Deepak Mishra

Assistant Professor

My journey began in 2010 with Healthcare R&D team in Siemens Information Systems Ltd. I was fortunate to work with some of the best mentors on challenging problems. It setup platform for an in-depth and rigorous research and motivated me to join IIT Delhi for higher studies. I explored different domains while remaining active in my doctoral research. I am continuing my journey as Assistant Professor at the Department of Computer Science and Engineering, IIT Jodhpur.

I am interested in fundamental and applied research problems related to deep learning, medical image analysis, and responsible AI. I have funded PhD and RA positions available in my group. Students with strong research aptitude are encouraged to contact with updated resumes. My research group, Medical AI Systems (MAISys) at IIT Jodhpur, is constantly working towards the development and deployment of AI-driven medical technologies. For more details, please visit MAISys website.

Education

PhD in Computer Technology (Electrical Engineering), IIT Delhi, 2019
M. Tech in Computer Technology, 2013, IIT Delhi
B. Tech in Electronics and Communication Engineering, Indian Institute of Information Technology Jabalpur, 2010

News

[Jul 2025] Our work "Scale-Aware Adaptive Feature Quantization for Robust Medical Image Representation Learning" is accepted at ECAI-2025.
[Jun 2025] Our work "Self-supervised Contextual Representations of Chest X-ray Images" is accepted in IEEE Signal Processing Letters.
[Jun 2025] Indian patent granted for "Adaptive Frame Resolution Based Image System for Always-On Imaging Applications".
[May 2025] Our work "Fine-Grained Rib Fracture Diagnosis with Hyperbolic Embeddings" is accepted (early accept) in MICCAI 2025.
[May 2025] Invited as a Distinguished Speaker in FDP on "AI in Medical Imaging and Diagnostics: Current Trends and Challenges" at NIT Jaipur.
[Apr 2025] Our work "FLAME" is accepted in IEEE Transactions on Emerging Topics in Computational Intelligence.
[Apr 2025] Indian patent granted for "Automated Personalized Assistance and Monitoring System for People with Special Needs".
[Jan 2025] Our work "ViT Coupled Efficient CMOS Image Sensor with Sparse Acquisition and Patch Selection" is accepted in ISCAS 2025.
[Nov 2024] Speaking at Sun Pharma Science Foundation National Symposium on “Artificial Intelligence in Healthcare”.
[Oct 2024] Our work "OTCXR: Rethinking Self-supervised Alignment using Optimal Transport for Chest X-ray Analysis" is accepted in WACV 2025.
[Aug 2024] Our work "Survival Prediction in Lung Cancer through Multi-modal Representation Learning" is accepted (early accept) in WACV 2025.
[Aug 2024] Our work "MLVICX" is accepted in IEEE Journal of Biomedical and Health Informatics (J-BHI).
[Aug 2024] Ishan Mishra, PhD scholar from our lab, is visiting the University of Buffalo (SUNY Buffalo) as research intern.
[Aug 2024] Indian patent filed on "System for Multimodal Data Acquisition and Synchronization and Method Thereof".
[Aug 2024] Our work on "Instance Discrimination Learning through Quantized Contextual Embeddings for Medical Images," is accepted in ICPR 2024.
[Jul 2024] Appointed as Associate Dean, Digital Infrastructure and Automation at IIT Jodhpur.
[Jun 2024] Our work on "Codebook Guided Bootstrapping for Medical Image Representation Learning" is accepted in MICCAI 2024.
[Mar 2024] Our work on "Aggregation-Assisted Proxyless Distillation: A Novel Approach for Handling System Heterogeneity in Federated Learning" is accepted in IJCNN 2024. This is an outcome of B.Tech Project work of Nirbhay Sharma and Mayank Raj.
[Jan 2024] Our work on "Towards soft-robotic assistance of ultrasonic Imaging" is accepted in IEEE South Asian Ultrasonics Symposium (SAUS) 2024.
[Dec 2023] Our work on "Severity of Error in Hierarchical Datasets" is accepted in Scientific Reports. This is an outcome of M.Tech thesis work of Satwik Srivastava.
[Oct 2023] Our work on "Large Scale Time-Series Representation Learning via Simultaneous Low and High Frequency Feature Bootstrapping" is accepted in IEEE Transactions on Neural Networks and Learning Systems (T-NNLS).

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