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Department of Bioengineering Seminar Series-Promoting personalized digital medicine leveraging computational and bioinformatics tools: The novel perspective of mechanical biocompatibility

March 11 @ 12:00 pm - 1:00 pm

Shillman Hall, Room 105  (+ Google Map)

Department of Bioengineering

Farhad Nezami

Cardiovascular disease remains the leading cause of morbidity and mortality worldwide. Percutaneous intervention revolutionized approaches to atherosclerotic disease. However, the recent clinical failure of bioresorbable scaffolds not only have forced greater clinical scrutiny of great innovations but also questioned why these events were not foreseen.
The argument has been made that we have departed from the mechanistic perspective that drove the golden era of creativity in cardiovascular devices. Innovation in this space has long benefited from the integration of computational, benchtop and animal modeling, and recently from the introduction of machine-learning techniques. It is crucial to appreciate how computational modeling and bioinformatics approaches bring together the preclinical and clinical experiences and how the disappointment of recent technologies forces renewed concentration on subject-specific models and physics-governed computations.
In this talk, I will highlight how we incorporate powerful computational tools, and capitalize on the synergy between engineering, translational medicine, and informatics. This approach has provided new perspective into the contextual biocompatibility of medical implants relative to the mechanical environment and offers new views of technology innovation.
Dr. Farhad Rikhtegar Nezami is a Research Scientist and Project Leader at Harvard-MIT Biomedical Engineering Center at MIT. He received his PhD in Mechanical engineering from ETH, working on hemodynamics and drug transport in stented arteries. His research interests revolve around human pathophysiology, design and optimization of medical devices, and developing predictive/prognostic tools incorporating clinical data, computational tools, and machine-learning algorithms to drive progress from the bench and computational toolkit to the patient’s bedside.