Dr. Manas Gaur
Assistant Professor · Tenure-Track
Department of Computer Science and Electrical Engineering
College of Engineering and Information Technology
He/Him/His/Himself
About
Manas Gaur is an Assistant Professor of Computer Science at UMBC and Director of the KAI² Lab, where he pursues use-inspired basic research at the intersection of neurosymbolic AI, mechanistic interpretability, and clinical natural language processing. His overarching mission is to render large language models trustworthy for high-stakes applications, particularly in mental health and clinical decision support. Gaur holds the prestigious Ramanujan Fellowship from the Anusandhan National Research Foundation, recognizing his standing among early-career researchers in artificial intelligence. He serves as an Advisor AI Research Scientist for NeuralNest and Aidvance. He brings industry-academic experience from Samsung Research America and Dataminr, combined with sustained clinical partnerships spanning the Maryland Psychiatric Research Center and university-embedded clinical psychology. His research traces a coherent arc from foundational work in social media mining for mental health, through the development of Knowledge-Infused Learning as a neurosymbolic paradigm, to mechanistic interpretability and grounding fidelity for clinical AI systems. His innovation portfolio includes three patents that define core algorithmic advances for high-stakes applications: AQGPT, ISEEQ, and Virtual Court Room, each anchoring trustworthy AI deployment in clinical and legal contexts. Gaur is deeply committed to early-stage researcher development and academic leadership. Through mentorship of undergraduates and high school students to first-author publication, he has seeded cumulative research impact now flowing through Carnegie Mellon, Purdue, and leading technology companies. His current research frames Knowledge-Infused Neurosymbolic AI around three scientific pillars, Interpret, Ground, and Control, positioning mechanistic reasoning as the foundation for trustworthy clinical AI.
Research interests
Neurosymbolic AI, Knowledge-Infused Learning, Mechanistic Interpretability, Sparse Autoencoders and Circuit Analysis, Long-Form Reasoning and Attribution, Retrieval-Augmented Generation, Grounding Fidelity in LLMs, Clinical Natural Language Processing, Trustworthy AI for High-Stakes Applications (Cybersecurity, Scientific Discovery, Mental Health, Legal), Adversarial Robustness
Teaching interests
Neurosymbolic AI Systems, Knowledge-infused Learning, Machine Learning, Semantic Mechanistic Interpretability, Trustworthy AI, Knowledge Graph Representation and AI Reasoning, Large Language Models, Natural Language Processing
Education
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Ph D, Computer Science
— University of South Carolina (2022) Knowledge-infused Learning
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MS, Software Engineering
— Delhi Technological University (Formerly Delhi College of Engineering) (2015) BIOGEOGRAPHY BASED OPTIMIZATION FOR COMPLEX SYSTEM
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BS, Computer Science
— Netaji Subhas University of Technology (East Campus) (Formerly Ambedkar Institute of Technology) (2013) Meticulous study of firewall using security detection tools
Publications
Presentations
- Process Knowledge-infused AI 2022
- Towards Effective Paraphrasing for Information Disguise 2022
- Towards Process Knowledge-infused Learning for Explainable Mental Healthcare 2022
Grants and Contracts
- Collaborative Research: SCH: Towards Observable and Instructible AI Agents for Supporting Mental Health Professionals
- Collaborative Research: VINES: Track 1: NSF-JST: Neuro-symbolic AI-Native Design of Semantic Communications
- SparqSSM: A Lightweight Hybrid State Space Framework for Fast SPARQL Query Generation and Conversation on Personal Lifelong Knowledge Graphs
Research in Progress
- Human-Centered AI Advising Systems 2025
Patents and Intellectual Property
- AQ-GPT: Custom and Compact Foundation Model for Personalized Air Quality Advisories Small App, Personalized Advisor on the go 2025
- Dynamic question generation for information-gathering 2022
Courses Taught
- Spec Topics In Comp Science : Knowledge-powered NeuroSymbolic AI for Explainability, Interpretability, and Safety
- Master's Thesis Research
- Independent Study in Computer Science
- Independent Study Computer Science
- Introduction to Machine Learning
- Intro Machine Learning