Member Database

Michael Mathis


Associate Professor
Anesthesiology, Computational Medicine and Bioinformatics

BSE, Duke University, 2006
MD, University of Michigan Medical School, 2010
Anesthesia Residency, University of Michigan Medical School, 2014
Cardiothoracic Anesthesiology Fellowship, University of Michigan Medical School, 2015
NIH T32 Research Fellowship, University of Michigan Medical School, 2017

Michael Mathis, MD, is an Associate Professor of Anesthesiology in the Division of Adult Cardiac Anesthesia at U-M. After completing his undergraduate degree in Biomedical Engineering at Duke University in 2006, Dr. Mathis received his MD from U-M in 2010 and completed his residency in Anesthesiology and fellowship in Adult Cardiothoracic Anesthesiology at U-M. He also completed an NIH T32 research fellowship sponsored by U-M’s Department of Anesthesiology.

Mathis has research interests in improving perioperative care for patients with advanced cardiovascular disease, particularly for patients with heart failure. As part of the Multicenter Perioperative Outcomes Group (MPOG), an international consortium of perioperative databases for which U-M serves as the coordinating center, he serves as Associate Research Director and plays a lead role in integration of MPOG data with data from national cardiac and thoracic surgery registries. He also has interests in leveraging novel data science methods to understand patterns within highly granular intraoperative physiologic data, studying hemodynamic responses to surgical and anesthetic stimuli as a means for early detection of cardiovascular diseases such as heart failure.

Mathis is Affiliated Faculty within the Center of Computational Medicine and Bioinformatics (CCMB), member of the Michigan Integrated Center for Health Analytics & Medical Prediction (MiCHAMP), and member of the Institute for Health Policy and Innovation (IHPI). He has received research funding from the NIH, Department of Defense, and the BCBS Foundation of Michigan.


5 K01 HL171701-05: Early Diagnosis of Heart Failure: A Perioperative Data-Driven Approach, 5 R01 HL139672-04: Genetic and Genomic Analysis of Arterial Dysplasia, Novel Assessments of Intraoperative Determinants of Surgical Complications through Video Assessment, R01DK133226 Cardiac sURgery anesthesia Best practices to reduce Acute Kidney Injury (CURB-AKI), W81XWH-17-2-0012: A Multimodal Integrative Platform for Continuous Monitoring and Decision Support during Postoperative Care in Cardiac Patients

University Affiliation(s)


Research Area(s)

Clinical care | Clinical research | Data science / Analytics / AI | Health outcomes | Perioperative Medicine


  • Co-investigator of: The IN-STEP (INtegrating cardiac Surgery and anesthesiology To rEduce Pulmonary Complications) Study
  • Co-investigator of: The IN-STEP (INtegrating cardiac Surgery and anesthesiology To rEduce Pulmonary Complications) Study
  • Consultant on: Early Detection of Heart Failure in Preoperative Care Using an Artificial Intelligence Based Clinical Decision Support System
View all grants