Dr. Tien Pham

Dr. Tien Pham

U.S. Army Research Laboratory

Talk Title: AI/ML Essential Research Area – research and develop artificially intelligent agents that rapidly learn, adapt, reason and act in complex environments

 

 

 

Biography:

Dr. Tien Pham is the Senior Campaign Scientist (SCS) for the Information Sciences Campaign at the U.S. Army Research Laboratory (ARL), Adelphi, MD, USA.  He is responsible for the planning, direction, management, and oversight of very complex theoretical and applied R&D programs associated with sensing and effecting, system intelligence and intelligent systems, human and information interaction, network and communications, and cyber security. He serves as the scientific ambassador and advisor for information sciences to top-level administrative and technical management officials within the ARL, Army, DoD, other Government agencies, and outside organizations such as academia and industry. Dr. Pham also serves as the Coordinator for the Artificial Intelligence and Machine Learning (AI/ML) Essential Research Area (ERA) at ARL. He has over 25 years of R&D experience and 15 years of research project and program management experience in network & information sciences, data & information fusion and processing, networked sensing, multi-modal sensing and acoustics. Dr. Pham received his B.S., M.S and Ph.D. degrees in Electrical Engineering from the University of Maryland respectively in 1988, 1991 and 2006.

Abtstract:

The U.S. Army Research Laboratory’s (ARL) Essential Research Area (ERA) on Artificial Intelligence & Machine Learning (AI/ML) seeks to research, develop and employ a suite of AI-inspired and ML techniques and systems to assist teams of soldiers and autonomous agents in dynamic, uncertain, complex operational conditions. Systems will be robust, scalable, and capable of learning and acting with varying levels of autonomy, to become integral components of networked sensors, knowledge bases, autonomous agents, and human teams. Three specific research gaps will be examined: (i) Learning in Complex Data Environments, (ii) Resource-constrained AI Processing at the Point-of-Need and (iii) Generalizable & Predictable AI. The talk will also outline ARL’s current internal research efforts over the next 3-5 years, addressing the Chief of Staff of the Army (CSA) Modernization Priorities for Next Generation Combat Vehicles (NGCV) and Networks/C3I. Specifically, the research will focus on: (1) adversarial distributed machine learning, (2) robust inference, (3) adversarial reasoning integrating learned information, (4) adaptive online learning and (5) resource-constrained adaptive computing converging on AI for generating tactically-sensible estimates for decision making at the tactical edge.