Dr. Shounak Chakraborty

Dr.SHOUNAK CHAKRABORTY, PhD


Assistant Professor,
Department of Computer Science and Engineering,
Email: shounak  [at] iiitk.ac.in | Contact Number: 08518-289100 - 132(E)

Academic Qualifications

  • Ph.D in Computer Science and Engineering from IIIT Guwahati

Areas of Interest

  • Artificial neural networks, remote sensing, pattern recognition and image processing

Professional / Teaching Experience

  • Welcome to my page. My name is Shounak and I have completed my PhD from the Indian Institute of Information Technology, Guwahati in August 2020. I have been awarded with the university Gold medal for standing first class first in M.Tech. I have also been a recipient of DST-INSPIRE fellowship (Fellow No.: IF150878), by Govt. of India for pursuing the doctoral studies 2015-20. I have four publications in SCI journals and nine in reputed conferences. Please click here to know more.

Publication Profile

Sponsored Research Projects

  • Name of the Project: OASIS: Online Aerial image-based Surface Information System
    Funding agency: IITI DRISHTI CPS under the aegis of NMICPS, DST, Govt. of India
    Amount: INR 8,64,300
    PI: Dr. Shounak Chakraborty
    Co-PI: Dr. K. Sathya Babu
    Duration: 12 months

  •  Name of the Project: Stock verification at RMHP using LiDAR drones
    Funding agency: RINL and STPI (MeitY)
    Amount: INR 34,50,000 through a faculty startup Bhoodrishti Pvt. Ltd.
    PI: Dr. Shounak Chakraborty
    Co-PI: Dr. Eswaramoorthy K. V. and Dr. Jit Mukherjee
    Duration: 12 months

International Journal Publications

  1. S. Chakraborty, I. Kalita, and M. Roy. “Unsupervised Domain Adaptation in land cover classification under neural approach using feature-level ensemble”. In 39th IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pages 724-727, July 2019.

  2. S. Chakraborty, I. Kalita, and M. Roy, “An adversarial learning mechanism for dealing with the class-imbalance problem in land-cover classification”. In 19th International Conference on Hybrid Intelligent Systems, Springer, Bhopal, India, pages 188-196, Bhopal, India, 2019.

  3. S. Chakraborty, N. Aggarwal, and M. Roy, "A deep semi-supervised approach for multi-label land-cover classification under scarcity of labelled images", in 10th International Conference on Soft Computing for Problem Solving (SocProS), Indore, India, 2020. (Accepted)

  4. I. Kalita, S. Chakraborty, and M. Roy, "Deep Ensemble Network for handling class-imbalance problem in land-cover classification". In 18th IEEE International Conference on Information Technology, pages 505-509, Bhubaneshwar, India, 2019.

  5. S. Chakraborty, S. K. Adhikary and M. Roy. “Automatic Land-cover Classification using Semi-supervised Multilayer Perceptron for Analyzing Remotely Sensed Images”. In IEEE International conference on  Innovations in Electronics, Signal Processing and Communication, Shillong, India, April 2017.

  6. S. Chakraborty and M. Roy. “Domain Adaptation for Land-cover Classification of Remotely Sensed Images using Ensemble of Multilayer Perceptrons”. In third IEEE International Conference on Recent Advances in Information Technology, pages 523-528, Dhanbad, India, March 2016.

  7. A. Chakravorty and S. Chakraborty. “A novel semi-supervised approach for semantic segmentation of aerial remote sensing images under limited ground-truth availability”. Signal, Image and Video Processing Journal, Springer. 

International Conferences

  1. S. Chakraborty, I. Kalita, and M. Roy. “Unsupervised Domain Adaptation in land cover classification under neural approach using feature-level ensemble”. In 39th IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, pages 724-727, July 2019.

  2. S. Chakraborty, I. Kalita, and M. Roy, “An adversarial learning mechanism for dealing with the class-imbalance problem in land-cover classification”. In 19th International Conference on Hybrid Intelligent Systems, Springer, Bhopal, India, pages 188-196, Bhopal, India, 2019.

  3. S. Chakraborty, N. Aggarwal, and M. Roy, "A deep semi-supervised approach for multi-label land-cover classification under scarcity of labelled images", in 10th International Conference on Soft Computing for Problem Solving (SocProS), Indore, India, 2020. (Accepted)

  4. I. Kalita, S. Chakraborty, and M. Roy, "Deep Ensemble Network for handling class-imbalance problem in land-cover classification". In 18th IEEE International Conference on Information Technology, pages 505-509, Bhubaneshwar, India, 2019.

  5. S. Chakraborty, S. K. Adhikary and M. Roy. “Automatic Land-cover Classification using Semi-supervised Multilayer Perceptron for Analyzing Remotely Sensed Images”. In IEEE International conference on  Innovations in Electronics, Signal Processing and Communication, Shillong, India, April 2017.

  6. S. Chakraborty and M. Roy. “Domain Adaptation for Land-cover Classification of Remotely Sensed Images using Ensemble of Multilayer Perceptrons”. In third IEEE International Conference on Recent Advances in Information Technology, pages 523-528, Dhanbad, India, March 2016.

  7. A. Chakravorty and S. Chakraborty. “A novel semi-supervised approach for semantic segmentation of aerial remote sensing images under limited ground-truth availability”. Signal, Image and Video Processing Journal, Springer, 2024

  8. R Abhishek, A. Chakravorty, and S. Chakraborty, " Active learning based semantic segmentation for extraction of minute objects from multispectral satellite images", in 42nd IEEE Geoscience and Remote Sensing Symposium, pages 7274-7277, Kuala Lumpur, Malaysia, 2022.

Book Chapters

  1. S. Chakraborty, N. Choudhury and I. Kalita, "AI‐Based Smart Agriculture Monitoring Using Ground‐Based and Remotely Sensed Images", The New Advanced Society: Artificial Intelligence and Industrial Internet of Things Paradigm, pages 191- 221, Wiley-Scrivener Publishing LLC, 2022

Courses Handled

  1. Deep learning (CS464/ AD 462)

  2. Problem solving and computer programming (CS101)

  3. Pattern Recognition (AD374)

  4. Soft Computing (AD303)

Accolades

  • University topper in M. Tech