TIANYI CHEN
About me
Prior to joining RPI, I received my Ph.D from the University of Minnesota under the supervision of Prof. Georgios B. Giannakis.
My background is in Signal Processing and Networking. My current research focuses on the theory and applications of Optimization, Machine Learning, and Reinforcement Learning.
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
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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
Recent news
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:
Jan 2021: My Ph.D dissertation was recognized as the inaugural IEEE SPS Best PhD Dissertation Award:
Dec 2020: New paper on linear speedup analysis of the popular A3C algorithm in RL:
Dec 2020: Our paper was accepted in AAAI 2021:
Dec 2020: Our paper was accepted in NeurIPS 2020 workshop on Federated Learning:
Sept 2020: Our paper was accepted in IEEE Trans on Pattern Analysis and Machine Intelligence:
Sept 2020: New paper on Byzantine-robust decentralized TD:
Aug 2020: New paper on simple stochastic algorithm for learning with compositional structures (e.g., meta learning and RL):
Aug 2020: I am invited to serve as a program committee member for ICLR 2021.
Jul 2020: New paper presented in ICML Workshop on Federated Learning for User Privacy and Data Confidentiality 2020:
May 2020: I am invited to serve as a program committee member for NeurIPS 2020.
May 2020: New paper on Byzantine-robust federated learning has been accepted in IEEE Transactions on Signal Processing:
Mar 2020: I am organizing the special session ``Reinforcement learning and bandits for communication systems'' at Asilomar 2020.
Feb 2020: I am invited to serve as a program committee member for ICML 2020.
Feb 2020: New paper on Lazily Aggregated Stochastic Gradients has been submitted to IEEE Transactions on Signal Processing:
Feb 2020: New paper on adaptive temporal-difference learning and [Python code]:
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:
Sept 2019: New paper on finite-sample analysis of decentralized learning with Markov sampling:
Sept 2019: Our paper was accepted at NeurIPS 2019:
Aug 2019: I am excited to join Department of Electrical, Computer, and Systems Engineering at Rensselaer Polytechnic Institute.
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