Research Interests
- Foundations & Methods — Machine (Deep) Learning, Probabilistic Modeling and Inference, Combinatorial Optimization, Neurosymbolic AI, Explainable and Interpretable Models
- Applications — Human–AI Interaction with Neuro-symbolic and Deep Models, Computer Vision, Video Understanding, and Language Reasoning
Current Research Focus
Dr. Arya’s current research focuses on developing neuro-symbolic 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
Neural Network Approximators for Marginal MAP Inference AAAI’24-Oral
Learning to Solve the Constrained Most Probable Explanation Task AISTATS’24 UAI TPM’24
A Neural Network Approach for Efficiently Answering Most Probable Explanation Queries in Probabilistic Models NeurIPS’24-Spotlight UAI TPM’24-Best Paper Award
SINE - Scalable MPE Inference for Probabilistic Graphical Models using Advanced Neural Embeddings AISTATS’25
Optimization-based Inference Schemes
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification AISTATS’24
Video Understanding and Activity Recognition
CaptainCook4D Dataset NeurIPS’24 DMLR’23
Explainable Activity Recognition TiiS’23
Predictive Task Guidance in Augmented Reality Poster at IEEE VR’24
Multi-Label Classification
Kernelized Random Vector Functional Link Network IJCNN’20
Certificates & Awards
