoT. [UMN News] [RPI ECSE News] [RPI News]

• Dec 2020: New paper on linear speedup analysis of the popular A3C algorithm in RL:

  Asynchronous Advantage Actor Critic: Non-asymptotic Analysis and Linear Speedup.

• Dec 2020: Our paper was accepted in AAAI 2021:

  Decentralized policy gradient descent ascent for safe multi-agent reinforcement learning.

• Dec 2020: Our paper was recognized as the Best Student Paper Award at NeurIPS 2020 workshop on Federated Learning:

  Hybrid Federated Learning: Algorithms and Implementation. [UCLA Math News]

• Sept 2020: Our paper was accepted in IEEE Trans on Pattern Analysis and Machine Intelligence:

  Lazily Aggregated Quantized Gradient Innovation for Communication-Efficient Federated Learning.

• Aug 2020: New paper on simple stochastic algorithm for learning with compositional structures (e.g., meta learning and RL):

  Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization.

• May 2020: I am invited to serve as a program committee member for ICML 2020, NeurIPS 2020 and ICLR 2021.

• May 2020: New paper on Byzantine-robust federated learning has been accepted in IEEE Transactions on Signal Processing:

  Federated Variance-Reduced Stochastic Gradient Descent with Robustness to Byzantine Attacks.

• Mar 2020: I am organizing the special session ``Reinforcement learning for communication systems'' at Asilomar 2020.

• Feb 2020: New paper on adaptive temporal-difference learning and [Python code]:

  Adaptive Temporal Difference Learning with Linear Function Approximation.

• Jan 2020: Our paper on variance-reduced robust stochastic gradient descent has been accepted in ICASSP 2020.

• Jan 2020: I am invited to serve as a program committee member for ACM MobiHoc 2020.

• Dec 2019: Presented our work on Lazily Aggregated Quantized Gradients at NeurIPS 2019: [Paper] and [Poster]

• Oct 2019: Our paper has been accepted in IEEE Transactions on Signal Processing:

  Secure Mobile Edge Computing in IoT via Collaborative Online Learning.

• Sept 2019: Our paper was accepted at NeurIPS 2019:

  Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients. [Python code]

• Aug 2019: I am excited to join Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute.