Carnegie Mellon University

Center for Machine Learning and Health

“The innovations we foresee emerging from the Center for Machine Learning and Health at Carnegie Mellon University will inform better healthcare decisions. The CMLH goal is to be at the center of the digital healthcare revolution, with a focus on big data and scientific solutions for managing and improving healthcare through personalized, precision medicine. The researchers at Carnegie Mellon and our partners at UPMC Enterprises are uniquely qualified and determined to turn those ideas into reality.”


Andrew Moore
Dean, School of Computer Science
Carnegie Mellon University

What is the Center for Machine Learning and Health?

The Center for Machine Learning and Health (CMLH) at Carnegie Mellon University is one of two centers launched under the umbrella of the Pittsburgh Health Data Alliance, formed in 2015 to unite Carnegie Mellon’s unrivaled applied-computing capabilities, the University of Pittsburgh’s world-class health-sciences research, and UPMC’s clinical care and commercialization expertise. The CMLH supports great science and engineering that can lead to innovative health solutions and new businesses.

Four Areas of Focus

Personalized Medicine

The use of data analytics and genomics to provide tailored care to individuals based on their DNA, healthcare history, and environmental, economic, and behavioral factors.

Clinical Tools

The development of the next generation of medical devices and systems for use by medical staff and patients.

Healthcare Delivery

The provision of healthcare services to individuals and populations in hospitals and clinics, at home, and remotely via the internet.

Healthcare Commerce

The organizational and business aspects of providing health insurance and healthcare services at scale.

Project Funding

From bench to bedside: the CMLH funds projects that strive to bridge the gap between research and practice.  All funded work at CMLH will have a clear line of sight to commercial application. Although many CMLH projects will involve data analytics and machine learning, our approach is technology agnostic. We welcome proposals that involve human-computer interaction, language technologies, information systems, computer graphics, computer vision, artificial intelligence, robotics, electrical engineering, economics, psychology, sociology, public policy, business administration, law, design, and any other disciplines that apply to healthcare.


Funding is available at two levels. Awards for early-stage research projects are typically in the range of $200K – $400K. Significantly larger awards are available for multi-year, follow-on projects that have demonstrated clear commercial potential.


In addition to offering funding, CMLH provides support for funded projects in the form of datasets, access to patients and doctors for empirical validation of new concepts, and entrepreneurial mentorship.




Awards are intended to support research that transforms healthcare from transactional and experience-centered to data-driven. Once selected for funding, CMLH will help identify potential clinical and data-transaction partners and oversee progression to further funding where appropriate.


Proposal submissions are open only to Carnegie Mellon faculty in all units of the university. All proposals should be submitted via email to:

Project Highlights
Computational Modeling of Behavioral Rhythms to Predict Readmissions

Readmission after complex cancer surgery is common, with studies reporting between 15% and 50% of patients being readmitted within 30 days of discharge from the hospital. Readmissions are associated with increased healthcare costs, poor clinical outcomes including increased risk of infection and early mortality, and patient and family stress and suffering. Prior research has identified demographic and clinical predictors of readmission, but the role of patient-centered behavioral processes remains relatively unexplored. Identifying behavioral factors associated with readmission, which might include physical activity levels, sleep habits and social contacts, could highlight ways to prevent readmissions and empower patients to take a more active role in their recovery.


Anind Dey, Ph.D., Charles M. Geschke Director of the Human-Computer Interaction Institute at Carnegie Mellon University, and Carissa Low, Ph.D., Assistant Professor of Medicine and Psychology, Biobehavioral Oncology Program, University of Pittsburgh Cancer Institute, are collaborating to take a generalizable and scalable approach that holistically looks at patients’ behavior before and after surgery and identifies routines in this behavior. These routines will form the basis of their understanding of the predictive factors for readmissions, particularly while patients are still in the hospital after surgery or other treatments.


Harnessing technology to monitor these and other behavioral risks before surgery, during inpatient recovery, and during the critical transition from hospital to home will advance the field in a number of ways:


  • Reliably assessing and computationally modeling behavioral risks associated with readmissions to improve risk stratification
  • Identifying optimal targets and timing for behavioral intervention to reduce preventable readmissions
  • Providing data that may be helpful and motivating for patients and that could inform clinical decision making to improve quality of care
Clinical Genomics Modeling Platform

The Clinical Genomics Modeling Platform is an engine for easily building precision-medicine models for various diseases and populations. Triage algorithms, for instance, might help to determine if patients with a certain disease should be sent home with monitoring or sent to the intensive care unit. Carl Kingsford, Ph.D., and Christopher Langmead, Ph.D., both associate professors of computational biology with the School of Computer Science, are leading this development.


