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Kaveri a. Thakoor
Kaveri a. Thakoor
Principal Investigator
Dr. Kaveri Thakoor's Artificial Intelligence for Vision Science (AI4VS) laboratory is focused on transforming AI/deep learning systems into teammates for ophthalmologists by tackling key challenges currently inhibiting the translation of AI to the clinic, such as robustness, interpretability, and portability. Dr. Thakoor earned her Ph.D. in Biomedical Engineering from Columbia University in the City of New York as a National Science Foundation Graduate Research Fellowship recipient. Prior to that, she earned her B.S. with Honors in Chemistry from Stanford University and her M.S. in Computer Science from the University of Southern California. Dr. Thakoor worked for two years as a research staff member on the Earthquake Early Warning algorithm development team at the California Institute of Technology Seismological Laboratory before joining Columbia. She was awarded the 2022 Morton B. Friedman Memorial Prize for Doctoral Excellence by Columbia Engineering, and she received the 2022 Young Scientist Award for Graduate Students/Postdocs at the Northeast Bioengineering Conference.
Graduate Students
Ye Tian, Ph.D. in BME
Ye is a second-year Ph.D. student in Biomedical Engineering. He worked on deep learning in glaucoma progression detection using OCT images and visual fields.
Ye is working on deploying diffusion models in OCT super-resolution for portable applications.
Michael Lau, Ph.D. in CS
Michael is a first-year PhD student in Computer Science, and a recent MS Electrical Engineering graduate from Columbia University with an undergraduate degree in Computer Engineering from the University of Illinois at Urbana-Champaign.
His interests lies in developing robust algorithms for healthcare applications.
Akshay Raman, M.S. in CS
Akshay is a research assistant at AI4VS lab, and a recent Master's in Computer Science graduate from New York University.
He is currently developing multimodal models that integrate clinician eye-gaze data and text for improved diagnostic performance from medical imagery. His broader research interests lie in representation learning, reinforcement learning, and the application of AI to scientific problems.
Kavin Aravindhan Rajkumar, M.S. in CS
Kavin is a second-year Master’s student in Computer Science at Columbia University and holds a B.E. in Computer Science and Engineering from PSG College of Technology. At the AI4VS Lab, he focuses on developing multimodal AI systems for ophthalmology. His work integrates Vision Transformers, SIGLIP image encoders, and Gemma-3 LLMs to analyze OCT scans and produce interpretable, clinician-aligned diagnostic reports. He is also exploring gaze-guided graph neural networks and deferral learning strategies to enhance model interpretability, trustworthiness, and AI-clinician collaboration in real-world healthcare settings.
Pre-College Students
Matthew Shen
Matthew Shen is a high school student interested in the intersection of Artificial Intelligence and Ophthalmology.
He is currently working on the AI-READI dataset to analyze HbA1C levels with retinal OCTA data to predict diabetic retinopathy.
Visiting Scholars
Angela McCarthy
Angela is a medical student at the University of Connecticut and Visiting Research Fellow in the AI4VS Lab focused on the ethical considerations and clinical implementation of artificial intelligence in ophthalmology.
Her goal is to educate physicians and medical students on AI's potential and limitations in healthcare. Angela is involved in projects that explore the integration of AI into clinical practice, and she is currently leading a multi-center initiative on federated learning for the detection of thyroid eye disease.
Undergraduate Students
Aruzhan Abil, B.S. in CS
Aruzhan is a CS undergraduate student interested in multimodal machine learning for applications in medical imaging. Her work incorporates image segmentation encoders, Vision Transformers, and Graph Neural Networks.
In the lab, she is working on classifying blood glucose levels from retinal OCTA images in the AI-READI dataset.
Allison Cui, B.S. in CS
Allison is a CS undergraduate student interested in machine learning applications in the field of ophthalmology and medicine.
She is learning image classification techniques in the context of eye-movement data to use deep learning methods to detect glaucoma progression and predict future visual fields.
Residents and Clinicians
OMAR MOUSSA Ophthalmology Resident
Sophie Gu Ophthalmology Resident
Jedrzej Golebka Postdoctoral Researcher
COLLABORATORS
Associate Professor of Electrical Engineering
ANDREW LAINE
ANDREW LAINE
Percy K. and Vida L.W. Hudson Professor of Biomedical Engineering and Professor of Radiology (Physics)
ROYCE CHEN
ROYCE CHEN
Associate Professor of Ophthalmology
Website: Royce W.S. Chen, MD
Steve feiner
Steve feiner
Professor of Computer Science
Lab Alumni
2025
2025
Rosha Kenia (09/2023 - 05/2025), M.S. in Computer Science, now a PhD student at Harvard, worked on using Vision Transformer (ViT) models to capture the eye-tracking of clinicians as they view OCT reports.
Tri (Tom) Le ( - 05/2025), M.S. in Computer Science, now at Tiktok, worked on Mask Autoencoder and attention-based LSTM.
Rishabh Srivastava ( - 05/2025), M.S. in Computer Science, now at Orby AI.
Tharun Kumar Jayaprakash ( - 05/2025), M.S. in Computer Science, now an Embedded Firmware Engineer at Quantum Computing Inc, worked on physical integration for OCTDiff model used to super-resolve portable OCT images.
Shefali Shrivastava : ( - 05/2025), M.S. in Computer Science, now works at CVS.
Sanmati Choudhary: (2022 - 2025) B.S. in Computer Science.
Thanos Zisimopoulos: (2024 Summer - 05/2025) Visiting Scholar - Ophthalmology Resident and a second-year Master's student in Translational Engineering in Health and Medicine (in Greece).
2024
2024
Angel (Leyi) Cui, B.A. in CS : ( 03/2023 - 04/2025), B.A. in Computer Science, now a PhD student at CMU,, worked on training experts’ eye fixation data to understand glaucoma results and developing helpful tools for clinicians and medical students
2023
2023
Anurag Sharma (09/2022 - 06/2023), M.S. in Biomedical Engineering, worked on developing approaches for extracting information from expert eye movement sequence data to train AI/deep learning systems.
Michelle Akerman (09/2022 - 06/2023), M.S. in Biomedical Engineering, worked on using unsupervised machine learning techniques to detect key features, such as fixations and saccades, in expert clinician eye movements.
Yifan Sun (09/2022 - 04/2023), M.S. in Financial Engineering, worked on incorporating expert clinician eye movement data (e.g. fixation location/duration) into training of Vision Transformer.
Shubham Kaushal (09/2022 - 12/2023), M.S. in Data Science, worked on incorporating expert clinician eye movement data (e.g. fixation order) into training of Vision Transformer models.
Alice Wang (05/2023 - 08/2023), B.S. in Computer Science, worked on incorporating expert clinician eye tracking data into Self-Supervised machine learning models and Vision Transformer models.
2022
2022
Joel Salas (Summer 2022), now at Rochester Institute of technology, worked on developing a PsychoPy experiment to assess the impact of incorporating predictive AI into the clinical workflow by measuring the impact on clinician's speed, confidence, and accuracy of glaucoma diagnosis.
Geoffrey Wu ( ? - September 2022), B.S. in CS, worked on developing multimodal deep learning algorithms using a combination of retinal fundus images, vessel segmentation images, and topological analysis of retinal vasculature to enable early detection of preeclampsia from vascular signatures of the disease found in the eye.
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