Ankita Chatterjee

Ankita Chatterjee received the Bachelor of Technology in Computer Science and Engineering from W.B.U.T, India and Master of Technology in Computer Science and Engineering from KIIT Deemed to be University, Bhubaneswar, India. Currently, she is working as a Junior Research Fellow (JRF) at IIT Bhubaneswar, India. Her research interests include Brain-Computer Interface, Machine Learning, Computer Vision and Deep Learning.


The key technical skills:

1. Exploratory Data Analysis

2. Data- pre-processing

3. Supervised and Unsupervised Learning (Logistic Regression, SVM, Ensembles models etc)

4. Image Classification using deep learning (VGG16, ResNet50, MobileNetV2 and EfficientNet

variants)

5. Object detection-Faster RCNN (ResNet50 backbone), EfficientDet, YOLOv5

6. Video Classification using deep learning (3D-CNN and CNN-LSTM)

7. Hand Gestures recognition using Mediapipe and LSTM

Professional Experience:

  • Working as a Machine Learning Engineer on the project “Roll Defect detection” (Image classification) at K2M Analytics LLP from 22nd March 2022 to present day.

  • Worked as a Junior Research Fellow (JRF) on the project “Augmented Reality in Classroom Environment” at VARCOE LAB of School of Electrical Science, IIT Bhubaneswar, Odisha, India. from 1st January 2021 to 17th January 2022.

  • Working as a Junior Research Fellow (JRF) on the project “Technical Scrutiny of PMGSY DPRs as STA and PTA” at School of Electrical Sciences, IIT Bhubaneswar, Odisha, India from 1st November 2019 to 28th January 2020.

  • Worked as a Junior Research Fellow (JRF) on the project “Implementation of Advanced Machine Learning Algorithms for Cluster Expansions” at School of Electrical Sciences, IIT Bhubaneswar, Odisha, India from 22nd January 2019 to 31st October 2019.

Publications:

  1. R. Chatterjee, A. Chatterjee, and SK, H. Islam, “Deep Learning Techniques for Observing the Impact of the Global Warming from Satellite Images of Water-Bodies” Multimedia Tools and Applications, Springer (SCIE, IF:1.935)

  2. R. Chatterjee, A. Chatterjee, SK, H. Islam, and Md. K. Khan, “An Object Detection-based Few-shot Learning Approach for Multimedia Quality Assessment” Multimedia Systems, Springer (SCIE, IF:2.757)

  3. R. Chatterjee, A. Chatterjee, R. Halder, “An Efficient Pneumonia Detection from the Chest X-Ray Images,” Proceedings of International Conference on Machine Intelligence and Data Science Applications. Algorithms for Intelligent Systems, Springer, Singapore pp. 779-789, 2021.

  4. R. Chatterjee, A. Chatterjee, R. Halder, “Impact of Deep Learning on Arts and Archaeology: An Image Classification Point of View,” Proceedings of International.

  5. A. Datta*, R. Chatterjee, D. K. Sanyal and D. Guha, “An Ensemble Classification approach to Motor-Imagery Brain State Discrimination Problem,” Proceedings of IEEE International Conference on Infocom Technologies and Unmanned Systems (ICTUS'2017), Dubai, December 2017.

  6. A. Datta*, R. Chatterjee, “Comparative Study of Different Ensemble Composition in EEG Signal Classification Problem,” Proceedings of Springer AISC series, International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2018), Kolkata, India, February 2018.

  7. R. Chatterjee, A. Datta* and D. K. Sanyal, “Ensemble Learning Approach to Motor-Imagery EEG Signal Classification,” Book chapter in Machine Learning in Bio-Signal Analysis and Diagnostic Imaging, Academic Press, Elsevier, pp. 183-208, 2018. (DOI: 10.1016/B978-0-12-816086-2.00008-4)

  8. R. Chatterjee, D.K. Sanyal, A. Datta* and S. Chatterjee, “Diversity Matrix based Performance Improvement for Ensemble Learning Approach,” Book chapter in Hybrid Computational Intelligence: Research and Applications, CRC Press, Taylor and Francis, 2019.


*Datta was her maternal surname before marriage.
(c) All Rights Reserved by Ankita Chatterjee 2022