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Registration deadline: 11:59pm ET, Thursday, May 9, 2024.

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 will come away from the summer academy with the necessary skills to abstractly consider data science solutions and apply them to biomedical problems. The summer academy is also a rich opportunity for interdisciplinary networking and research collaboration.

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

Academy Details

Outcomes

  • 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

Tuition Cost

We will send payment instructions to accepted applicants.

  • $3,000 for external participants (30% discount for U-M Alums)
  • Thanks to support from the University, we are able to offer a reduced price of $100 for U-M personnel and students

Who Should Attend

This academy workshop is open to all U-M and external biomedical scientists, but the content is geared towards junior faculty members and those from the public and private sector who are interested in learning about incorporating data science into their research.

Cancellation Policy

  • 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

Prerequisites

College-level math or statistics. No previous coding experience is required.

Location

TBA – In-person at U-M Ann Arbor campus

Lead Instructor

Dr. Najarian

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 and Research

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 Science (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

Michael Mathis

Michael Mathis

Associate Professor,
Anesthesiology

 

Cristian Minoccheri

Research Investigator, Computational Medicine and Bioinformatics and Adjunct Lecturer in Computational Medicine and Bioinformatics, Medical School

Nambi Nallasamy

Assistant Professor,
Ophthalmology

Ken Reid

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

Academy Schedule

*Session dates and times may be subject to change

8:00am – 9:30am

Session 1: Welcome and Introduction to the program

Presented by: Kayvan Najarian


9:30am – 9:45am

Break


9:45am – 11:15am

Session 2: Math Foundations I – Brief Introduction to Mathematical Foundations of Machine Learning

Presented by: Cristian Minoccheri


11:15am – 11:30am

Break


11:30am – 1:00pm

Session 3: Math Foundations II – Brief Introduction to Mathematical Foundations of Machine Learning

Presented by: Cristian Minoccheri


1:00pm – 2:00pm

Lunch Break (lunch will be provided)


2:00pm – 3:30pm

Session 4: Clustering vs Classification; k-means; k-Nearest Neighbors

Presented by: Kayvan Najarian


3:30pm – 3:45pm

Break


3:45pm – 5:15pm

Session 5: Introduction to Python Programming

Presented by: Emily Wittrup

8:00am – 9:30am

Session 6: Simple Classification Methods and Feature Analysis

Presented by: Kayvan Najarian


9:30am – 9:45am

Break


9:45am – 11:15am

Session 7: Linear Regression, Logistic Regression

Presented by: Cristian Minoccheri


11:15am – 11:30am

Break


11:30am – 1:00pm

Session 8: Model Validation and Assessment

Presented by: Kayvan Najarian


1:00pm – 2:00pm

Lunch Break (lunch will be provided)


2:00pm – 3:30pm

Session 9: Using Machine Learning for Clinical and Health Applications I

Presented by: Kayvan Najarian


3:30pm – 3:45pm

Break


3:45pm – 5:15pm

Session 10: Using Machine Learning for Clinical and Health Applications II

Presented by: Michael Mathis

8:00am – 9:30am

Session 11: Python Programming for Linear Regression, Logistic Regression; Ridge Regression and Naïve Bayes

Presented by: Emily Wittrup


9:30am – 10:45am

Break


10:45am – 11:15am

Session 12: Artificial Neural Networks I

Presented by: Kayvan Najarian


11:15am – 11:30am

Break


11:30am – 1:00pm

Session 13: Regression Trees

Presented by: Kayvan Najarian


1:00pm – 2:00pm

Lunch Break (lunch will be provided)


2:00pm – 3:30pm

Session 14: Random Forest

Presented by: Kayvan Najarian


3:30pm – 3:45pm

Break


3:45pm – 5:15pm

Session 15: Python Programming for Neural Networks, Regression Trees and Random Forest

Presented by: Emily Wittrup

8:00am – 9:30am

Session 16: Support Vector Machines

Presented by: Kayvan Najarian


9:30am – 9:45am

Break


9:45am – 11:15am

Session 17: Python Programming for Support Vector Machines

Presented by: Emily Wittrup


11:15am – 11:30am

Break


11:30am – 1:00pm

Session 18: Using Machine Learning for Clinical and Health Applications III

Presented by: Michael Sjoding


1:00pm – 2:00pm

Lunch Break (lunch will be provided)


2:00pm – 3:30pm

Session 19: Deep Learning I

Presented by: Ken Reid


3:30pm – 3:45pm

Break


3:45pm – 5:15pm

Session 20: Deep Learning II

Presented by: Ken Reid

8:00am – 9:30am

Session 21: Python Programming for Deep Learning

Presented by: Emily Wittrup


9:30am – 9:45am

Break


9:45am – 11:15am

Session 22: Guidelines on Using Machine Learning for Clinical Applications

Presented by: Nambi Nallasamy


11:15am – 11:30am

Break


11:30am – 1:00pm

Session 23: Strategies to Add Data Science Flavor to Health Related Projects and Grant Proposals

Presented by: Kayvan Najarian


1:00pm – 2:00pm

Lunch Break (lunch will be provided)


2:00pm – 3:30pm

Session 24: Using Machine Learning for Clinical and Health Applications IV

Presented by: Michael Mathis

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 Science. 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 XX AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Create a MiCME Account to Claim CME Credit

Questions?

Contact Faculty Training Program Manager, Kelly Psilidis at psilidis@umich.edu