1 citations · 3 across the 13 of their papers we have counts for
13 papers
EMMA: Extracting Multiple physical parameters from Multimodal Data
Farhat Shaikh, Ayan Banerjee, Sandeep Gupta
We introduce EMMA, a physics-informed multimodal framework that recovers all identifiable dynamical parameters of a system directly from raw video, audio, and image-based time-seri…
Human Knowledge Integrated Multi-modal Learning for Single Source Domain Generalization
Ayan Banerjee, Kuntal Thakur, Sandeep Gupta
Generalizing image classification across domains remains challenging in critical tasks such as fundus image-based diabetic retinopathy (DR) grading and resting-state fMRI seizure o…
Hardware Acceleration for Neural Networks: A Comprehensive Survey
Bin Xu, Ayan Banerjee, Sandeep Gupta
Neural networks have become dominant computational workloads across cloud and edge platforms, but their rapid growth in model size and deployment diversity has exposed hardware bot…
XAI-MeD: Explainable Knowledge Guided Neuro-Symbolic Framework for Domain Generalization and Rare Class Detection in Medical Imaging
Midhat Urooj, Ayan Banerjee, Sandeep Gupta
Explainability domain generalization and rare class reliability are critical challenges in medical AI where deep models often fail under real world distribution shifts and exhibit…
Enabling Physical AI at the Edge: Hardware-Accelerated Recovery of System Dynamics
Bin Xu, Ayan Banerjee, Sandeep Gupta
Physical AI at the edge -- enabling autonomous systems to understand and predict real-world dynamics in real time -- requires hardware-efficient learning and inference. Model recov…
NEURO-GUARD: Neuro-Symbolic Generalization and Unbiased Adaptive Routing for Diagnostics -- Explainable Medical AI
Midhat Urooj, Ayan Banerjee, Sandeep Gupta
Accurate yet interpretable image-based diagnosis remains a central challenge in medical AI, particularly in settings characterized by limited data, subtle visual cues, and high-sta…
MedXAI: A Retrieval-Augmented and Self-Verifying Framework for Knowledge-Guided Medical Image Analysis
Midhat Urooj, Ayan Banerjee, Farhat Shaikh +2
Accurate and interpretable image-based diagnosis remains a fundamental challenge in medical AI, particularly under domain shifts and rare-class conditions. Deep learning models oft…
Hardware Software Optimizations for Fast Model Recovery on Reconfigurable Architectures
Bin Xu, Ayan Banerjee, Sandeep Gupta
Model Recovery (MR) is a core primitive for physical AI and real-time digital twins, but GPUs often execute MR inefficiently due to iterative dependencies, kernel-launch overheads,…
Single Domain Generalization in Diabetic Retinopathy: A Neuro-Symbolic Learning Approach
Midhat Urooj, Ayan Banerjee, Farhat Shaikh +2
Domain generalization remains a critical challenge in medical imaging, where models trained on single sources often fail under real-world distribution shifts. We propose KG-DG, a n…
Generating customized prompts for Zero-Shot Rare Event Medical Image Classification using LLM
Payal Kamboj, Ayan Banerjee, Bin Xu +1
Rare events, due to their infrequent occurrences, do not have much data, and hence deep learning techniques fail in estimating the distribution for such data. Open-vocabulary model…
Framework for developing and evaluating ethical collaboration between expert and machine
Ayan Banerjee, Payal Kamboj, Sandeep Gupta
Precision medicine is a promising approach for accessible disease diagnosis and personalized intervention planning in high-mortality diseases such as coronary artery disease (CAD),…
Detection of Unknown Errors in Human-Centered Systems
Aranyak Maity, Ayan Banerjee, Sandeep Gupta
Artificial Intelligence-enabled systems are increasingly being deployed in real-world safety-critical settings involving human participants. It is vital to ensure the safety of suc…
Detection of Unknown-Unknowns in Human-in-Plant Human-in-Loop Systems Using Physics Guided Process Models
Aranyak Maity, Ayan Banerjee, Sandeep Gupta
Unknown-unknowns are operational scenarios in systems that are not accounted for in the design and test phase. In such scenarios, the operational behavior of the Human-in-loop (HIL…