Projects per year
Personal profile
Personal profile
Dr. Keqin (Kevin) Liu (刘克勤) received his Ph.D. degree from the University of California at Davis in 2010 and joined the industry as a software engineer in 2012 after 2 years of postdoc experience. After 8 years of working in the Silicon Valley, Dr. Liu still has a lot passion in mathematics and decided to return to the academy in 2020 when working in ASML and subsequently taught in Nanjing University until joining XJTLU in 2024.
Research interests
Dr. Liu’s research interests include the development of modern math theory for AI technologies, the extension of pure math theory, especially number theory, and the application of pure math theory to solving practical problems in machine learning, e.g., multi-armed bandit in reinforcement learning.
Experience
Associate Professor, FAM Dept., XJTLU, China (2024.08-present)
Research Fellow and Lecturer, Math. Dept., NJU, China (2020.11-2024.07)
Sr. Software Engineer, ASML, Silicon Valley, USA (2019.02-2020.10)
Staff Software Engineer, KLA, Silicon Valley, USA (2016.03-2019.01)
CTO, Startup (Social E-Commerce), Silicon Valley, USA (2015.07-2016.03)
Sr. Software Engineer, MOOG, Silicon Valley, USA (2012.09-2015.07)
Postdoctoral Scholar and Lecturer, UC-Davis, USA (2010.10-2012.09)
Teaching
Fall 24/25:
MTH 205: Introduction to Statistical Methods
MTH 301: Final Year Project
Spring 23/24 (NJU):
Calculus II
Linear Algebra
Machine Learning: Mathematical Theory and Applications
Light of Science: AI and Mathematics
Light of Science: Human Civilization and Technology Development Boosted by Mathematics
Fall 23/24 (NJU):
Calculus I
Probability Theory and Statistics (2 parallel classes)
Spring 22/23 (NJU):
Machine Learning: Mathematical Theory and Applications
Light of Science: Human Civilization and Technology Development Boosted by Mathematics
Fall 22/23 (NJU):
Calculus I
Spring 21/22 (NJU):
Machine Learning: Mathematical Theory and Applications
Light of Science: Human Civilization and Technology Development Boosted by Mathematics
Operations Research
Fall 21/22 (NJU):
Probability Theory and Statistics
Spring 20/21 (NJU):
Stochastic Optimization
Summer 2011 (UC-Davis):
EEC150A: Signals and Systems I
Awards and honours
2024 First Prize for Excellent Paper Award, JAAS Annual Academic Conference, September 20-22, Nanjing, China.
2023 Zhi-Jian Award for Excellence in Teaching (mathematics), NJU, China
2021 Zi-Jin Talent Award (800,000 RMB fund), NJU, China
2021 Zheng-Gang Talent Award (300,000 RMB fund), NJU, China
2012 Zuhair A. Munir Award for Best Doctoral Dissertation, College of Engineering (selected from all the 7 departments), UC-Davis, USA
2011 Alien of Extraordinary Ability, USCIS, USA
External positions
Council Member, Jiangsu Association for Applied Statistics
Jul 2022 → Jul 2026
Research Fellow, Jiangsu National Center for Applied Mathematics
Nov 2020 → …
Person Types
- Staff
Fingerprint
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Collaborations and top research areas from the last five years
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Projects
- 2 Not started
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Study on single-loop full splitting algorithms for nonconvex, nonsmooth optimization with multiple composite structures
Tao, M. & Liu, K.
1/01/25 → 31/12/28
Project: Governmental Research Project
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High-Dimensional Reinforcement Learning by Multi-Armed Bandit
1/01/25 → 31/12/27
Project: Internal Research Project
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Equilibrium condition-based optimization models for network-wide travel time estimation using limited observed data
Cao, S., Lam, W. H. K., Liu, K., Shao, H., Tam, M. L. & Wu, T., 31 Jan 2024, In: Transportmetrica A: Transport Science. 2304033.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
Low-complexity algorithm for restless bandits with imperfect observations
Liu, K., Weber, R. & Zhang, C., 5 Sept 2024, In: Mathematical Methods of Operations Research.Research output: Contribution to journal › Article › peer-review
1 Citation (Scopus) -
Efficient reinforcement learning for reversi AI
Chen, H. & Liu, K., 2023, Second International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2022. Dai, W. & Jin, S. (eds.). SPIE, 125973Y. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 12597).Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review
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Temperature balancing, layer-wise weight analysis, and neural network training
Zhou, Y., Pang, T., Liu, K., Martin, C. H., Mahoney, M. W. & Yang, Y., 2023, In: Advances in Neural Information Processing Systems. 36Research output: Contribution to journal › Conference article › peer-review
2 Citations (Scopus) -
Frequentist multi-armed bandits for complex reward models
Chen, H., Deng, W., Liu, K. & Wu, T., 2022, 2021 International Conference on Statistics, Applied Mathematics, and Computing Science, CSAMCS 2021. Yin, H-M., Chen, K., Mestrovic, R. & Oliveira, T. A. (eds.). SPIE, 121631P. (Proceedings of SPIE - The International Society for Optical Engineering; vol. 12163).Research output: Chapter in Book or Report/Conference proceeding › Conference Proceeding › peer-review
Activities
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High-Dimensional AI with Multi-Armed Bandits
Keqin Liu (Speaker)
26 Nov 2024Activity: Talk or presentation › Invited talk
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UCB Learning of MAB with Heavy Tails
Keqin Liu (Speaker)
21 Sept 2024Activity: Talk or presentation › Presentation at conference/workshop/seminar
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CSIAM-The 4th Forum on Mathematics Promoting Enterprise Innovation and Development
Keqin Liu (Participant)
24 May 2024 → 26 May 2024Activity: Participating in or organising an event › Participating in an event e.g. a conference, workshop, …
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Guideline Drafting for Sino-Russian Mathematical Challenge Fund (Second Round)
Keqin Liu (Participant)
28 Mar 2024 → 29 Mar 2024Activity: Participating in or organising an event › Participating in an event e.g. a conference, workshop, …