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Dr. Manas Gaur

Dr. Manas Gaur

Assistant Professor, Computer Science & Engineering (CSE)

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About

Dr. Manas Gaur is an Assistant Professor in the Department of Computer Science at the University of Maryland, Baltimore County (UMBC), where he leads the Knowledge-infused AI and Inference Lab (KAI²-Lab). His work focuses on advancing Knowledge-infused Learning, Artificial Intelligence, Natural Language Processing, Knowledge Graphs, Conversational AI, and Human-AI Collaboration, with a significant emphasis on applications in healthcare and crisis informatics.

Dr. Gaur earned his Ph.D. from the Artificial Intelligence Institute at the University of South Carolina in 2022, under the mentorship of Dr. Amit P. Sheth. His dissertation, titled Knowledge-infused Learning, explores novel approaches to integrating domain knowledge into AI models. He also pursued doctoral studies at Wright State University and SUNY Albany before transferring to South Carolina. His academic foundation includes an M.Tech in Software Engineering from Delhi Technological University (2015), where he worked on multiobjective optimization, and a B.Tech in Computer Science from Netaji Subhas University of Technology (2013), with research on mobile ad hoc networks and AI.

Throughout his career, Dr. Gaur has made impactful contributions to academia and research. He has been recognized with prestigious awards, including the AAAI New Faculty Award (2023), a Dissertation Research Award from Samsung Research America, and the EPSRC-UKRI Grant in collaboration with the Alan Turing Institute for research in mental health. Additionally, he was honored with multiple travel awards and fellowships, such as the Dataminr AI for Social Good Fellowship and the Eric and Wendy Schmidt Data Science Fellowship.

Dr. Gaurs professional journey includes pivotal roles as an NLP Research Lead at Samsung Research America and a Visiting Researcher at the Alan Turing Institute. His research outputs are well-regarded for their real-world impact, particularly in building trustworthy AI systems.

Experience
  • 4+ Years
Qualification
  • Ph.D                
  • M.Tech                
  • B.Tech                
     
Award & Recognition

  • "AAAI New Faculty, 2023"
  • University of South Carolina Eminent Doctoral Profiles, 2022                                                        
  • Dissertation Research Award, Samsung Research America ($15000), 2022                                                        
  • Microsoft Travel Award for AAAI ($700), 2020                                                        
  • University of South Carolina Travel Award for AAAI ($500), 2020                                                        
     

Research

Research                                                        

  • "Guest Editor of ACM Transactions of Computing for Healths Special Issue on Large Language
  • Models, Conversational Systems, and Generative AI in Health."                                                        
  • Personal Knowledge Graphs: Methodology, Tools, and Applications, IET                                                        
  • Knowledge-infused Learning: Knowledge-powered NeuroSymbolic AI for Explainability, Interpretability, and Safety, Cambridge University Press                                                        

Publication                                                        

  • "TF-SDG 2024 IoT-Based Preventive Mental Health Using Knowledge Graphs and Standards for Better Well-Being"                                                        
  • JBHI 2024 A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression                                                        
  • "FAI 2023 A review of the explainability and safety of conversational agents for mental health to identify avenues for improvement"                                                        
  • "FBD 2023 Proknow: Process knowledge for safety constrained and explainable question generation for mental health diagnostic assistance"                                                        
  • IoT-eHealth 2022 Reasoning Over Personalized Healthcare Knowledge Graph: A Case Study of Patients with Allergies and Symptoms