Research Interests
Computer Vision, Machine (Deep) learning and Artificial Intelligence.
Current Research Focus
Probabilistic Graphical Models, tractable probabilistic modeling, integrating probabilistic models and deep learning models, neuro-symbolic AI, explainable AI and combinatorial optimization.
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
