General Information

Full Name Aditya Chattopadhyay
Date of Birth 6th July 1994
Languages English, Hindi, Bengali


  • 2024 (Expected)
    Johns Hopkins University
    • Advisors - Prof. René Vidal and Prof. Donald Geman
    • Thesis - An information-theoretic framework for designing interpretable predictors.
  • 2018
    M.S. in Computational Natural Sciences
    International Insitute of Information Technology, Hyderabad
    • Advisor - Prof. U. Deva Priyakumar
    • Thesis - A probabilistic framework for constructing temporal relations in Monte Carlo trajectories.
  • 2018
    B.Tech. in Computer Science
    International Insitute of Information Technology (IIIT), Hyderabad

Areas of Interests

  • Explainable AI, Computer Vision, Optimization Methods, Probabilistic Graphical Models, Information Theory, Generative Models

Work Experience

  • Jun 2021 - Nov 2021
    Applied Science Intern
    • Supervisor - Dr. Himanshu Arora
    • Summer internship at Amazon Visual Search
    • Worked on generative models for indoor scene synthesis.
    • Work published as a conference paper at WACV 2023.
  • Aug 2018 - Jun 2018
    Research Assistant
    Indian Institute of Technology (IIT), Hyderabad
    • Supervisor - Prof. Vineeth N Balasubramanian
    • Worked on developing algorithms for explainable AI.
    • Developed Grad-CAM++ which is now a popular method for interpreting decisions made by a CNN, publised at WACV 2018.
    • Developed Causal Attributions, a method for interpretating decisions made by a deep network build from first principles of causality. This work was published at ICML 2019.
  • Jan 2017 - July 2017
    Research Assistant
    International Institute of Information Technology (IIIT), Hyderabad
    • Supervisor - Prof. U Deva Priyakumar
    • Worked on developing ML methods for understanding biomolecule unfolding.
  • Jun 2015 - Aug 2015
    Research Intern
    WestfalischeWilhelms-Universitat, Muenster
    • Supervisor - Dr. Mark P Waller
    • Worked on using ML methods to predict molecular structure from their Infrared Spectra.


  • 2023
    • A sparse-coding algorithm with applications to explainable-AI for image classification problems.
  • 2022
    Variational Information Pursuit
    • A framework for making interpretable ML decisions for classification problems.
  • 2018
    Causal Attributions
    • A post-hoc interpretbility method for understanding deep network predictions with causal guarantees.
  • 2018
    • A post-hoc interpretbility method for understanding CNN predictions.


  • 2021
    • Awarded the MINDS Summer & Spring Fellowships.
  • 2018
    • Awarded Gold Medal for outstanding academic achievement at IIIT, Hyderabad (given to students with the highest cumulative grade point average [CGPA] in graduating class).
    • Consistently in Dean’s List for all semesters in IIIT, Hyderabad which is awarded to top 15% of students in each programme.