Machine Learning in Health and Biomedicine - Guest Editors

Dr. Atul Butte

Guest Editor, PLOS Medicine Machine Learning in Health and Biomedicine

Atul Butte, MD, PhD is the Priscilla Chan and Mark Zuckerberg Distinguished Professor and inaugural Director of the Institute for Computational Health Sciences ( at the University of California, San Francisco (UCSF). Dr. Butte is also the Executive Director for Clinical Informatics across the six University of California Medical Schools and Medical Centers. 

Dr. Butte has authored over 200 publications, with research repeatedly featured in the New York Times, Wall Street Journal, and Wired Magazine. Dr. Butte was elected into the National Academy of Medicine in 2015, and in 2013, he was recognized by the Obama Administration as a White House Champion of Change in Open Science for promoting science through publicly available data. Other recent awards include the 2014 E. Mead Johnson Award for Research in Pediatrics, 2013 induction into the American Society for Clinical Investigation, and the 2011 National Human Genome Research Institute Genomic Advance of the Month.  Dr. Butte is also a founder of three investor-backed data-driven companies: Personalis, providing medical genome sequencing services, Carmenta (acquired by Progenity), discovering diagnostics for pregnancy complications, and NuMedii, finding new uses for drugs through open molecular data. Dr. Butte is a principal investigator of three major programs: the California Initiative to Advance Precision Medicine; ImmPort, the clinical and molecular data repository for the National Institute of Allergy and Infectious Diseases; and the California Precision Medicine Consortium, helping recruit tens of thousands of participants into President Obama's Precision Medicine Initiative. Dr. Butte trained in Computer Science at Brown University, worked as a software engineer at Apple and Microsoft, received his MD at Brown University, trained in Pediatrics and Pediatric Endocrinology at Children's Hospital Boston, then received his PhD from Harvard Medical School and MIT.

Competing Interests: Dr. Butte in a scientific founder and/or advisory board member for Genstruct, NuMedii, Personalis, and Carmenta. He has received honoraria for talks, has consulted, and has corporate relationships with a number of biotechnology, research, healthcare, and financial companies, and some of his students have founded biotechnology and healthcare companies. Dr. Butte’s full list of competing interests can be found here.

Dr. Suchi Saria

Guest Editor, PLOS Medicine Machine Learning in Health and Biomedicine

Dr. Suchi Saria, is the John C. Malone assistant professor of computer science, health policy and statistics, the Director of the Machine Learning and Healthcare Lab, and the Research Director of the Malone Center for Engineering in Healthcare at Johns Hopkins University. She is on the editorial board of the Journal of Machine Learning Research. 

Dr. Saria is considered a leading expert in machine learning and its use in developing next generation models for healthcare delivery. Her work has led to novel approaches for individualizing decision-making from heterogeneous noisy data sources as well as their use in improving care for diverse populations. In 2015, she was recognized by the IEEE Intelligent Systems as Artificial Intelligence’s “10 to Watch”. In 2016, she received the DARPA Young Faculty award and was named to Popular Science’s “Brilliant 10.” In 2017, she was named to MIT Technology Review’s ‘35 Innovators under 35’ (TR35). In 2018, she received the prestigious Sloan Research award and was selected as a World Economic Forum Young Global Leader. She has given over 80 invited talks including invited presentations at the National Academy of Sciences, National Academy of Engineering, and the White House Frontier’s Meeting hosted by President Obama.


Competing Interests: Dr. Saria is an investor and technical advisor to PatientPing

Dr. Aziz Sheikh

Guest Editor, PLOS Medicine Machine Learning in Health and Biomedicine

Dr. Aziz Sheikh is Professor of Primary Care Research & Development and Director of the Usher Institute of Population Health Sciences at The University of Edinburgh. He is Honorary Consultant in Paediatric Allergy at the Royal Hospital for Sick Children in NHS Lothian. He is Co-Director of the NHS Digital Academy and is an investigator in the Farr Institute, Health Data Research UK and the NHS Global Digital Exemplars.  He was part of the Wachter Review of NHS England’s IT Strategy and a member of the Medical Research Council’s Scientific Leadership Team on Health Informatics. He serves on the Scottish Government’s Digital Health and Care Strategy Strategic Oversight Group. Dr. Sheikh chairs the World Innovation Summit for Health (WISH) on Data Science and Artificial Intelligence.

He has given keynote and plenary presentations in over 40 countries and has won numerous national fellowships and awards including a Harkness Fellowship in Health Policy and Practice based at Brigham and Women’s Hospital/Harvard Medical School. He held the position of Visiting Professor of Medicine at Harvard Medical School (2014-16) and currently holds visiting chairs at the University of Birmingham (UK), Queen Mary’s University of London (UK) and Maastricht University (the Netherlands). He is Co-Director of Harvard Medical School’s Safety, Quality, Informatics Leadership (SQIL) Program.

Dr. Sheikh has been honored with fellowships from 8 learned societies, including the Fellowship of the Royal Society of Edinburgh, Fellowship of the UK’s Academy of Medical Sciences, Fellowship of the American College of Medical Informatics and a Founding Fellow of the Faculty of Clinical Informatics. 

Dr. Sheikh was made an Officer of the Order of the British Empire for Services to Medicine and Health Care by Her Majesty Queen Elizabeth II in 2014. 

