Emma Pierson is a senior researcher at Microsoft Research and an incoming assistant professor of computer science at Cornell Tech. She develops data science and machine learning methods to study inequality and healthcare. Her work has been recognized by a Rhodes Scholarship, Hertz Fellowship, Rising Star in EECS, and Forbes 30 Under 30 in Science. She has written for The New York Times, FiveThirtyEight, The Atlantic, The Washington Post, Wired, and various other publications.

Laure Wynants is assistant professor of Epidemiology at Maastricht University in the Netherlands and post-doctoral fellow of the Research Foundation Flanders in Belgium. She earned a M.A. in Biostatistics, summa cum laude, and a PhD from the KU Leuven in Belgium. She’s interested in methods to handle heterogeneity between populations when developing and validating prediction models, and in the utility of prediction models in clinical practice. Her methodological work has received the Lee Lusted award for Quantitative Methods and Theoretical Developments from the society of medical decision-making and the Doug Altman award. Her article “Random‐effects meta‐analysis of the clinical utility of tests and prediction models” was recognized as one of the most read in Statistics in Medicine in 2019. She is associate editor for BMC Diagnostic and Prognostic Research, member of the International Society for Clinical Biostatistics and member of STRATOS’ (STRengthening Analytical Thinking for Observational Studies) topic group on the evaluation of diagnostic tests and prediction models.

Her applied work includes models for gynecological cancers, hospital-acquired infections, and covid- 19. She worked closely with the IOTA consortium on the development and validation of diagnostic models for ovarian cancer. These models are now implemented in mobile apps and ultrasound machines of GE and Samsung, and are incorporated in international clinical guidelines. Since March 2020, she leads an international consortium to systematically review models for covid-19. This review already has over 500 citations and has been picked up by policymakers, including the European Commission.

Michael Mina, MD, PhD is a physician-scientist and Assistant Professor in the department of Epidemiology and Immunology and Infectious Diseases at the Harvard School of Public Health. He is also a pathologist at Brigham and Women’s Hospital and Harvard Medical School where he helps to oversee molecular virology. He is a recent recipient of the National Institutes of Health NIH Director’s Early Independence Award, and his public health efforts have been highlighted by The Economist Magazine where he was named one of eight Global Progress Makers. He has received numerous national and international recognitions for his research on childhood infectious diseases and vaccines.

Most recently, Michael is working with colleagues at the Ragon Institute, the MIT Sloan School and Princeton University to develop a Global Immunological Observatory (GIO) – a not-for-profit network of high throughput laboratories to monitor emerging and existing pathogens in the US and globally. The objective is to create a virus monitoring system that matches the weather forecasting systems in use globally today.

Michael has been on the front lines of the COVID-19 pandemic since it began, helping to advise state, national, and international institutions and governments and has been instrumental in setting up high throughput COVID-19 testing programs at the Broad Institute of MIT and Harvard, where he is also an Associate Member. His research focuses on monitoring and tracking outbreaks of novel and seasonal viruses through the global population. His laboratory at the Harvard School of Public Health develops new technologies and statistical algorithms that measure the immune system response in fine detail and leverages immunological data to detect and forecast pathogen movement. His clinical work at the Brigham and Women’s Hospital is as a medical director overseeing molecular virology diagnostics.

Dr. Noubar Afeyan is co-founder and chairman of Moderna and the founder and CEO of Flagship Pioneering, an enterprise where entrepreneurially-minded scientists invent pioneering solutions to improve human health, nutrition, and sustainability. Dr. Afeyan is a lecturer at Harvard Business School; and from 2000 to 2016 was a senior lecturer at MIT’s Sloan School of Management where he taught courses on technology-entrepreneurship, innovation, and leadership. He teaches and speaks around the world on topics ranging from entrepreneurship, innovation, and economic development to biological engineering, new medicines, and renewable energy.

Dr. Afeyan has co-founded and helped build over 40 life science and technology startups during his career as an inventor, entrepreneur, and CEO. He was founder and CEO of PerSeptive Biosystems, a leader in bio-instrumentation that grew to $100 million in annual revenues. After PerSeptive’s acquisition by Perkin Elmer/Applera Corporation in 1998, he became senior vice president and chief business officer of Applera, where he initiated and oversaw the creation of Celera Genomics.

