MEMP PhD Thesis Defense: Karim Kadry Abdelmeguid

Integrating Deep Learning, Spatial Computing, and Physics Simulations for Digital Cardiology

Biophysical modeling offers the potential for virtual interventions across a broad array of cardiovascular interventions and is increasingly important as device sophistication rises and inclusion criteria broaden. Virtual interventions have been increasingly integrated within digital cardiology workflows by solving partial differential equations on complex anatomical structures, enabling the extraction of clinically relevant treatment outcomes. In medical workflows, they can support the personalization of interventions to patient-specific anatomy; in engineering workflows, they can enable the evaluation of device designs across virtual cohorts; and in scientific workflows, they can reveal relationships between anatomic features and device outcomes. This thesis starts from the premise that virtual interventions, despite exhibiting clinical-grade accuracy, have yet to realize their full potential within digital workflows due to anatomical complexity. For example, simulation-ready anatomical models remain labor-intensive to reconstruct, limiting the scale of 3D datasets. Moreover, high-fidelity simulations remain too slow for rapid medical decision-making or device-design optimization. Lastly, anatomic variation between patients is complex, hampering the extraction of causal, rather than correlational, relationships between structure and device outcomes. I address these limitations by leveraging the latest advances in differentiable programming, spatial computing, deep learning, and physical simulation. First, I develop geometric processing and meshing tools to reconstruct simulation-ready virtual twins from multi-component coronary segmentations predicted through deep learning. Second, I develop a scalable virtual trial engine for patient-specific coronary angioplasty and train deep-learning models to rapidly emulate virtual interventions. Third, I develop anatomical diffusion models that are programmable with respect to geometry and topology, enabling counterfactual simulations that probe causal relationships between anatomical features and interventional outcomes. Together, these tools contribute towards digitally closing key feedback loops within cardiovascular medicine, engineering, and science.

Thesis Supervisor:
Elazer R. Edelman, MD, PhD
Edward J. Poitras Professor in Medical Engineering and Science, Brigham and Women’s Hospital & Harvard Medical School
Director, Center for Clinical Translational Research (CCTR), MIT

Thesis Committee Chair:
Polina Golland, PhD
Sunlin and Priscilla Chou Professor of Electrical Engineering and Computer Science, MIT

Thesis Readers:
Farhad Nezami, PhD
Assistant Professor of Surgery, Harvard Medical School

Vincent Sitzmann, PhD
Assistant Professor of Electrical Engineering and Computer Science, MIT
________________________________________________________________________________________

Zoom Invitation
Karim Kadry is inviting you to a scheduled Zoom meeting

Topic: Karim Kadry MEMP PhD Thesis Defense
Time: Monday, May 11, 2026, 3:00 PM Eastern Time (US and Canada)

Your participation is important to us: please notify hst [at] mit.edu (hst[at]mit[dot]edu), at least 3 business days in advance, if you require accommodations in order to access this event.

Join Zoom Meeting
https://mit.zoom.us/j/94843166766

One tap mobile
+16465588656,,94843166766# US (New York)
+16699006833,,94843166766# US (San Jose)

Meeting ID: 948 4316 6766

US: +1 646 558 8656 or +1 669 900 6833

International Numbers: https://mit.zoom.us/u/a2U1m7YsR

Join by SIP
94843166766 [at] zoomcrc.com (94843166766[at]zoomcrc[dot]com)

Join by Skype for Business
https://mit.zoom.us/skype/94843166766