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Biomedical Data Science Summer Academy 2026

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Add to Calendar Biomedical Data Science Summer Academy 2026 6/22/2026 8:00:00 AM 6/26/2026 4:00:00 PM America/New_York For More Details: https://umich.cloud-cme.com/course/courseoverview?EID=93322 Description: Open to all U-M and external biomedical scientists. Trainees will learn among an interdisciplinary cohort the key concepts of data science and applied solutions to biomedical problems. Participants will learn supervised and unsupervised machine learning as well as deep learning for clinical applications. They will be able to determine which data science/artificial intelligence techniques are appropriat... Central Campus Classroom Building (CCCB) false MM/DD/YYYY


Date & Location
Monday, June 22, 2026, 8:00 AM - Friday, June 26, 2026, 4:00 PM ET, Central Campus Classroom Building (CCCB), Ann Arbor, MI

Target Audience
Professions - Physician, Scientist

Credits
AMA PRA Category 1 Credits™ (31.50 hours), Non-Physician Attendance (31.50 hours)

Overview
Open to all U-M and external biomedical scientists. Trainees will learn among an interdisciplinary cohort the key concepts of data science and applied solutions to biomedical problems. Participants will learn supervised and unsupervised machine learning as well as deep learning for clinical applications. They will be able to 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. They will also develop strategies for integrating data science into their grant applications, work effectively with data scientists, and build new collaborations

Objectives
At the conclusion of this activity, learners will be able to:

  1. 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.
  2. Develop strategies for integrating data science into their grant applications, work effectively with data scientists, and build new collaborations.
  3. Utilize data science solutions and apply them to biomedical problems.

Accreditation

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 MIDAS - Michigan Institute for Data & AI in Society.

Credit Designation
AMA PRA Category 1 
The University of Michigan Medical School designates this Live Activity for a maximum of 31.50 AMA PRA Category 1 Credit(s)™.  Physicians should claim only the credit commensurate with the extent of their participation in the activity.


Additional Information

Accessibility Statement
 
The University of Michigan Medical School is committed to ensuring that its programs, services, goods and facilities are accessible to individuals with disabilities as specified under Section 504 of the Rehabilitation Act of 1973 and the Americans with Disabilities Amendments Act of 2008.  If you have needs that require special accommodations, including dietary concerns, please contact the CME Activity Coordinator.

Questions? Please contact Janet Gribbons or Kelly Psilidis.



Keywords: LIVEAMAFeatured

Mitigation of Relevant Financial Relationships

The University of Michigan Medical School adheres to the ACCME’s Standards for Integrity and Independence in Accredited Continuing Education. All individuals in a position to control the content of a CE activity, including faculty, planners, or others are required to disclose all relevant financial relationships with ineligible entities. All relevant financial relationships have been mitigated prior to the commencement of the activity.

Member Information
Role in activity
Nature of Relationship(s) / Name of Ineligible Company(s)
Janet Gribbons, Other
Training Program Specialist
MIDAS
Activity Coordinator
Non-Clinical Exception
Jing Liu, PhD
Educational Co-Planner
Non-Clinical Exception
Kayvan Najarian, PhD
University of Michigan - Ann Arbor
Educational Planner, Faculty
Non-Clinical Exception
Keith D Aaronson, MD
University of Michigan
Faculty
Non-Clinical Exception
Xiaosu Hu, PhD
Data Scientist
Michigan Institute for Data and AI in Society (MIDAS)
Faculty
Non-Clinical Exception
Michael R Mathis, MD
University of Michigan Medicine
Faculty
Non-Clinical Exception
Nambi Nallasamy, MD
University of Michigan
Faculty
Non-Clinical Exception
Emily Wittrup, MS
Senior Computational Biologist
University of Michigan
Faculty
Non-Clinical Exception

Monday, June 22, 2026
Session 1: Welcome & Introduction to the Program
8:00AM - 9:30AM
Kayvan Najarian, PhD
Session 2: Clustering vs Classification; k-means; k-Nearest Neighbors
9:45AM - 11:15AM
Kayvan Najarian, PhD
Session 3: Simple Classification Methods and Feature Analysis
11:30AM - 1:00PM
Kayvan Najarian, PhD
Session 4: Introduction to Python Programming
2:00PM - 3:30PM
Emily Wittrup, MS
Tuesday, June 23, 2026
Session 5: Linear Regression, Logistic Regression
8:00AM - 9:30AM
Kayvan Najarian, PhD
Session 6: Model Validation and Assessment
9:45AM - 11:15AM
Kayvan Najarian, PhD
Session 7: Artificial Neural Networks
11:30AM - 1:00PM
Kayvan Najarian, PhD
Session 8: Python Programming for Linear Regression, and Naive Bayes
2:00PM - 3:30PM
Emily Wittrup, MS
Session 9: Using Machine Learning for Clinical and Health Applications I
3:45PM - 4:45PM
Nambi Nallasamy, MD
Wednesday, June 24, 2026
Session 10: Regression Trees/Random Forest
8:00AM - 9:30AM
Kayvan Najarian, PhD
Session 11: Support Vector Machines
9:45AM - 11:15AM
Kayvan Najarian, PhD
Session 12: Python Programming for Neural Networks, Regression Trees, and Random Forest
11:30AM - 1:00PM
Emily Wittrup, MS
Session 13: Python Programming for Support Vector Machines
2:00PM - 3:00PM
Emily Wittrup, MS
Session 14: Using Machine Learning for Clinical and Health Applications II
3:15PM - 4:45PM
Xiaosu Hu, PhD
Thursday, June 25, 2026
Session 15: Deep Learning I
8:00AM - 9:30AM
Kayvan Najarian, PhD
Session 16: Deep Learning II
9:45AM - 11:15AM
Kayvan Najarian, PhD
Session 17: Python Programming for Deep Learning
11:30AM - 1:00PM
Emily Wittrup, MS
Session 18: Using Machine Learning for Clinical and Health Applications III
2:00PM - 3:00PM
Michael R Mathis, MD
Session 19: Guidelines on Using Machine Learning for Clinical Applications
3:15PM - 4:45PM
Michael R Mathis, MD
Friday, June 26, 2026
Session 20: Strategies to Add Data Science Flavor to Health Related Projects and Grant Proposals
8:00AM - 9:30AM
Kayvan Najarian, PhD
Session 21: LLMs and Applications of Generative Models
9:45AM - 11:15AM
Kayvan Najarian, PhD
Session 22: Using Machine Learning for Clinical and Health Applications IV
11:30AM - 1:00PM
Keith D Aaronson, MD

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