Research Interests
- Foundations & Methods — Machine (Deep) Learning, Neurosymbolic AI, Combinatorial Optimization, Probabilistic Modeling and Inference, Explainable and Interpretable Models
- Applications — Human–AI Interaction with Neurosymbolic and Deep Models, Computer Vision, Video Understanding, and Language Reasoning
Current Research Focus
Dr. Arya’s current research focuses on developing neurosymbolic models that integrate deep neural architectures with symbolic reasoning and probabilistic frameworks; advancing tractable probabilistic modeling to support efficient and scalable inference; creating neural network–based solvers for large-scale combinatorial optimization; and designing methods for explainable AI in structured and hybrid systems, with applications in computer vision, activity recognition, and multimodal learning.
Inference in Probabilistic Models
Neural Network-Based Inference
| Title | Venue |
|---|---|
| SINE - Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings | AISTATS'25 |
| A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models | NeurIPS'24-Spotlight UAI TPM'24-Best Paper Award |
| Learning to Solve the Constrained Most Probable Explanation Task | AISTATS'24 UAI TPM'24 |
| Neural Network Approximators for Marginal MAP Inference | AAAI'24-Oral |
Optimization-based Inference Schemes
| Title | Venue |
|---|---|
| Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification | AISTATS'24 |
Video Understanding and Activity Recognition
| Title | Venue |
|---|---|
| CaptainCook4D Dataset | NeurIPS'24 DMLR'23 |
| Explainable Activity Recognition | TiiS'23 |
| Predictive Task Guidance in Augmented Reality | IEEE VR'24 Poster |
Multi-Label Classification
| Title | Venue |
|---|---|
| Kernelized Random Vector Functional Link Network | IJCNN'20 |
Certificates & Awards
