Lab Members
Graduate Students
Ye Tian
- Ph.D. Student in Biomedical Engineering
Ye is a first-year Ph.D. student in Biomedical Engineering. He works on deep learning in glaucoma progression detection using OCT images and visual fields.
He also has interested in deploying diffusion models in OCT super-resolution for portable applications.
Michael Lau
- CS Ph.D. Student
Michael is 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.
Sanmati Choudhary
- CS Undergraduate Student
Sanmati is a CS undergraduate working on using unsupervised machine learning/deep learning techniques (such as categorical embeddings) to extract meaningful clusters from expert clinician eye movement data. By understanding the decision-making process of experts through their eye movements, her work has the potential to aid in medical education and in developing automated disease diagnosis systems.
Allison Cui
- CS Undergrad Student
Allison is a CS undergraduate student interested in machine learning applications in the field of ophthalmology and medicine.
She works on the Machine Learning Approach to Detect Subtle Differences between Normal and Anisometropic Eye Movements. She is also 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.
Angel (Leyi) Cui
- CS Undergrad Student
Angel is a CS undergraduate student interested in applying machine learning to the field of healthcare and designing and developing useful and usable software to aid clinicians and improve healthcare software.
She is working on training experts’ eye fixation data to understand glaucoma results and developing helpful tools for clinicians and medical students to facilitate learning and diagnostic processes.
Akshay Raman
- M.S. in CS
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.
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.
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.
Ramya Amancherla
- M.S. in CS
Ramya is a Master’s student in Computer Science at Columbia University with substantial experience in machine learning, deep learning, and computer vision, including work on research-driven and real-world AI systems. Her interests focus on multimodal learning and medical imaging, with a particular emphasis on developing robust, interpretable models that can support reliable decision-making in healthcare and other scientific domains.
Vaishnavi Varaghavenkatagiri
- M.S. in CS
Vaishnavi Varaghavenkatagiri is a Master's student in Computer Science at Columbia University (Dec 2026). She is interested in the intersection of deep learning and healthcare applications. At the AI for Vision Science Lab, Vaishnavi develops deep learning models to predict cardiac outcomes from retinal fundus images. Prior to Columbia, she worked as a Software Engineer at Walmart Global Tech, building scalable microservices and data pipelines.
Andrew Shin
Andrew is a medical student at Columbia interested in the applications of artificial intelligence in medicine.
He is working on a project aiming to develop machine learning models to predict outcomes of advanced cardiovascular imaging modalities using fundus imaging.
Benji Freeman
Benji is a fourth-year medical student at Columbia. He is interested in studying the impacts of artificial intelligence on physician workflow, diagnostic reasoning, and management decisions.
Currently characterizing ophthalmologists’ interactions with AI-guided diagnostic systems in neovascular AMD and glaucoma.
Xinxin Fang
- B.S. in Math
Xinxin Fang is a current sophomore studying Math, Statistics and CS at Columbia College
Anna Sun
Jedrzej Golebka
