Key Research Areas

Deep Learning and Its Applications, Biometrics, Medical Imaging


I have received my Masters (M.Tech) and Doctoral (Ph.D) degrees from Indian Institute of Technology Kanpur in 2009 and 2014 respectively. Later, I joined the School of Computing and Electrical Engineering (SCEE) in August 2014. Currently, I am working as an Associate Professor at IIT Mandi, HP. A few research areas in which I am active are Biometrics, Deep Learning and its applications, Medical Imaging. I am really passionate about teaching especially Deep Learning related topics.

Recent Publications

1. C. A. Daksh Thapar Aditya Nigam, "Anonymizing egocentric videos," in ICCV'21, 2021.
2. A. A. Avantika Singh and A. Nigam, "Cancelable Iris template generation by aggregating patch level Ordinal relations with its holistically extended performance and security analysis," Image and Vision Computing, vol. 104, 2020.
3. B. V. R. K. Ranjeet R. Jha Hrithik Gupta, Aditya Nigam, Arnav Bhavsar, Sudhir Pathak, Walter Schneider, "Enhancing HARDI reconstruction from undersampled data via multi-context and feature inter-dependency GAN," in ISBI'21, 2021.
4. A. N. Daksh Thapar Gaurav Jaswal and C. Arora, "Gait metric learning Siamese network exploiting dual of spatio-temporal 3D-CNN intra and LSTM based inter gait-cycle-segment features," Pattern Recognition Letters, vol. 125, pp. 646-653, 2018.
5. A. N. Avantika Singh Pratyush Gaurav, Chirag Vashist and R. P. Yadav, "IHashNet: Iris Hashing Network based on efficient multi-index hashing," in IJCB'20, 2020.
6. C. A. Daksh Thapar and A. Nigam, "Is Sharing of Egocentric Video Giving Away Your Biometric Signature?," in ECCV'20, 2020.
7. C. A. Daksh Thapar Aditya Nigam, "Merry Go Round: Rotate a Frame and Fool a DNN," in CVPR'22, 2022.
8. A. N. Ranjeet Ranjan Jha Sudhir K. Pathak, Vishwesh Nath, Walter Schneider, B. V. Rathish Kumar, Arnav Bhavsar, "VRfRNet: Volumetric ROI fODF reconstruction network for estimation of multi-tissue constrained spherical deconvolution with only single shell dMRI," The Journal of MRI, vol. 90, pp. 1-16, 2022.

Course Taught

1. CS671: Deep Learning and its Applications
2. CS672: Advance Deep Learning Concepts
3. CS370: System Practicum
4. IC272: Data Science 3
5. IC252: Data Structure and Algorithms