Dr. Afeyan currently serves on the boards of a number of public and private Flagship companies in addition to Moderna, including Rubius Therapeutics (NASDAQ: RUBY), Cellarity, Omega Therapeutics, Tessera Therapeutics, and others. Previously, he was a member of the founding team, director and investor in several highly successful ventures including Chemgenics Pharmaceuticals (acquired by Millennium Pharmaceuticals), Color Kinetics (acquired by Philips), Adnexus Therapeutics (acquired by Bristol-Myers Squibb) and Affinnova (acquired by AC Nielsen).

Dr. Afeyan is a member of the Corporation of MIT (the Institute’s governing body) and a member of the board of trustees for the Boston Symphony Orchestra.

Chris Holmes is Programme Director for Health and Medical Sciences at The Alan Turing Institute. His appointment is made jointly with Health Data Research UK.

He is Professor of Biostatistics at the University of Oxford with a joint appointment between the Department of Statistics and the Nuffield Department of Clinical Medicine through the Wellcome Trust Centre for Human Genetics and the Li Ka Shing Centre for Health Innovation and Discovery.

Before joining Oxford, Chris was based at Imperial College, London, and also worked in industry conducting research in scientific computing. He holds a Programme Leader’s award in Statistical Genomics from the Medical Research Council UK. In 2016, WIRED UK magazine named him one of the ‘Innovators of the year in AI’.

Chris has a broad interest in the theory, methods and applications of statistics and statistical modelling. He is particularly interested in pattern recognition and nonlinear, nonparametric statistical machine learning methods applied to the genomic sciences and genetic epidemiology.

Aisha Walcott-Bryant, PhD leads a team of researchers and engineers who are working on transformational innovations in global health using advanced AI, Blockchain, and other technologies. She and her team are engaged in projects in Maternal Newborn Child Health (MNCH), Family Planning (FP), disease intervention planning, and water management. She earned her PhD in Electrical Engineering and Computer Science from MIT.

Parthasarathy Suryanarayanan is a senior research software engineer and architect, part of the Foundational Innovation department under the Healthcare & Life Sciences division of IBM Research. As the engineering lead in one of IBM’s research teams, he has spent the last decade in applying NLP to the medical domain.

I am at Microsoft Research’s Healthcare NExT working on problems related to clinical natural language processing and machine reading. My research focuses on exploring relationships in complex, unstructured healthcare data using natural language processing and unsupervised learning techniques. Previously, I completed a PhD in the Clinical Decision Making group at MIT CSAIL with Prof. Peter Szolovits, where the research focused on leveraging text representations for clinical predictive tasks, combining structured and unstructured healthcare data. My work has appeared in KDD, AAAI, AMIA, JMIR, Science Translational Medicine, and Nature Translational Psychiatry.

While at MIT, I was an Instructor for HST.953 (Collaborative Data Science for Medicine) and co-authored its textbook, “Secondary Analysis of Electronic Health Records.” I served as the General Chair for the NIPS 2018 Machine Learning for Health (ML4H) workshop, and co-organized the NIPS 2017 ML4H workshop, the COLING 2016 Clinical NLP workshop, and several “datathon” events, which bring together participants to address problems of clinical interest. I also served as a mentor for the MIT Summer Research Program (MSRP), and has spent time as a Software Engineering Intern at Intel Corporation. Prior to MIT, I was a Program Manager at Microsoft Corporation, an Associate Product Manager Intern at Google, and received B.S. and M.S degrees in computer science from Columbia University. While at Columbia University, I was a MS-TA fellow and recipient of the Andrew P. Kosoresow Memorial Award for Outstanding Performance in TA-ing and Service.

Andrew Beam, PhD is an assistant professor in the Department of Epidemiology at the Harvard T.H. Chan School of Public Health, with secondary appointments in the Department of Biomedical Informatics at Harvard Medical School and the Department of Newborn Medicine at Brigham and Women's Hospital. His research develops and applies machine-learning methods to extract meaningful insights from clinical and biological datasets, and he is the recipient of a Pioneer Award from the Robert Wood Johnson Foundation for his work on medical artificial intelligence.

Previously he was a Senior Fellow at Flagship Pioneering and is founding head of machine learning at Generate Biomedicines, a Flagship-backed venture that seeks to use machine learning to improve our ability to engineer proteins.

