Email: nspunn@iiitm.ac.in
Experience: Worked as: 1) Consultant for 2 years (2015-17) in Intellect Design Arena Ltd., Chennai, industry; 2) Postdoctral Research Fellow at Mayo Clinic, Arizona, USA
Address:
Room No. 207, Department of CSE, Block IV, ABV-IIITM Gwalior, 474015 (M.P)
Personal Web Page: https://punndeeplearningblog.com
I am an Assistant Professor in the Department of Computer Science and Engineering at Atal Bihari Vajpayee Indian Institute of Information Technology and Management (ABV-IIITM), Gwalior, India. I received my Ph.D. in Biomedical Image Segmentation from the Indian Institute of Information Technology Allahabad, Prayagraj, in April 2022. Before joining ABV-IIITM Gwalior in March 2023, I worked as a Postdoctoral Fellow at the Machine Intelligence in Medical Imaging (MI2) Lab, Mayo Clinic, Arizona, USA. I completed my bachelor's degree in Computer Science and Engineering from the National Institute of Technology Hamirpur in 2015 and subsequently worked as a software developer at Intellect Design Arena Ltd., Chennai.
My research interests include deep learning, biomedical image analysis, medical image segmentation and classification, self-supervised learning, explainable artificial intelligence (XAI), and trustworthy AI. My research focuses on developing data-efficient, robust, and interpretable deep learning methods for healthcare applications, particularly in scenarios involving limited labeled data. I am also interested in adversarial robustness, computer vision, bioinformatics, and the responsible application of artificial intelligence in healthcare.
I have authored and co-authored research articles published in reputed international journals and conferences. My academic work also involves teaching, mentoring students, delivering invited talks, and organizing workshops and short-term courses in emerging areas of artificial intelligence and machine learning. I have received multiple best paper awards and was selected to participate in the Heidelberg Laureate Forum in 2020, 2021, and 2022. Through my research and teaching, I aim to advance reliable, data-efficient, and interpretable AI methodologies and contribute to the development of intelligent systems that address real-world challenges, particularly in healthcare.
Number of M. Tech. Graduated:06,
Number of Publications: 09 (Journals), 19 (Conferences),
h-index: 18, i10 index: 22, Number of Projects: 02,
Number of Keynote/Invited Talks Delivered: 19,
Number of Committee Memberships: 01
Office: 207, Block-V, ABV-IIITM
|
Degree |
Year |
Subject |
University/Institution |
|
PhD |
2022 |
Information Technology |
Indian Institute of Information Technology Allahabad, Uttar Pradesh, India |
|
M. Tech. |
2019 |
Software Engineering |
Indian Institute of Information Technology Allahabad, Uttar Pradesh, India |
|
B. Tech. |
2015 |
Computer Science and Engineering |
National Institute of Technology Hamirpur, Himachal Pradesh, India |
Deep Learning, Medical Image Analysis, Bioinformatics, Quantum Machine Learning, Big Data Analytics
1.Rajpopat, Subodh, Sunil Kumar, and Narinder Singh Punn. “Cerebral palsy detection from infant using movements of their salient body parts and a feature fusion model.” The Journal of Supercomputing 81.1 2025: 106, https://doi.org/10.1007/s11227-024-06520-z.
2.Tanay, Mogalluru Chidhvilas, Rahul Rathnam, Pamba Vamshi Krishna, Mrinal Devnath, and Narinder Singh Punn. “Enhanced QSAR Modeling for Drug Discovery: Leveraging Advanced Computational Tools and Techniques.” In 2024 IEEE Region 10 Symposium (TENSYMP),pp.1-6.IEEE,2024. https://doi.org/10.1109/TENSYMP61132.2024.10752307.
3.Punn, Narinder Singh, Bhavik Patel, and Imon Banerjee. “Liver fibrosis classification from ultrasound using machine learning: a systematic literature review.” Abdominal Radiology 49, 2024: 69-80, https://doi.org/10.1007/s00261-023-04081-y.
4.Punn, Narinder Singh, and Sonali Agarwal. “BT-Unet: A self-supervised learning framework for biomedical image segmentation using Barlow Twins with U-Net models.” Machine Learning 111, 2022: 4585-4600, https://doi.org/10.1007/s10994-022-06219-3.
5.Punn, Narinder Singh, and Sonali Agarwal. “CHS-Net: A Deep Learning Approach for Hierarchical Segmentation of COVID-19 via CT Images.” Neural Processing Letters 54, 2022: 3771-3792, https://doi.org/10.1007/s11063-022-10785-x.
6.Punn, Narinder Singh, and Sonali Agarwal. “Modality specific U-Net variants for biomedical image segmentation: a survey.” Artificial Intelligence Review 55, 2022: 5845–5889, https://doi.org/10.1007/s10462-022-10152-1.
7.Punn, Narinder Singh, and Sonali Agarwal. “RCA-IUnet: a residual cross-spatial attention-guided inception U-Net model for tumor segmentation in breast ultrasound imaging.” Machine Vision and Applications 33, no. 2, 2022: 27, https://doi.org/10.1007/s00138-022-01280-3.
8.Punn, Narinder Singh, and Sonali Agarwal. “Automated diagnosis of COVID-19 with limited posteroanterior chest X-ray images using fine-tuned deep neural networks.” Applied Intelligence 51, no. 5, 2021: 2689-2702, https://doi.org/10.1007/s10489-020-01900-3.
9.Punn, Narinder Singh, and Sonali Agarwal. “Multi-modality encoded fusion with 3D inception U-net and decoder model for brain tumor segmentation.” Multimedia Tools and Applications 80, no. 20 2021: 30305-30320, https://doi.org/10.1007/s11042-020-09271-0.
10.Punn, Narinder Singh, and Sonali Agarwal. “Inception u-net architecture for semantic segmentation to identify nuclei in microscopy cell images.” ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM) 16, no. 1, 2020: 1-15, https://doi.org/10.1145/3376922.
|
Sl |
Grant (Project/ Consultancy) |
Funding Agency |
PI/Co-PI |
|
1 |
A multimodal analysis for mental disorder recognition to improve mental health and well-being (MODEST) |
UPCST |
Co-PI |
|
2 |
Robust Defense Mechanism Against Adversarial Attacks in Medical Imaging Systems |
FIG |
PI |
|
SN |
Title |
Period |
Sponsoring Organization |
Venue |
|
|
|
|
From |
To |
|
|
|
1. |
FDP - Towards AI Enabled Computer Vision |
15/07/2024 |
19/07/2024 |
Self |
ABV-IIITM Gwalior |
|
2. |
Advancements in Medical Image Analysis: Theory and Hands-on Workshop on Cutting Edge Technologies |
20/04/2024 |
21/04/2024 |
TIIC |
ABV-IIITM Gwalior |
|
3 |
Advanced Workshop on Deep Learning Applications in Imaging Systems |
17/07/2023 |
23/07/2023 |
SERB |
ABV-IIITM Gwalior |
|
4 |
High End Workshop on Machine Learning applications for Medical Imaging |
10/07/2023 |
16/03/2023 |
SERB |
ABV-IIITM Gwalior |