Their project focuses on the understanding of the relationship between genetic variation and medical outcomes in a large population, which is key to realizing the vision of personalized medicine. Efforts are now underway to obtain the genome sequences of thousands of individuals, and as the cost of sequencing continues to drop, it will become routine to sequence patients in a medical setting. However, a number of computational and practical challenges remain in the way of using genomic sequencing for clinical decision making.


In this project, Carl and Chris are aiming to increase the usage of predictive systems based on machine learning techniques and genomics through the development and commercialization of a computational system that will:


  • Make predictive models easy to build for clinical researchers
  • Make predictive models easy to share (sell) and apply
  • Make results of models easy to understand
Phylogenetic Models for Predicting Cancer Progression

Despite screening technologies and public health efforts that have improved the detection of early-stage cancers, methods to reliably predict which of these cancers will progress to more aggressive stages, metastasis, and ultimately patient mortality are lacking.


Russell Schwartz, Professor of Biological Sciences and Computational Biology, and Jian Ma, Associate Professor of Computational Biology, both with the School of Computer Science, are working on a suite of software tools that clinicians will use to improve cancer diagnosis and therapeutics based on the molecular signatures of the patient’s tumor genome.


The goal of the project is to develop novel models, algorithms, and software tools to better understand the origin and evolution of tumor cells and how a patient’s tumors are likely to progress. This information would contribute to personalized, precision treatment of cancer.

Detecting Intestinal Activity By Analyzing Gut Sounds

A non-invasive device that analyzes sounds from the intestinal tract could become a powerful new tool to help physicians diagnose and monitor a variety of gastric illnesses, such as acute pancreatitis, bowel obstructions, irritable bowel syndrome, inflammatory bowel disease, and Crohn’s disease.


Carnegie Mellon researchers, collaborating with UPMC gastroenterologists, will be developing, testing and performing clinical research on a wearable sensor array to detect intestinal sounds – a new vital sign for the ‘gut.’ Its impact could be substantial: in the U.S. alone, digestive diseases afflict 60-70 million people annually with treatment costs totaling more than $100 billion.


Using machine learning techniques, the researchers will develop a means of interpreting the acoustic signals to examine the determinants of gut activity and activity-suppression, the ability to identify gastric disorders on the basis of sounds, and the device’s therapeutic potential for enabling gut regulation via biofeedback. The project will also develop classification procedures that use these features to distinguish between the various types of gastrointestinal activity.


The research team includes George Loewenstein, Professor of Economics and Psychology, Department of Social and Decision Sciences and Ali Momeni, Associate Professor of Art, College of Fine Arts, along with Rich Stern, Professor Electrical & Computer Engineering, Max Gsell, Associate Professor Department of Statistics and Valerie Ventura, Associate Professor Department of Statistics.


Executive Director

Joe Marks

Joe Marks, Executive Director of CMLH, leads the strategic initiatives, business development, and negotiations for the Center. Joe brings a diverse background to CMLH that includes leadership roles at Disney Research and Mitsubishi Electric Research Labs (MERL), and involvement in multiple start-ups.


He earned his undergraduate and PhD degrees from Harvard University. His publications have focused mostly on topic areas that include computer graphics, human-computer interaction, and artificial intelligence, but he’s happy to work in new areas as the opportunity arises.

Chief Science Officer

Carl Kingsford

Carl Kingsford is an Associate Professor in the Computational Biology Department in the School of Computer Science at Carnegie Mellon University. His research group develops efficient algorithms for analysis of large-scale biological data, particularly high-throughout genomic sequencing experiments. He and his group are the authors of several widely used, open source software packages for genomic analysis. More information about his research can be found at He is the recipient of an NSF CAREER award, a Sloan Research Fellowship in Computational and Molecular Biology, and he is one of 14 Data Driven Discovery Investigators funded by the Gordon and Betty Moore Foundation selected from across all areas of science. He received his Ph.D. in Computer Science from Princeton University.

Carl Kingsford Photo_Feb 2017

Heather Johnson

Manager of Operations


Phone: 412-268-6750


Min Kyung Lee

Research Scientist


Phone: 412-268-9202


Nicole Flynn

Project Services Manager


Phone: 412-268-7559


James Ciuca

Development Officer for Commercialization


Phone: 412-268-5062


Ari Lightman

Marketing and Commercialization Advisor for the CMLH

Distinguished Service Professor of Digital Media and Marketing

Heinz College, Carnegie Mellon University


Phone: 412-268-9312

Andrew Moore, Dean of Carnegie Mellon University’s School of Computer Science, and Joe Marks serve on the CMLH Steering Committee with other members of the School of Computer Science and executives from UPMC. The CMLH Steering Committee includes stakeholders from both CMU and UPMC with a shared vision and goals for the work of the Center. The Steering Committee acts as the ultimate decision maker for organization and management issues for the center’s mission.

Contact Us

Main Phone Number: 412-268-6288