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Personal profile

Research interests

My research blends Computer Vision with Arts and Cultural Heritage, developing novel AI solutions for preservation, interpretation, and reconfiguration of arts across diverse media. I focus on generative models, 3D reconstruction, multimodal large language models, depth estimation, visual saliency, and multitask learning aiming to push the boundaries of how AI integrates into society.

 I am passionate about exploring how AI interacts with human cognition and understanding the ethical and social impacts of AI technologies. My work seeks to create AI systems that align with human values, ensuring that they are responsible and inclusive.

Teaching interests

I enjoy teaching courses in Visual Computing, Computer Vision, AI Ethics, and Machine Learning.

Teaching is not just about imparting knowledge, but inspiring creativity and critical thinking. I strive to create an inclusive classroom where diverse perspectives are encouraged, and students can collaborate on cutting-edge AI projects.

Other responsibilities

 As the Chair of the Department’s Self-Assessment Team (DSAT) and a member of the Equality, Diversity, & Inclusion Committee, I am committed to fostering a culture that values diversity in STEM. I have been recognised for my outreach efforts as a mentor for various non-profit initiative for teaching children and empowering women in technology. I am the organiser of Women in Computer Vision Workshop in CVPR 2025, Nashville, and in the past in CVPR 2024, Seattle. I have been a mentor for Women in STEM in ECCV 2024, Milan. I also led the organisation of  workshops ike AI for Visual Arts at ECCV 2024, co-chairing the publicity of CVPR 2025, hosted events for early-career researchers in flagship conferences like CVPR 2024, creating spaces for networking and collaboration internationally. Through these efforts, I aim to bring STEM to communities inclusive of all representations- gender, sexual orientation, nationality, and race.

 

Willing to supervise doctoral students

 I am always excited to supervise doctoral students who are passionate about Computer Vision. I have supervised 5 doctoral students until now, 26 Master research students and 18 Bachelor students across the UK, US, Switzerland, aand China. If you are interested in computer vision  (preferrably for visual arts and/or cultural heritage and/or media and/or content creation), please reach out. I am happy to chat more about impact-driven research brought by interdisciplinary AI. Email: db2466 (at) bath (dot) ac (dot) uk

Education/Academic qualification

Computer Science, Doctor of Science, Estimating Image Depths in Comics Domain, Swiss Federal Inst of Technology (EPFL)

1 Mar 201931 Mar 2023

Award Date: 18 Jul 2023

Computer Science, Master of Science, An Evolutionary Learning Algorithm based on Plant Intelligence to solve Optimization Problems, Kyungpook National University

1 Aug 20151 Aug 2017

Award Date: 15 May 2017

External positions

Postdoctoral Scientist, Swiss Federal Inst of Technology (EPFL)

1 Sept 202330 Jun 2024

Google Inside Look Intern, Google Research

Jul 2019

Project Lead, Samsung Postech Intelligent Media Research Centre

1 Aug 20181 Feb 2019

Deep Learning Research Engineer, Samsung Postech Intelligent Media Research Centre

1 Aug 201731 Jul 2018

Machine LearningResearcher, Brain Korea 21 + Research Project, Ministry of Education

1 Aug 201630 Jul 2017

Google Summer of Code Intern, Google Research

1 May 20151 Aug 2015

Keywords

  • QA75 Electronic computers. Computer science
  • Computer Vision
  • Visual Computing
  • Evolutionary Computation

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

  1. SDG 4 - Quality Education
    SDG 4 Quality Education
  2. SDG 5 - Gender Equality
    SDG 5 Gender Equality
  3. SDG 10 - Reduced Inequalities
    SDG 10 Reduced Inequalities

Fingerprint

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Collaborations and top research areas from the last five years

Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
  • CoDA: Instructive Chain-of-Domain Adaptation with Severity-Aware Visual Prompt Tuning

    Gong, Z., Li, F., Deng, Y., Bhattacharjee, D., Ma, X., Zhu, X. & Ji, Z., 29 Oct 2024, Computer Vision – ECCV 2024 - 18th European Conference, Proceedings. Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T. & Varol, G. (eds.). Springer Science and Business Media Deutschland GmbH, p. 130-148 19 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 15135 LNCS).

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    Open Access
    11   Link opens in a new tab Citations (SciVal)
  • Data Augmentation via Latent Diffusion for Saliency Prediction

    Aydemir, B., Bhattacharjee, D., Zhang, T., Salzmann, M. & Süsstrunk, S., 11 Sept 2024.

    Research output: Working paper / PreprintPreprint

    File
    95 Downloads (Pure)
  • Unlocking Comics: The AI4VA Dataset for Visual Understanding

    Grönquist, P., Bhattacharjee, D., Aydemir, B., Ozaydin, B., Zhang, T., Salzmann, M. & Süsstrunk, S., 27 Oct 2024, ECCV Workshop: arXiv.

    Research output: Working paper / PreprintPreprint

    File
    129 Downloads (Pure)
  • Vision Transformer Adapters for Generalizable Multitask Learning

    Bhattacharjee, D., Süsstrunk, S. & Salzmann, M., 1 Oct 2023, 2023 IEEE/CVF International Conference on Computer Vision (ICCV). U. S. A.: IEEE, p. 18969-18980

    Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

    15   Link opens in a new tab Citations (SciVal)