Multi-disciplinary research


We are interested in developing machine learning methods and systems using heterogeneous real-world data for applications in health. This includes electronic health records data and medical imaging for medical diagnostics and prognostics. Methodologies of interest pertain to multi-modal fusion, representation learning, and augmenting machine and human decision-making, to achieve high performance and utility in clinical practice.

Our research agenda consists of two interrelated tracks:

Projects that we lead

Privacy-preserving Intra-City Collaborative Learning for Health Screening Applications

Grant period: 2022-2024

Sponsor: NYUAD Center for Interacting Urban Networks & Center for Cybersecurity

Collaborators: Michail Maniatakos (NYUAD Engineering) & Mariam Al Harbi (Abu Dhabi Health Services)

EyeScore: Leveraging Cross-modal Associations between Clinical Data and Optical Imaging for Stroke Recurrence Risk Prediction

Grant period: 2022-2024

Sponsor: NYUAD Research Enhancement Fund

Collaborators: Bartlomiej Piechowski (Cleveland Clinic Abu Dhabi) & Farokh Atashzar (NYU Tandon School of Engineering)

Abu Dhabi Precision Medicine Virtual Research Institute

Grant period: 2022-2026

Sponsor: ASPIRE

Collaborators: Coming soon!

Projects that we collaborate on

Tailoring Breast Cancer Diagnosis Workflow with Deep Neural Networks

Grant period: 2020-2022

Sponsor: Gordon and Betty Moore Foundation

Collaborators: Krzysztof Geras (NYU Grossman School of Medicine)

Deep Learning for Robotic Human Augmentation

Grant period: 2022-2027

Sponsor: NYUAD Center for Artificial Intelligence & Robotics

Collaborators: Farokh Atashzar (NYU Tandon School of Engineering) & Tuka Al Hanai (NYUAD Engineering)

Enhancing the Conservation of Coral Reefs through Vision-based Health Monitoring

Grant period: 2022-2027

Sponsor: NYUAD Arabian Center for Climate and Environmental Sciences

Collaborators: John Burt (NYUAD Biology), Shady Amin (NYUAD Biology), & Anthony Tzes (NYUAD Engineering)