Publications

GraphPrint: Combining Traditional Fingerprint with Graph Neural Networks For Drug Target Prediction

less than 1 minute read

Published in NEURIPS, 2023

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

less than 1 minute read

Published in NEURIPS, 2023

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

1 minute read

Published in IEEE BIBM conference, 2023

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

1 minute read

Published in MICCAI conference, 2023

Learning to control DaVinci surgical robot for soft tissue manipulation using expert demonstrations.

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Multi-Modality Deep Learning Methods to Learn Alzheimer’s Disease Classification

less than 1 minute read

Published in Georgia Institute of Technology, 2023

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

less than 1 minute read

Published in arxiv, 2022

We break down the path of surgical automation into features required and current challenges and advances required to make this happen

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Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

1 minute read

Published in Nature - Scientific Reports, 2021

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.

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Personalized Brain State Targeting via Reinforcement Learning

1 minute read

Published in Neuromatch academy, 2020

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.

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In-Silico Repositioning of Drugs for Neurofibromatosis 2 Vestibular Schwannoma using Machine Learning

2 minute read

Published in Synapse, 2020

Deep learning based model to re-purpose drugs drugs to new diseases(Neurofibromatosis 2 Vestibular Schwannoma). Part of submission for Hack4Rare hacakathon by MIT.

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