Seminar Resources (Past Events)
Events
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Virtual Seminar with Rishi Kamaleswaran: “Forecasting Patient Trajectories: Leveraging Irregularly Sampled, Multimodal Time-Series for Clinical Foundation Models”
As in-patient environments generate vast amounts of heterogeneous data, ranging from continuous physiological telemetry to sparsely sampled medical imaging, there is a profound opportunity to translate this information into actionable clinical foresight. This talk will focus on the development of novel foundation models and transformer architectures designed to handle the temporal complexities of longitudinal hospital data.
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Center for Computational and Digital Health Innovation Demo Day
The Center for Computational and Digital Health Innovation will host a Center for Computational and Digital Health Innovation Demo Day, bringing together faculty, students, researchers, and members of the broader Duke and local community to experience interactive demonstrations of computational and digital health research.
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Virtual Seminar with Philipp Gutruf: “Wearable and Implantable Systems for Seamless High-Fidelity Diagnostics and Therapeutics”
Advances in materials and fabrication concepts for soft electronics coupled with miniaturization of wireless energy transfer enables the creation of high-performance electronic and optoelectronic systems with footprint and physical properties matched to biology. This talk explores the creation of such systems and discusses applications in the context of imperceptible body-worn devices for the assessment of physiology.
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Virtual Seminar with Ricardo Henao
Topic to be announced
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Virtual Seminar with David A. Bader: “High-Performance Graph Analytics for Motif Finding in Neuroscience Connectome Graphs and Beyond Using Arachne”
The growth of network-structured data across domains like neuroscience and cybersecurity demands scalable graph analytics, but complex tasks like subgraph isomorphism remain accessible only to high-performance computing (HPC) specialists. Arachne is an open-source framework that democratizes high-performance graph analytics through a Python interface while abstracting parallelism complexities.