Electrical and Computer Engineering
Associate Professor, Electrical and Computer Engineering
Member, Rice Digital Health Initiative and Houston Methodist-Rice Digital Health Institute
Member, Ken Kennedy Institute
Member, Institute of Health Resilience and Innovation
Average rating
3.7
16 temporary mock ratings
Difficulty
3.3
course-linked average
Courses
6
in seeded sections
Dr. Sano's research focuses on machine learning, digital health, mobile health, affective computing, ubiquitous computing, behavioral science, adaptive systems, artificial intelligence, human/computer interaction, intelligent systems, biomedical health informatics, digital phenotyping, just-in-time interventions, personalized medicine
COMP 390
Theoretical and experimental investigations under staff direction. Repeatable for Credit.
ELEC 482
This course introduces the latest artificial intelligence technology for healthcare. The class materials focus on methods, systems, applications, challenges, and ethical considerations. Through lectures, discussions, case studies, assignments, and hands-on projects, students will gain a comprehensive understanding of AI technologies in healthcare, from medical imaging and clinical decision support to population health management and drug discovery. Additionally, the course will include discussions about ethical, legal, and social implications of AI and its potential applications in healthcare. Cross-list: ELEC 509. Mutually Exclusive: Cannot register for ELEC 482 if student has credit for ELEC 509.
ELEC 490
Theoretical and experimental investigations under staff direction. A research project plan should be prepared and approved by the faculty member advising the project. Information about ELEC 490 project plans is available on the ECE Web site on the Academics section under ECE forms. May be repeated for a total of 6 credit hours for undergraduates. Repeatable for Credit.
ELEC 509
This course introduces the latest artificial intelligence technology for healthcare. The class materials focus on methods, systems, applications, challenges, and ethical considerations. Through paper reading, lectures, discussions, case studies, assignments, and hands-on projects, students will gain a comprehensive understanding of AI technologies in healthcare, from medical imaging and clinical decision support to digital twins and intervention development. Additionally, the course will include discussions about ethical and social implications of AI and its potential applications in healthcare. Additional coursework required beyond the undergraduate course requirements. Cross-list: ELEC 482. Mutually Exclusive: Cannot register for ELEC 509 if student has credit for ELEC 482.
ELEC 590
Theoretical and experimental investigations under staff direction. Repeatable for Credit.
ELEC 800
Repeatable for Credit.
