Guannan Liang

I am interested in machine learning, stochastic optimization, recommender system and differential privacy

About me

As an Applied Scientist at Amazon, I am dedicated to developing cutting-edge machine learning and artificial intelligence solutions to tackle complex challenges. My expertise lies in leveraging advanced ML/AI techniques such as Transformers and continual learning to safeguard Amazon’s customers from fraudulent activities and ensure the integrity of their accounts.

I obtained my Ph.D. degree in Computer Science from University at Connecticut, UCONN in May 2021, under the supervision of Prof. Jinbo Bi, M.S. in Statistics from University of California, Davis and B.S. in Mathematics from Zhengzhou University. My research interests include Machine Learning, Mathematical Optimization, Statistics, Deep Learning, Differential Privacy, Federated Learning and Recommender System. My goal is to develop efficient and privacy-preserving optimization algorithms for machine learning, deep learning and federated learning.

Research interests

Research areas

News

(May 2022) Reviewer for NeurIPS 2022
(Jan 2022) One paper is accepted by Neurocomputing
(Jan 2022) Reviewer for ICML 2022
(Oct 2021) Invited to serve as a PC for IJCAI 2022
(Sep 2021) Reviewer for ICLR 2022
(Aug 2021) One paper is accepted by RecSys 2021
(May 2021) Reviewer for ACM Transactions on Intelligent Systems and Technology
(May 2021) Reviewer for NeurIPS 2021
(May 2021) Volunteer for ICLR 2021
(April 2021) Technical Presentation at School of Artificial Intelligence, Jilin University, China
(April 2021) Ph.D. thesis defense
(Dec 2020) Two papers are accpected by AAAI 2021
(Oct 2020) Invited to serve as a PC member for IJCAI 2021
(Oct 2020) Invited to serve as a reviewer for AAAI 2021

Courses

(Oct 2021 - Dec 2021) I am studying Reinforcement Learning via Deep RL Bootcamp

Contact

Email: guannan.liang AT uconn.edu