Webpage: https://icahn.mssm.edu/education/phd/biomedical-sciences/artificial-intelligence
Faculty: https://icahn.mssm.edu/education/phd/biomedical-sciences/artificial-intelligence/faculty
The Artificial Intelligence and Emerging Technologies in Medicine (AIET) concentration of the PhD Program in Biomedical Sciences at ISMMS offers students with solid quantitative and technical backgrounds educational and research opportunities in AI/machine learning, next generation medical technologies (medical devices, sensors, robotics, etc.), imaging, nanotechnology, information technology, and virtual/augmented reality simulation technologies for clinical applications or drug discovery. In addition to receiving foundational education in the use of information systems, students enrolled in the AIET training area will learn how to develop and interpret predictive diagnostic and therapeutic models using a variety of machine learning tools based on statistics and probability theory, drawing upon quantitative fields such as computer science, mathematics, theoretical physics, theoretical/computational chemistry, and digital engineering. AIET further leverages existing relationships with several well-regarded higher education institutions (State University of New York at Stony Brook, Rensselaer Polytechnic Institute (RPI), the Grove School of Engineering at the City College of New York, the Cooper Union - Albert Nerken School of Engineering, and the Hasso Plattner Institute of the University of Potsdam, in Germany) to offer complementary technical expertise to expand collaborative research and enrichment opportunities for trainees and faculty.
Hayit Greenspan, PhD
212-824-8494
Alan C. Seifert, PhD
212-824-8440
Core: Systems Biomedicine (includes Matlab Bootcamp and Journal Club) (BSR1800, 8.5 credits)
Programming: Python (BDS1007, 1 credit)
Programming: Intro to R (BIO6300, 2 credits)
RCR: Responsible Conduct of Research (BSR1021, 0.5 credits)
WIP: Works in Progress Seminar in AIET (BSR5912, 1 credit)
Research: Lab Rotation (BSR1006, 4 credits)
Core: Principles of Physiology and Pharmacology (includes Journal Club) (BSR1802, 3.5 credits)
Biostat: Applied Biostatistics for Biomedical Research (BSR1026, 3 credits)
RCR: Rigor and Reproducibility (BSR1022, 0.5 credits)
WIP: Works in Progress Seminar in AIET (BSR5913, 1 credit)
Research: Lab Rotation (BSR1007, 4 credits)
Advanced Electives: Advanced electives, depending on student’s needs (must total to 6 credits in Year 2)
Seminar: Seminars in AIET (BSR5910/BSR5911, 1 credit)
Journal Club: Journal Club in AIET (BSR4910/BSR4911, 1 credit)
WIP: Works in Progress Seminar in AIET (BSR5912/BSR5913, 1 credit)
Research: Independent Research (BSR8000, 10 credits)
(Must complete qualifying exam/thesis proposal by June 30 of Year 2)
Additional advanced electives if appropriate for the student’s needs
Seminar: Seminars in AIET (BSR5910/BSR5911, 1 credit)
WIP: Works in Progress Seminar in AIET (BSR5912/BSR5913, 1 credit)
Research: Dissertation Research (BSR9000, 8 credits)
Systems Biology: Biomedical Modeling (BSR1803, 3 credits, strongly recommended for all students)
Introduction to AI & Deep Learning in Medical Imaging (recommended, particularly for students undertaking dissertation research in AI/ML)
Any other electives offered by AIET, any other MTA, through any relationship with an outside institution such as the Hasso Plattner Institute or Rensselaer Polytechnic Institute, or even through other institutions, can be appropriate if agreed upon by the student, dissertation advisor, and MTA co-directors. Students are encouraged to take advantage of this flexibility and choose advanced electives that are most relevant to their dissertation research and training goals.
Introduction to Biophysics and Biophysical Instrumentation (BSR6901, 1.5 credits)
Introduction to Nanomedicine (BSR 0907, 3 credits)
The Systems Biomedicine core (BSR1800/1802) can be exchanged for the Biomedical Science curriculum (BSR1012/1013) for students with a particularly strong quantitative background but relatively little background in basic biology.
BIO6400 can fulfill the biostatistics requirement but may result in an unacceptably heavy workload in Y1 Fall semester if combined with the Systems Biomedicine core (BSR1800/1802). This option will only be appropriate in rare and exceptional cases.
Advanced electives should be taken in Y2; it is strongly discouraged to undertake advanced electives before completing the core curriculum and identifying a dissertation advisor and lab.
MD/PhD students will have completed their MD/PhD-specific core curriculum in MD Y1, so the curriculum for MD/PhD students will be identical to the above, with the following exceptions:
‘Core’ will be dropped
Journal club and Seminar will be added in PhD Y1
Advanced electives may be taken in Y1 if a dissertation advisor and lab has been identified