📢 Dr. Arya is recruiting motivated Ph.D. students (Fall 2026 & Spring 2027) as well as undergraduate and master’s students interested in research in AI and machine learning. For more details, please see the hiring page.
Biography
Shivvrat Arya is an Assistant Professor of Computer Science at the Ying Wu College of Computing, New Jersey Institute of Technology (NJIT), Director of the ARIA Lab, and a Core Faculty member of the Center for AI Research. His research develops structured, interpretable, and efficient AI methods that integrate learning, reasoning, and optimization, with a focus on probabilistic and neurosymbolic reasoning, neural combinatorial optimization, and structured vision and multimodal systems.
He received his Ph.D. in Computer Science from The University of Texas at Dallas, where he was advised by Vibhav Gogate and Yu Xiang. His doctoral research focused on learning-based methods for probabilistic inference and structured reasoning.
For current projects and research activity, visit the ARIA Lab website.
Research Focus
- Neurosymbolic AI & Explainable Systems: Integrating symbolic structure with deep learning for transparent reasoning. We develop hybrid architectures that combine neural pattern recognition with logical inference for interpretable decision-making.
- Neural Combinatorial Optimization: Learning-based solvers for combinatorial and constrained problems. Our work explores neural architectures that learn to solve NP-hard optimization problems efficiently.
- Deep Reinforcement Learning for Graph Optimization: Graph neural networks combined with reinforcement learning for solving complex graph-based optimization problems, including routing, scheduling, and resource allocation.
- Applications of Neurosymbolic Methods: Computer vision, video understanding, activity recognition, human-computer interaction, and multimodal reasoning. Applying neurosymbolic AI to real-world tasks requiring structured understanding.
Research Highlights
- Publications recognized with best paper awards, spotlights, and oral presentations at top AI/ML venues
- Developed NeuPI, a neural inference engine that accelerates probabilistic reasoning from minutes to microseconds
- Built real-time AR guidance systems for complex physical tasks
- Released CaptainCook4D, an egocentric 4D dataset for procedural task understanding
For earlier externally funded research contributions, including work supported through DARPA, NSF, and AFOSR projects, see Funded Projects.
Education
- Ph.D., Computer Science – The University of Texas at Dallas
- M.S., Computer Science – The University of Texas at Dallas
- B.Tech., Computer Science and Engineering – IIIT Vadodara
Our paper, "Learning to Condition: A Neural Heuristic for Scalable MPE Inference," has been accepted for publication in The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025) as a Poster Presentation.
Our paper, “RELINK: Edge Activation for Closed Network Influence Maximization via Deep Reinforcement Learning,” has been accepted for publication in the Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM 2025).
Dr. Arya has joined NJIT as an Assistant Professor in the Department of Computer Science at the Ying Wu College of Computing.