Lab Members
Michelle Akerman
- BME M.S. Student
Michelle is a BME M.S. student working on using unsupervised machine learning techniques to detect key features, such as microsaccades, in 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.
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.
Shubham Kaushal
- Data Science M.S. Student
Shubham is a Data Science M.S. student working on incorporating expert clinician eye movement data (e.g. fixation order) into training of Visual Transformer models. This work has the potential to improve the disease classification accuracy of such expert eye-movement informed deep learning models.
Omar Moussa
- Ophthalmology Resident
Omar Moussa is an Ophthalmology Resident at Columbia University Irving Medical Center interested in use of AI for detection of Age-Related Macular Degeneration. Earlier, he contributed to the group's work on developing a multimodal deep learning system for detection of late stages of AMD and for finding common AMD ocular biomarkers for both human experts and AI.
Pooja Mukund
- Biostatistics M.S. Student
Pooja is a Biostatistics M.S. student working on developing a deep learning segmentation model (inspired by the UNet architecture) to determine regions of interest in medical images using expert clinician eye movement annotations instead of conventional hand annotations.
Joel Salas
- Summer Student (Now at Rochester Institute of Technology)
Joel Salas was a summer student interested in pursuing a career in medicine who started at Rochester Institute of Technology in Fall 2022. In summer of 2022, he 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 and accuracy of glaucoma diagnosis.
Anurag Sharma
- BME M.S. Student
Anurag Sharma is a BME M.S. student interested in neuroscience and deep learning. He is working on developing approaches for extracting information from expert eye movement sequence data to train AI/deep learning systems.
Yifan Sun
- Financial Engineering M.S. Student
Yifan is a Financial Engineering M.S. student working on incorporating expert clinician eye movement data (e.g. fixation location/duration) into training of Visual Transformer models. This work has the potential to improve the disease classification accuracy of such expert eye-movement informed deep learning models.
Ye Tian
- BME M.S. Student
Ye is a BME M.S. student working on robust deep learning approaches inspired by Visual Transformers for detecting glaucoma progression in space and over time. He also spearheaded the group's past work on a multimodal deep learning model for detecting ocular and systemic vascular disorders, called 'DVT-Net.'
Geoffrey Wu
- CS Undergraduate Student
Geoffrey is a sophomore undergraduate student interested in deep learning applied to medicine. He is working on developing multimodal deep learning algorithms using a combination of retinal fundus images, vessel segmentation images (containing retinal vasculature information), and topological analysis of retinal vasculature to enable early detection of preeclampsia (a leading cause of morbidity among pregnant patients) from vascular signatures of the disease found in the eye.