Balagopal Unnikrishnan

Balagopal Unnikrishnan

PhD Researcher at Vector Institute / UofT & Ex AI Engineer with A*STAR, Singapore

Download CV

I'm open for collaborations. You'll find me at the Vector Institute situated in the U of T: Schwartz Reisman Innovation Campus.

Find me at:
  • NeurIPS 2025
    San Diego, Dec 1-7
  • WACV 2026
    Tucson, March 6-10

About me

I'm a PhD candidate at the University of Toronto, advised by Dr. Chris McIntosh and Dr. Michael Brudno where my research revolves around developing algorithms that mitigate shortcut learning and spurious correlations in AI models applied to medical data.

A special shoutout to the Schwartz Reisman Institute, Vector Institute, University Health Network (UHN) and SickKids Hospital for generously supporting my work and enabling interdisciplinary collaboration between research and clinical practice.

I completed my Masters from the National University of Singapore (NUS), specializing in Computational Intelligence. Before joining the PhD program, I worked as an AI Research Engineer developing algorithms and clinical tools for AI-augmented healthcare applications.

PhD Updates

  • Our paper "Data-First Mitigation of Spatial and Spectral Shortcuts Without Introducing New Confounders" has been accepted at WACV 2026. This work presents a novel approach to addressing shortcut learning in AI models through data-centric methods rather than architectural changes or loss functions.
  • I'll be presenting our work "When AI Democratizes Exploitation: LLM-Assisted Strategic Manipulation of Fair Division Algorithms" at the ACA Workshop, NeurIPS 2025.
  • Excited to share that my research has crossed 550 citations on Google Scholar. Thank you to the research community for engaging with and building upon our work.
  • Successfully achieved PhD candidacy. My thesis will be centered around addressing shortcut learning & improving model robustness for AI in healthcare.
  • Excited to announce that I've been awarded the prestigious 2024 Schwartz Reisman Institute Graduate Fellowship. The fellowship supports my work on improving robustness and addressing shortcut learning in healthcare AI models.
  • Our paper "Shortcut Learning in Medical AI Hinders Generalization" has been accepted at Nature Digital Medicine. We have also publicly released our code which allows researchers to estimate the generalization of their AI models without external data.
  • I'm appearing as a guest on the ATGO-AI Podcast (Accountability, Trust, Governance, and Oversight of Artificial Intelligence) talking about my research and recent publication on shortcut learning and spurious correlations in healthcare data. The podcast is available on Spotify and Apple Podcasts: Episode 1 and Episode 2.

