Miao Liu (刘淼)
Staff Research Scientist, IBM Research
IBM Thomas J. Watson Research Center, Yorktown Heights, NY, USA
Contact: miao.liu.duke at gmail.com, miao.liu1 at ibm.com
I am a Staff Research Scientist at IBM T. J. Watson Research Center, Yorktown Heights NY. My research focuses on developing novel machine learning methods for building AI foundations, with applications in task-oriented dialog systems, financial trading, resource management for system on chip and cloud computing, etc. Prior to joining IBM, I was a Postdoctoral Associate in the Laboratory of Information and Decision System (LIDS) at Massachusetts Institute of Technology (MIT), where I was working on scalable Bayesian nonparametric methods for solving multi-robot learning and autonomous driving related problems. I received my Ph.D. degree in Electrical and Computer Engineering from Duke University, and received both my B.S. and M.S. degrees in Electronics and Information Engineering from Huazhong University of Science and Technology (HUST), China. My research interests include statistical machine learning, reinforcement learning, multiagent systems and foundational models.
I am a Staff Research Scientist at IBM T. J. Watson Research Center, Yorktown Heights NY. My research focuses on developing novel machine learning methods for building AI foundations, with applications in task-oriented dialog systems, financial trading, resource management for system on chip and cloud computing, etc. Prior to joining IBM, I was a Postdoctoral Associate in the Laboratory of Information and Decision System (LIDS) at Massachusetts Institute of Technology (MIT), where I was working on scalable Bayesian nonparametric methods for solving multi-robot learning and autonomous driving related problems. I received my Ph.D. degree in Electrical and Computer Engineering from Duke University, and received both my B.S. and M.S. degrees in Electronics and Information Engineering from Huazhong University of Science and Technology (HUST), China. My research interests include statistical machine learning, reinforcement learning, multiagent systems and foundational models.