Liang Ma is a Senior AI Research Scientist at Dataminr, New York, NY. Dr. Ma received the Ph.D. degree from the Department of Electrical and Electronic Engineering of Imperial College London in July, 2014. He received both the M.Sc. and B.Sc. degree with distinction from the Beijing University of Posts and Telecommunications (BUPT), China. 

His current work at Dataminr is focusing on NLP, LLM, prompt engineering and finetuning, sentence generations and classifications, NLP Quality Estimation (QE), hallucination mitigation, reinforcement learning (RL), deep neural networks, entity embedding, graph algorithms, distributed computing, and product-level AI model development and deployment per customer needs, e.g., high-speed alerting system at Dataminr.

Before joining Dataminr, he once worked as a research intern at NTT DoCoMo Beijing Labs, Ericsson (China) Communications, and Microsoft Research Asia (MSRA), where he was involved in WLAN Medium Access Control, High-speed Switching System, and Software Radio-Based Gigabit Multi-antenna Communications, respectively. After Ph.D. graduation, he then worked as a Research Staff Member at IBM T.J. Watson Research Center, where he led two projects with team members from Yale, UCSB, Northwestern, Penn State, Imperial College London, and UMASS-Amherst. The project objectives were to develop efficient resource management strategies via reinforcement learning, low-dimensional node sequence embedding, and efficient question answering in dynamic and multi-genre networks.

Dr. Ma has served as a peer reviewer in a range of conferences and journals, including ACL, ICLR, ACM TKDD, IEEE/ACM TON, INFOCOM, SECON, etc. He was the recipient of IEEE International Conference on Communications (ICC 2019) Best Paper Award on reinforcement learning approaches for resource management, IEEE International Conference on Distributed Computing System (ICDCS 2013) Best Paper Award, IBM Outstanding Technical Achievement Award, Chatschik Bisdikian Memorial Best Student Paper Award, ACM SIGCOMM Internet Measurement Conference (IMC 2013) Best Paper Award Finalist, and the winner of Outstanding Graduate Student 2008 and Excellent Student Awards four times during 2003-2006.