Deep Chakraborty

About Me

I am a Ph.D. candidate at the Manning College of Information & Computer Sciences at UMass Amherst where I investigate fundamental problems in computer vision through the lens of self-supervised learning and information theory, under the supervision of Erik Learned-Miller at the Vision lab.

I have also worked with Mario Parente from RHOgroup, and Ina Fiterau from InfoFusion lab in the past on problems in applied computer vision and deep learning. My industry experience includes two research internships at Apple and one at Philips Lighting Research, where I worked on a variety of topics from scene understanding to audio processing. In my free time, I love to experiment with coffee brewing techniques (inspired by James Hoffman), cook gourmet dishes (like those by Gordon Ramsay), and listen to classic rock music (I’m a big fan of Queen).

Research Interests

• Machine Learning: self-supervised learning, unsupervised learning, information theory
• Computer Vision: scene understanding, object detection, tracking

News

[Oct. 2024] Our paper got accepted to the workshop on Self-Supervised Learning - Theory and Practice at NeurIPS 2024. I will be attending the conference in person!
[Dec. 2023] Received the CICS dissertation writing fellowship for Spring 2024!
[Dec. 2022] Passed the Ph.D. qualifying exam (portfolio) to achieve candidacy!
[Apr. 2022] Our paper on Mars Terrain Classification got accepted to Earthvision workshop at CVPR 2022. I will be attending in-person!

Publications

2024 | 2022 | 2019 | 2018 | 2016

^{[* = Authors Contributed Equally]}

2024

• Squeezing Water from a Stone: Improving Pre-Trained Self-Supervised Embeddings Through Effective Entropy Maximization
  Deep Chakraborty, Tim G. J. Rudner, Erik Learned-Miller
  NeurIPS 2024 Workshop on Self-Supervised Learning - Theory and Practice
  [PDF]

2022

• Self-Supervised Learning to Guide Scientifically Relevant Terrain Categorization in Martian Terrain Images
  Tejas Panambur*, Deep Chakraborty*, Melissa Meyer, Ralph Milliken, Erik Learned-Miller, Mario Parente
  IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  [PDF] [arXiv] [Code]

2019

• Nonparallel emotional speech conversion
  Jian Gao, Deep Chakraborty, Hamidou Tembine, Olaitan Olaleye
  Annual Conference of the International Speech Communication Association (INTERSPEECH)
  [PDF] [arXiv] [Project Page]

• Pedestrian Detection in Thermal Images using Saliency Maps
  Debasmita Ghose*, Shasvat Desai*, Sneha Bhattacharya*, Deep Chakraborty*, Madalina Fiterau, Tauhidur Rahman
  IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  [PDF] [arXiv] [Project Page] [Spotlight Video]

2018

• Unsupervised Hard Example Mining from Videos for Object Detection
  SouYoung Jin*, Aruni RoyChowdhury*, Huaizu Jiang, Ashish Singh, Aditya Prasad, Deep Chakraborty, Erik Learned-Miller
  European Conference on Computer Vision (ECCV)
  [PDF] [arXiv] [Project Page]

2016

• Bird Call Identification using Dynamic Kernel based Support Vector Machines and Deep Neural Networks
  Deep Chakraborty, Paawan Mukker, Padmanabhan Rajan, Dileep A.D.
  IEEE International Conference on Machine Learning and Applications (ICMLA)
  [PDF]

Activities

• Reviewer for ECCV, CVPR
• Speaker at EMBER
• Mentor at Eureka!

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