ICON Seminar in Learning: Prof. Miad Faezipour (Purdue SOET)
| Event Date: | February 24, 2023 |
|---|---|
| Speaker: | Prof. Miad Faezipour |
| Speaker Affiliation: | Purdue SOET |
| Time: | 3:30-4:45 pm |
| Location: | ARMS 1109
Zoom Link: https://purdue-edu.zoom.us/j/99326083199?pwd=TjhqaE1TYkZ2RG1RMkt6S1JZbGk1UT09 |
| Contact Name: | Junjie Qin |
| Contact Email: | jq@purdue.edu |
| Priority: | No |
| School or Program: | College of Engineering |
| College Calendar: | Show |
Location: ARMS 1109 (in-person) and Zoom (online) https://purdue-edu.zoom.us/j/99326083199?pwd=TjhqaE1TYkZ2RG1RMkt6S1JZbGk1UT09
Agenda: 3:30pm-4:45pm (Seminar + Q&A); 4:45pm-5:030pm (Networking). Snacks and coffee will be provided.
Digital Healthcare – Design for Interpretability and Embedded Intelligence
Abstract
While AI in healthcare has transformed complex diagnostic procedures, and labor- intensive/repetitive bio-informatic tasks, design for medical interpretability remains an open problem that has had limited progress. There is a high demand for automation in the processing, analysis, and augmentation of the biomedical data for designing intelligent decision support Internet of Medical Things (IoMT) systems as well as for high reliability and interpretability of their machine learning-based inferences. Moreover, there is a pressing need for embedded/on-device bio-data analysis in evolving medical and healthcare applications, especially for real-time, urgent, and intensive care cases where decisions based on data processing have to be made immediately. My research is centered around artificial intelligence in healthcare with a focus on designing advanced machine learning and signal processing algorithms that are efficiently implementable in-situ, and optimized from both data constraint and time complexity perspectives. I also address the medical interpretability requirement by integrating domain expert knowledge into the algorithmic design pipeline. My talk spans various D-BEST (Digital/Biomedical Embedded Systems and Technology) lab research areas, mainly covering three emerging healthcare applications pertaining to the heart, lung and brain, with underlying bases for implementation and augmentation in embedded IoMT systems and/or edge devices. The premises ideas call for further data dimensionality augmentation and medical data interpretability using the integration of domain knowledge with advanced machine learning techniques.
Bio
Dr. Miad Faezipour is an associate professor of electrical and computer engineering technology at the School of Engineering Technology, Purdue University. She is the founder and director of the Digital/Biomedical Embedded Systems and Technology (D-BEST) research laboratory. Prior to joining Purdue University, she has served the Computer Science & Engineering and Biomedical Engineering programs of the University of Bridgeport, Connecticut, as a faculty member for ten years. She received the B.Sc. degree in electrical engineering from the University of Tehran, Iran, and the M.Sc. and Ph.D. degrees in electrical engineering from the University of Texas at Dallas. She has also been a Postdoctoral Research Associate at the University of Texas at Dallas collaborating with the Center for Integrated Circuits and Systems and the Quality of Life Technology laboratories. Her research interests primarily include healthcare technology with embedded intelligence, digital/biomedical embedded hardware/software co-designs, biomedical signal/image processing, computer vision, computational neuroscience, healthcare/biomedical informatics, artificial intelligence and AI-based bio-data augmentation. She is a Senior Member of IEEE, EMBS and the IEEE Women in Engineering.
Recording:
2023-02-24 15:30:00 2023-02-24 16:45:00 America/Indiana/Indianapolis ICON Seminar in Learning: Prof. Miad Faezipour (Purdue SOET) Digital Healthcare; Design for Interpretability and Embedded Intelligence ARMS 1109 Zoom Link: https://purdue-edu.zoom.us/j/99326083199?pwd=TjhqaE1TYkZ2RG1RMkt6S1JZbGk1UT09