Academy Overview
The Biomedical Summer Academy will introduce participants to key concepts of data science and artificial intelligence, showing how they can be leveraged in biomedical research and incorporated into grant proposals. Previous course topics have included introductions to Python programming, machine learning techniques, and use-case examples.
Participants are expected to bring a laptop for the academy
Light breakfast options will be available daily. A dedicated lunch reception is planned for Wednesday.
Topics
- Key concepts of data science
- Introduction to Python programming and its use in data science
- Machine learning techniques, including support vector machines, artificial neural networks, and deep learning
- Examples of biomedical research projects that leverage data science
- Incorporating data science into biomedical grant proposals
Curriculum and Academy Schedule
*Session dates and times may be subject to change
Click each section for more details
8:00 AM – 9:30 AM
Session 1: Welcome and Introduction to the Program
Presented by Kayvan Najarian
9:30 AM – 9:45 AM
Break
9:45 AM – 11:15 AM
Session 2: Math Foundations I – Brief Introduction to Mathematical Foundations of Machine Learning
Presented by Cristian Minoccheri
11:15 AM – 11:30 AM
Break
11:30 AM – 1:00 PM
Session 3: Math Foundations II – Brief Introduction to Mathematical Foundations of Macihne Learning
Presented by Cristian Minoccheri
1:00 PM – 2:00 PM
Lunch on your own
2:00 PM – 3:30 PM
Session 4: Clustering vs Classification; k-means; k-Nearest Neighbors
Presented by Kayvan Najarian
3:30 PM – 3:45 PM
Break
3:45 PM – 4:45 PM
Session 5: Introduction to Python Programming
Presented by Emily Wittrup
8:00 AM – 9:30 AM
Session 6: Simple Classification Methods and Feature Analysis
Presented by Kayvan Najarian
9:30 AM – 9:45 AM
Break
9:45 AM – 11:15 AM
Session 7: Linear Regression, Logistic Regression
Presented by Cristian Minoccheri
11:15 AM – 11:30 AM
Break
11:30 AM – 1:00 PM
Session 8: Using Machine Learning for Clinical and Health Applications I
Presented by Joseph Kohne
1:00 PM – 2:00 PM
Lunch on your own
2:00 PM – 3:30 PM
Session 9: Model Validation and Assessment
Presented by Kayvan Najarian
3:30 PM – 3:45 PM
Break
3:45 PM – 4:45 PM
Session 10: Using Machine Learning for Clinical and Health Applications II
Presented by Michael Mathis
8:00 AM – 9:30 AM
Session 11: Python Programming for Linear Regression, and Naïve Bayes
Presented by Emily Wittrup
9:30 AM – 9:45 AM
Break
9:45 AM – 11:15 AM
Session 12: Artificial Neural Networks I
Presented by Kayvan Najarian
11:15 AM – 11:30 AM
Break
11:30 AM – 1:00 PM
Session 13: Regression Trees
Presented by Kayvan Najarian
1:00 PM – 2:00 PM
Lunch Reception (provided)
2:00 PM – 3:30 PM
Session 14: Random Forest
Presented by Kayvan Najarian
3:30 PM – 3:45 PM
Break
3:45 PM – 4:45 PM
Session 15: Python Programming for Neural Networks, Regression Trees, and Random Forest
Presented by Emily Wittrup
UPDATED START TIME
8:30 AM – 9:30 AM
UPDATED SCHEDULE
Session 22: Using Machine Learning for Clinical and Health Applications IV
Presented by Nambi Nallasamy
9:30 AM – 9:45 AM
Break
9:45 AM – 11:15 AM
Session 17: Python Programming for Support Vector Machines
Presented by Emily Wittrup
11:15 AM – 11:30 AM
Break
11:30 AM – 1:00 PM
Session 18: Python Programming for Deep Learning
Presented by Emily Wittrup
1:00 PM – 2:00 PM
Lunch on your own
2:00 PM – 3:30 PM
Session 19: Using Machine Learning for Clinical and Health Applications III
Presented by Michael Sjoding
3:30 PM – 3:45 PM
Break
3:45 PM – 4:45 PM
Session 20: Deep Leaning I
Presented by Ken Reid
8:00 AM – 9:30 AM
Session 21: Deep Learning II
Presented by Ken Reid
9:30 AM – 9:45 AM
Break
9:45 AM – 11:15 AM
UPDATED SCHEDULE
Session 16: Support Vector Machines
Presented by Kayvan Najarian
11:15 AM – 11:30 AM
Break
11:30 AM – 1:00 PM
Session 23: Strategies to Add Data Science Flavor to Health Related Projects and Grant Proposals
Presented by Cristian Minoccheri
1:00 PM – 2:00 PM
Lunch on your own
2:00 PM – 3:30 PM
Session 24: Guidelines on Using Machine Learning for Clinical Applications
Presented by Michael Mathis
Additional Information
- Determine which data science / artificial intelligence techniques are appropriate for a given clinical application and apply them to their own clinical and/or research activities
- Develop strategies for integrating data science into their grant applications, work effectively with data scientists, and build new collaborations
- Utilize data science solutions and apply them to biomedical problems
- Apply a breadth of data science topics with data science experts as collaborators
- Skills to abstractly consider data science solutions and apply them to biomedical problems
- Receive a certificate of completion
We will send payment instructions to accepted applicants.