Publications

SilverLining: Data-First Mitigation of Spatial and Spectral Shortcuts Without Introducing New Confounders
Balagopal Unnikrishnan, Michael Brudno, Chris McIntosh
The IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2026
Coming Soon
Shortcut Learning in Medical AI Hinders Generalization: Method for Estimating AI Model Generalization Without External Data
Cathy Ong Ly*, Balagopal Unnikrishnan*, Tony Tadic, Tirth Patel, Joe Duhamel, Sonja Kandel, Yasbanoo Moayedi, Michael Brudno, Andrew Hope, Heather Ross, Chris McIntosh (*co-first authors)
Nature Digital Medicine 2024
Paper | Code
When AI Democratizes Exploitation: LLM-Assisted Strategic Manipulation of Fair Division Algorithms
Priyanka Verma*, Balagopal Unnikrishnan* (*co-first authors)
ACA Workshop, NeurIPS 2025
Paper
Beyond Ethics: How Inclusive Innovation Drives Economic Returns in Medical AI
Balagopal Unnikrishnan, Ariel Guerra Adames, Amin Adibi, Sameer Peesapati, Rafal Kocielnik, Shira Fischer, Hillary Clinton Kasimbazi, Rodrigo Gameiro, Alina Peluso, Chrystinne Oliveira Fernandes, et al.
Under Review: Nature Digital Medicine
Paper
Red Teaming Large Language Models for Healthcare
Vahid Balazadeh, Michael Cooper, David Pellow, Atousa Assadi, Jennifer Bell, Mark Coatsworth, Kaivalya Deshpande, Jim Fackler, Gabriel Funingana, Spencer Gable-Cook, et al. (including Balagopal Unnikrishnan)
Findings from Machine Learning for Healthcare Conference Workshop 2024
Paper
Diverse and Consistent Multi-view Networks for Semi-supervised Regression
Cuong Nguyen, Arun Raja, Le Zhang, Xun Xu, Balagopal Unnikrishnan, Mohamed Ragab, Kangkang Lu, Chuan-Sheng Foo
Machine Learning, 2023
Paper
Semi-supervised Classification of Radiology Images with NoTeacher: A Teacher that is Not Mean
Balagopal Unnikrishnan, Cuong Nguyen, Shafa Balaram, Chao Li, Chuan Sheng Foo, Pavitra Krishnaswamy
Medical Image Analysis, 2021
Paper
Self-Path: Self-Supervision for Classification of Pathology Images with Limited Annotations
Navid Alemi Koohbanani, Balagopal Unnikrishnan, Syed Ali Khurram, Pavitra Krishnaswamy, Nasir Rajpoot
IEEE Transactions on Medical Imaging, 2021
Paper
Semi-supervised Classification of Diagnostic Radiographs with NoTeacher: A Teacher that is Not Mean
Balagopal Unnikrishnan, Cuong Manh Nguyen, Shafa Balaram, Chuan Sheng Foo, Pavitra Krishnaswamy
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021 | Runner Up - Best Paper Award
Paper
Semi-supervised and Unsupervised Methods for Heart Sounds Classification in Restricted Data Environments
Balagopal Unnikrishnan, Pranshu Ranjan Singh, Xulei Yang, Matthew Chin Heng Chua
arXiv:2006.02610
Paper
CaRENets: Compact and Resource-Efficient CNN for Homomorphic Inference on Encrypted Medical Images
Jin Chao, Ahmad Al Badawi, Balagopal Unnikrishnan, Jie Lin, Chan Fook Mun, James M Brown, J Peter Campbell, Michael Chiang, Jayashree Kalpathy-Cramer, Vijay Ramaseshan Chandrasekhar, et al.
Privacy in Machine Learning Workshop, NeurIPS 2019
Paper
Towards Practical Unsupervised Anomaly Detection on Retinal Images
Khalil Ouardini, Huijuan Yang, Balagopal Unnikrishnan, Manon Romain, Camille Garcin, Houssam Zenati, J Peter Campbell, Michael F Chiang, Jayashree Kalpathy-Cramer, Vijay Chandrasekhar, Pavitra Krishnaswamy, Chuan-Sheng Foo
Domain Adaptation and Representation Transfer Workshop, MICCAI 2019
Paper
Deep Learning Models for Tuberculosis Detection from Chest X-ray Images
Quang H Nguyen, Binh P Nguyen, Son D Dao, Balagopal Unnikrishnan, Rajan Dhingra, Savitha Rani Ravichandran, Sravani Satpathy, Palaparthi Nirmal Raja, Matthew CH Chua
26th International Conference on Telecommunications, ICT 2019
Paper
Learning of Multi-Dimensional Analog Circuits Through Generative Adversarial Network (GAN)
Rahul Dutta, Salahuddin Raju, Ashish James, Chemmanda John Leo, Yong-Joon Jeon, Balagopal Unnikrishnan, Chuan Sheng Foo, Zeng Zeng, Kevin Tshun Chuan Chai, Vijay R Chandrasekhar
IEEE International System-on-Chip Conference, SOCC 2019
Paper
Semi-supervised Deep Learning for Abnormality Classification in Retinal Images
Bruno Lecouat, Ken Chang, Chuan-Sheng Foo, Balagopal Unnikrishnan, James M Brown, Houssam Zenati, Andrew Beers, Vijay Chandrasekhar, Jayashree Kalpathy-Cramer, Pavitra Krishnaswamy
Machine Learning for Health (ML4H) Workshop, NeurIPS 2018
Paper

Volunteer Positions & Leadership

  • Distinguished Reviewer: IEEE Transactions in Medical Imaging (TMI)
  • Reviewer: Nature Digital Medicine
  • Reviewer: Nature Scientific Reports
  • Mentor: Graduate Application Assistance Program (GAAP)
  • Chairperson: Singapore Computer Society (SCS) Chapter - NUS ISS
  • Richard E Merwin Scholar - IEEE Computer Society
  • Youth Excellence Award for Most Promising Engineer - Kerala
  • Section Student Representative - IEEE Kerala Section
  • Chairperson: IEEE Computer Society CET Chapter
  • Co-Organizer: NASA Space Apps Challenge

Beyond Research

I love the sea, and I'm a certified scuba diver. P.S: If you ever need company to dive, drop me a message. Thanks to long layovers and commutes, I've been managing to read a bit. Here's my reading list if you are thinking "hmm, what should I read next?"