ARIA Lab

The ARIA Research Lab in the Department of Computer Science at the Ying Wu College of Computing, New Jersey Institute of Technology (NJIT), led by Dr. Shivvrat Arya, develops methods for trustworthy, structured, and efficient AI. Our research lies at the intersection of neurosymbolic AI and probabilistic reasoning, with applications in computer vision, video understanding, human–AI interaction, multimodal learning, and reasoning with large language models. In parallel, we advance combinatorial optimization, with a focus on neural and learning-based solvers.

What we work on

• Neurosymbolic AI and Explainable Systems Methods that integrate symbolic structure such as logic, constraints, and graphs with deep learning to enable transparent, controllable reasoning.

• Probabilistic Modeling and Inference Tractable and approximate inference in rich probabilistic models, including neural inference engines capable of answering complex queries at scale.

• Neural Combinatorial and Constrained Optimization Learning-based solvers for large-scale combinatorial and constrained reasoning tasks.

• Reinforcement Learning for Graph Optimization RL and graph neural network approaches for routing, scheduling, and graph-structured decision-making.

• Applications in Vision, Video, and HCI Structured and neurosymbolic methods for video understanding, activity recognition, augmented reality task guidance, and human–AI collaboration.

Joining the lab

We are always looking for curious, rigorous, and collaborative students who are excited about building the next generation of structured, explainable, and reliable AI systems.

• Ph.D. students: Opportunities to work on core problems in neurosymbolic AI, probabilistic inference, graph optimization, and structured deep learning.
• M.S. and undergraduate students: Positions for research-oriented students interested in gaining hands-on experience with modern AI methods, systems building, and publications.

If you are interested in joining the ARIA Research Lab, please read the hiring page and follow the instructions there. Briefly describe your background, relevant coursework or projects, and which of the lab’s research directions you are most excited about.

Selected projects & highlights

• NeuPI – Neural Inference Engine
  A neural engine for probabilistic inference that accelerates reasoning in graphical models from minutes to microseconds, enabling real-time decision-making in structured domains.

• CaptainCook4D
  A large-scale egocentric 4D dataset for procedural task understanding, used to study how AI systems perceive and reason about long-horizon activities in realistic, cluttered environments.

• Explainable activity recognition & AR task guidance
  Models that not only recognize what people are doing but also provide structured, interpretable explanations and real-time guidance in augmented reality for complex tasks.

• Award-winning work at top venues
  Lab publications have received best paper awards, spotlights, and oral presentations at venues such as NeurIPS, AISTATS, AAAI, and UAI.