He earned his PhD in 2014 from N.C. State University for work on Bayesian neural networks, and he holds degrees in computer science (BS), computer engineering (BS), electrical engineering (BS), and statistics (MS), also from N.C. State. He completed a postdoctoral fellowship in Biomedical Informatics at Harvard Medical School and then served as a junior faculty member.

I work at Google Medical Brain. I am also an Assistant Professor in Statistical Science at Duke University, where I also hold secondary appointments in Computer Science, Electrical and Computer Engineering, Neurobiology, Neurology, and is affiliated with the Center for Cognitive Neuroscience and the Information Initiative at Duke. I received my PhD from the Gatsby Computational Neuroscience Unit at UCL, and was a postdoc at the University of Cambridge on an EPSRC postdoc fellowship, and at MIT on an NSF postdoc fellowship. In terms of grants I am the recent recipient of a couple of Google faculty research awards, a first round BRAIN initiative award from the NSF, a CAREER, a DARPA lifelong learning award, and an ARI award for modeling teams. My PhD thesis work was on coming up with efficient ways to do Bayesian clustering. I did a bunch of work on nonparametric Bayes, social networks, and in computational cognitive science. I am currently interested in the combination of Bayesian statistics and deep neural networks, interacting dynamical techniques, and time series and network modeling in general. I apply the work that I do to problems in medicine and in social sciences. Here are a couple of my recent projects: 1) Using a combination of Gaussian Processes and Recurrent Neural Networks to predict whether a hospital patient is at risk for sepsis. This work has been deployed in the Emergency Department at Duke University Hospital. 2) I have used Gaussian Processes, Infinite Factor Models, and logistic regression to tackle other medical problems such as chronic kidney disease, and the prediction of surgical complications. 3) I developed an app to collect data on people with multiple sclerosis. The problem when you have any chronic illness is that you spend more time outside the medical system (where data might be collected) than in it, so this is an exploration of using mobile devices and other trackers to fill in the missing data. 4) I am using time series, stochastic Bayesian methods to model social networks, ranging from conversation networks (who is most influential in a controversial supreme court case?), to disease networks (is my friend likely to get me sick?), to Duke basketball team networks (how do we construct a winning basketball team?). On a personal level: I grew up in the Bronx, and in the northwest suburbs of NYC. I went to school on long island, and in Manhattan. I lived for a decade in the UK, a year each in Oxford and Cambridge, the rest of the time in London. Over a year in Boston, and, most recently, 6 years in North Carolina. I have two children, 4 and 6 years old.

Dr. Nsoesie applies data science methodologies to global health problems, using digital data and technology to improve health, particularly in the realm of surveillance of chronic and infectious diseases. She has also been appointed as a BU Data Science Faculty Fellow, as part of the BU Data Science Initiative at the Hariri Institute for Computing. The Data Science Faculty Fellows program assembles a cluster of uniquely talented faculty whose expertise transcends traditional disciplinary boundaries to enable fundamental advances in data science. Dr. Nsoesie completed her PhD in Computational Epidemiology from the Genetics, Bioinformatics and Computational Biology program at Virginia Tech, and her PhD dissertation, Sensitivity Analysis and Forecasting in Network Epidemiology Models, at the Network Dynamics and Simulations Science Lab at Virginia Tech BioComplexity Institute. After postdoctoral associate positions at Harvard Medical School and Boston Children’s Hospital, Dr. Nsoesie joined the faculty of the Institute for Health Metrics and Evaluation (IHME) at the University of Washington.

Rudraksh Tuwani is a graduate reserach assistant in Dr. Andrew Beam's lab at the Harvard School of Public Health. His research focuses on large language models for medical question and answering tasks. He is currently a masters student in the Harvard Health Data Science program and holds a B.Sc. in statistics from Delhi University.

Ben Kompa is a PhD student in Dr. Andrew Beam's lab at the Harvard School of Public Health. His research focuses on uncertainty estimation for medical machine learning models. Additionally, he works on NLP for medical data and medical question and answering tasks. Previously, he interned at Microsoft Research and earned his M.Phil. in Computational Biology at the University of Cambridge as a Churchill Scholar. Prior to that, he studied mathematics and computer science at the University of North Carolina at Chapel Hill.