- Internal Participants (U-M Personnel and Students):
- $180 (Thanks to the support from the University that allows us to offer a deep discount for U-M employees and students)
- Other academic institutions and U-M alumni:
- $1,000
- External Participants:
- $3,000
This academy is open to all biomedical scientists, with the content geared towards junior faculty members and those from the public and private sectors who are interested in learning about incorporating data science and AI into their research.
Summer academies are designed with faculty, staff, and postdocs in mind. Students are also welcome to apply, though priority will be given to faculty, staff, and postdocs.
- More than 14 days before the first day: full refund minus $50 processing fee
- Cancellation between 7 and 14 days of the first day: 50% refund
- Less than 7 days: no refund
College-level math or statistics. No previous coding experience is required.
Central Campus Classroom Building (CCCB) Room 3460
1225 Geddes Ave.
Ann Arbor, MI 48109
Parking available nearby includes a parking structure for U-M Blue/Gold permit holders, located at 525 Church St., and metered street parking along Church St. There is also a public garage at 650 S. Forest Ave. View available public parking in Ann Arbor here and real time occupancy counts and public parking structures here.
Instructors
Lead Instructor
Kayvan Najarian, PhD
Professor of Computational Medicine and Bioinformatics, Emergency Medicine, and Electrical Engineering and Computer Science; MIDAS Associate Director; Director, Center for Data-Driven Drug Development and Treatment Assessment (DATA)
Bio
Dr. Kayvan Najarian is a Professor in the departments of Computational Medicine and Bioinformatics (DCMB), Emergency Medicine, and Electrical Engineering and Computer Science at the University of Michigan. He is the Director of the Biomedical and Clinical Informatics Laboratory and is an Associate Director for the Weil Institute for Critical Care Research and Innovation. Dr. Najarian is also an Associate Director for the Michigan Institute for Data & AI in Society (MIDAS), serving as the point person for data science collaboration in Biological Sciences and Health Sciences.
Dr. Najarian received his Ph.D. in Electrical and Computer Engineering from University of British Columbia, Canada, M.Sc in Biomedical Engineering from Amirkabir University, Iran, and B.Sc. in Electrical Engineering from Sharif University, Iran. The focus of Dr. Kayvan Najarian’s research is on the design of signal/image processing and machine learning methods to create computer-assisted clinical decision support systems that improve patient care and reduce the costs of healthcare.
Other Instructors
Joseph Kohne
Assistant Professor of Pediatrics, Medical School
Michael Mathis
Associate Professor, Anesthesiology
Cristian Minoccheri
Research Investigator, Computational Medicine and Bioinformatics; Adjunct Lecturer in Computational Medicine and Bioinformatics, Medical School
Nambi Nallasamy
Assistant Professor, Ophthalmology
Ken Reid
Data Scientist, MIDAS
Michael Sjoding
Associate Professor, Division of Pulmonary Critical Care
and Department of Internal Medicine
Emily Wittrup
Research Manager, Biomedical & Clinical Informatics Lab
ACCME Accreditation Designation
This activity has been planned and implemented in accordance with the accreditation requirements and policies of the Accreditation Council for Continuing Medical Education (ACCME) through the joint providership of the University of Michigan Medical School and the Michigan Institute for Data & AI in Society. The University of Michigan Medical School is accredited by the ACCME to provide continuing medical education for physicians.
The University of Michigan Medical School designates this live activity for a maximum of 34 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.
Questions? Contact Us.
Contact Faculty Training Program Manager, Kelly Psilidis at [email protected]