Podium presentation at USCAP (Boston, USA). Mar 2025
Publications
European Journal of Cancer, Volume 220, 2025, 115390, ISSN 0959-8049
Digital poster presentation at AHA 2024
GraphPrint: Combining Traditional Fingerprint with Graph Neural Networks For Drug Target Prediction
Combining traditional molecular and protein features with 3D features from graph neural network for drug target affinity prediction.
Class-Incremental Continual Learning for General Purpose Healthcare Models
Building medical imaging AI that can learn new diseases without catastrophic forgetting on previous tasks, in different medical modalities, specialties, and hospitals.
Multi-Modal Deep Feature Integration for Alzheimer’s Disease Staging
Learning Alzheimer’s disease stage classification using multimodal data: MRI, PET, EHR, and Genomics data. Published at IEEE BIBM conference 2023
Autonomous Soft Tissue Retraction Using Demonstration-Guided Reinforcement Learning
Learning to control DaVinci surgical robot for soft tissue manipulation using expert demonstrations.
Multi-Modality Deep Learning Methods to Learn Alzheimer’s Disease Classification
Paper presentation at Suddath Symposium(Biomedical Informatics and AI for Biodiscovery and Healthcare) Georgia Institute of Technology. Mar 2023
Roadmap to Autonomous Surgery - A Framework to Surgical Autonomy
We break down the path of surgical automation into features required and current challenges and advances required to make this happen
We describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets.
Personalized Brain State Targeting via Reinforcement Learning
We propose a novel use of reinforcement learning as an active closed-loop assistive system that learns in real time to lead any brain state to a given goal state.
Deep learning based model to re-purpose drugs drugs to new diseases(Neurofibromatosis 2 Vestibular Schwannoma). Part of submission for Hack4Rare hacakathon by MIT.