Faculty at School of Engineering

Nitin Kumar

Nitin Kumar

Assistant Professor

School of Engineering


Contact Information

Nitin Kumar is a Researcher and currently an Assistant Professor at the CSE Department of Shiv Nadar University. His research interests lie in the fields of pattern recognition and machine learning. Specifically, he is interested in understanding 1-D signals, medical images and language models.

  • Ph.D. in Computer Science - IIT Bombay
  • M.E. - IISc Bengaluru
  • B.Tech - NIT Warangal

  • Assistant Professor, Shiv Nadar Institution of Eminence, Noida (2023–Present)
  • Senior Research Engineer, Philips Research (2021–2023)
  • Assistant Professor, LNMIIT Jaipur (2019–2021)
  • Research Project Associate, Aditya Image Information Technologies (2014–2018)
  • Member of Technical Staff, VMware Software India Pvt. Ltd., Bengaluru (2013–2014)

  • Machine Learning
  • Medical Image Computing
  • Computer Vision
  • Graph Representation Learning

To learn more about my work, visit: https://nitinnazkani.github.io/nitinnazkani/

 

  • In top 10 (0.3%) finalists for best paper award at IEEE International Conference on Image Processing (ICIP) -2017
  • Recipient of travel grants from MICCAI, ICIP, MedImage societies

  • N. Kumar and S. P. Awate, “Semi-supervised robust mixture models in RKHS for abnormality detection in medical images,” IEEE Transactions on Image Processing, vol. 29, pp. 4772–4787, 2020.

Conferences:

  • V. T. Abburi, A. Singhal, S. J. Shigwan, and N. Kumar, “Armarecon: An ARMA convolutional filter based graph neural network for neurodegenerative dementias classification,” in 2026 IEEE 23rd International Symposium on Biomedical Imaging (ISBI), IEEE, 2026, pp. 1–4.
  • Aditya, N. Kumar, and S. Shigwan, “UCDSC: Open set uncertainty aware deep simplex classifier for medical image datasets,” in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2026, pp. 4787–4796.
  • A. M. Adityaja, S. J. Shigwan, and N. Kumar, “UnSegMedGAT: Unsupervised medical image segmentation using graph attention networks clustering,” in 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI), IEEE, 2025, pp. 1–4.
  • S. Dubey, K. Mittal, S. K. Behera, M. Ravikiran, N. Kumar, S. Shigwan, and R. Saluja, “Multi-feature graph convolution network for Hindi OCR verification,” in Proceedings of the 1st Workshop on Benchmarks, Harmonization, Annotation, and Standardization for Human-Centric AI in Indian Languages (BHASHA 2025), 2025, pp. 1–10.
  • C. G. Kamra, I. D. Mastan, N. Kumar, and D. Gupta, “SimSam: Simple Siamese representations based semantic affinity matrix for unsupervised image segmentation,” in 2024 IEEE International Conference on Image Processing (ICIP), IEEE, 2024, pp. 1172–1178.
  • K. S. G. Reddy, S. Bodduluri, A. M. Adityaja, S. Shigwan, N. Kumar, and S. Mukherjee, “UnSegArmaNet: Unsupervised image segmentation using graph neural networks with convolutional ARMA filters,” in 35th British Machine Vision Conference 2024 (BMVC 2024), Glasgow, UK, November 25–28, 2024, BMVA, 2024. [Online]. Available: https://papers.bmvc2024.org/ 0922.pdf.
  • N. Kumar, S. Chandran, A. V. Rajwade, and S. P. Awate, “Semi-supervised robust one-class classification in RKHS for abnormality detection in medical images,” in 2019 IEEE International Conference on Image Processing (ICIP), IEEE, 2019, pp. 544–548.
  • N. Kumar, A. V. Rajwade, S. Chandran, and S. P. Awate, “Kernel generalized Gaussian and robust statistical learning for abnormality detection in medical images,” in 2017 IEEE International Conference on Image Processing (ICIP), IEEE, 2017, pp. 4157–4161.
  • N. Kumar, A. V. Rajwade, S. Chandran, and S. P. Awate, “Kernel generalized-Gaussian mixture model for robust abnormality detection,” in International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, 2017, pp. 21–29.
  • S. Banerjee, N. Kumar, and C. V. Madhavan, “Text simplification for enhanced readability,” in KDIR/KMIS, 2013, pp. 202–207.

Invited Talks:

  • “Unsupervised and Semisupervised Learning for Abnormality Detection in Medical Images,” Indian Institute of Technology Roorkee, Roorkee, India.
  • “Unsupervised and Semisupervised Learning for Abnormality Detection in Medical Images,” IIIT Bangalore, Bengaluru, India.

MICCAI-2017, IEEE ICIP-2017 and IEEE ICIP-2019  

IEEE Transactions on Medical Imaging, IEEE Internet of Things, MICCAI, IEEE ISBI, BMVC etc.