Research Interests
Computer Vision, Machine (Deep) learning and Artificial Intelligence.
Current Research Focus
Probabilistic Graphical Models, tractable probabilistic modeling, integrating probabilistic graphical models and deep learning for computer vision tasks, neuro-symbolic AI.
Inference in Probabilistic Models
Neural Network-Based Inference for Probabilistic Graphical Models
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
Optimization based Inference Schemes for Graphical Models
Deep Dependency Networks and Advanced Inference Schemes for Multi-Label Classification AISTATS’24
Activity Recognition and Video Understanding
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