Competing Interests: Dr. Sheikh has relevant research grants from: Chief Scientist’s Office of the Scottish Government, Department of Health, Farr Institute, Health Data Research UK, National Institute of Health Research and NHS England and he chairs the World Innovation Summit for Health (WISH) Forum on Data Science and Artificial Intelligence.

Dr. Quaid Morris

Guest Editor, PLOS Computational Biology Machine Learning in Health and Biomedicine


Proteomics and functional genomics datasets are integrated in order to define and stratify cancers according to their distinctive molecular signatures. Underlying signal transduction and metabolic networks, and their regulation by post-translational modifications, are then molecularly characterized for their contributions to cancer phenotypes.


Computational biology, machine learning, gene function prediction, post-transcriptional regulation

Competing Interests: None

Dr. Leo Anthony Celi

Guest Editor, PLOS ONE Machine Learning in Health and Biomedicine

Leo Anthony Celi has practiced medicine in three continents, giving him broad perspectives in healthcare delivery. As clinical research director and principal research scientist at the MIT Laboratory for Computational Physiology (LCP), and as an attending physician at the Beth Israel Deaconess Medical Center (BIDMC), he brings together clinicians and data scientists to support research using data routinely collected in the process of care. His group built and maintains the public-access Medical Information Mart for Intensive Care (MIMIC) database, which holds clinical data from over 60,000 stays in BIDMC intensive care units (ICU). It is an unparalleled research resource; over 5000 investigators from more than 70 countries have free access to the clinical data under a data use agreement. In 2016, LCP partnered with Philips eICU Research Institute to host the eICU database with more than 2 million ICU patients admitted across the United States.

Dr. Celi also founded and co-directs Sana, a cross-disciplinary organization based at the Institute for Medical Engineering and Science at MIT, whose objective is to leverage information technology to improve health outcomes in low- and middle-income countries. He is one of the course directors for HST.936 – global health informatics to improve quality of care, and HST.953 – collaborative data science in medicine, both at MIT. He is an editor of the textbook for each course, both released under an open access license. The textbook “Secondary Analysis of Electronic Health Records” came out in October 2016 and was downloaded more the 100,000 times in the first year of publication. The massive open online course HST.936x “Global Health Informatics to Improve Quality of Care” was launched under edX in February 2017. Finally, Leo has spoken in 25 countries about the value of data in improving health outcomes.

Competing Interests: None

Dr. Luca Citi

Guest Editor, PLOS ONE Machine Learning in Health and Biomedicine

Luca Citi is a Reader (associate professor) within the School of Computer Science and Electronic Engineering at the University of Essex, where he teaches Machine Learning and Data Mining to post-graduate students.

His research focuses on the application of statistics, signal processing and machine learning to extract information from biomedical signals and develop technology that improve people's quality of life or enhance human performance.

He holds a degree in Electronic Engineering with major in Biomedical Engineering from Università di Firenze (Italy). He obtained a PhD in Biorobotics Science and Engineering jointly offered by Scuola Superiore Sant'Anna and IMT Lucca (Italy) with a thesis about the decoding of neural signals for the control of robotic arm prostheses. He was a post-doc for one year at Essex, then for three years at the Harvard Medical School and Massachusetts Institute of Technology specializing on statistical analysis of point processes (stochastic processes representing discrete events in time) applied to heartbeat series and neural spike trains.

Competing Interests: None

Dr. Marzyeh Ghassemi

Guest Editor, PLOS ONE Machine Learning in Health and Biomedicine

Marzyeh Ghassemi is a Visiting Researcher with Google’s Verily and a post-doc in the Clinical Decision Making Group at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) supervised by Dr. Peter Szolovits. She will join the University of Toronto as an Assistant Professor in Computer Science and Medicine in Fall 2018, and will be affiliated with the Vector Institute.

Dr. Ghassemi’s research focuses on machine learning with clinical data to predict and stratify relevant human risks, encompassing unsupervised learning, supervised learning, structured prediction. Her work has been applied to estimating the physiological state of patients during critical illnesses, modelling the need for a clinical intervention, and diagnosing phonotraumatic voice disorders from wearable sensor data.

Her work has appeared in KDD, AAAI, IEEE TBME, MLHC, JAMIA, and AMIA-CRI; she has also co-organized the NIPS 2016 Machine Learning for Healthcare (ML4HC) and 2014 Women in Machine Learning (WIML) workshops. Prior to MIT, Marzyeh received B.S. degrees in computer science and electrical engineering as a Goldwater Scholar at New Mexico State University, worked at Intel Corporation, and received an MSc. degree in biomedical engineering from Oxford University as a Marshall Scholar.

Competing Interests: Dr. Ghassemi is supported in part as a Visiting Researcher at Verily

Dr. Tom Pollard

Guest Editor, PLOS ONE Machine Learning in Health and Biomedicine

Tom Pollard is a Research Scientist at the Laboratory for Computational Physiology at Massachusetts Institute of Technology (MIT). With colleagues at MIT he explores questions in critical care medicine with data collected during routine clinical care. He also develops software for the deidentification and integration of clinical data, and is one of the creators of MIMIC-III, a freely-available critical care database that has enabled machine learning research and education around the world. He has a particular interest in reproducibility, and recently collaborated on a project that attempted to reproduce the approach of 28 published mortality prediction studies. He is a Fellow of the Software Sustainability Institute and a Member of the MIT Task Force on Open Access.

Competing Interests: None