Keynotes & Speakers 2020

Keynote Address & Special Guests

Dr. Leo Anthony Celi (MD, MSc, MPH)

Leo Anthony Celi MD is clinical research director and principal research scientist at the Massachusetts Institute of Technology Laboratory for Computational Physiology (LCP), and an attending physician at the Beth Israel Deaconess Medical Center (BIDMC). He has practiced medicine in three continents (including New Zealand), giving him broad perspectives in healthcare delivery. As 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; close to 10,000 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.

Leo 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 thetextbook 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.

Mr. Saxon Connor FRACS (Gastrointesintal/Hepatopancreatobiliary);  E-clinical Health Lead (CDHB)

Saxon has been an HPB Surgeon at Christchurch hospital, New Zealand since 2005. After completing his RACS training in NZ he spent 4 years in United Kingdom completing HPB training (Liverpool and Edinburgh). His research interests have focused on clinical issues including safe cholecystectomy, bile duct injury, minimally invasive pancreatic necrosectomy, enhanced recovery surgery and post pancreatectomy pancreatitis. He has taken a role as the e-clinical health lead for the Canterbury district health board with the aim of helping to establish a digital health system. He has been involved with tech companies bringing electronic workflow solutions for clinicians from concept to production. Since 2008 he has been an Editor of HPB and sits on editorial boards of the World Journal of Surgery and British Journal of Surgery. He is on the ANZHPBA board and the research committee of the IHPBA. He is an e-tutor for HPB module of the University of Edinburgh online ChM programme. He has published 125 articles including, book chapters and videos

Minnan Xu

Minnan completed her BS and MS in Electrical Engineering from MIT. She then completed a PhD in Biomedical Engineering on  2010. She subsequently became a postdoc at the Massachusetts Eye and Ear Infirmary, Harvard University. She is now Principle Scientist at Philips Research.

 

 

Matthieu Komorowski

An experienced anaesthetist and intensivist with 10 years in training and practice, Matthieu Komorowski hold full specialist board certification in both France and the UK. A former medical research fellow at the European Space Agency, he completed a Master in Biomedical Engineering at Imperial College London. He currently pursues a PhD at Imperial College and a research fellowship in intensive care at Charing Cross Hospital in London. A visiting scholar at the Laboratory of Computational Physiology at Harvard/MIT, he belongs to the MIT Critical Data group (Professor Leo Celi) where he apply machine learning to large critical care databases, with the objective of developing decision support systems for sepsis, the number one killer in intensive care.

Rhema Vaithianathan

Rhema is Director of the Centre for Social Data Analytics at Auckland University of Technology where she is also a Professor of Economics. In addition, she holds a partial appointment at the Institute for Social Science Research at the University of Queensland, Australia, as a Professor of Social Data and Analytics. She is also Director of the Singapore Life Panel, a large population-representative monthly survey run from Singapore Management University. Rhema is recognised internationally for implementation of machine learning tools in high stakes government systems such as child welfare. She leads the international research team that developed, and continues to refine, the Allegheny Family Screening Tool, a child welfare predictive risk modelling tool for Allegheny County, PA (United States). Rhema’s current predictive analytics work in the United States is diverse, including implementation of a child welfare predictive risk model for Douglas County, CO, and a feasibility study for a predictive risk algorithm to help Allegheny County prioritise homelessness services. Her work has been published in top journals and profiled in The New York Times and Nature. She is frequently invited to speak to government agencies, researchers and practitioners around the world about ethical use of machine learning tools in public policy. Rhema has held research positions in Australia, Singapore and the United States, including a Harkness Fellowship at Harvard University.

Tom Pollard

Tom Pollard PhD is a Research Scientist at the Massachusetts Institute of Technology (MIT) Laboratory for Computational Physiology. Most recently he has been working with colleagues to release the [eICU Collaborative Research Database] (http://eicu-crd.mit.edu/), a freely-accessible database comprising patient data collected from critical care units across the US. Prior to joining MIT in 2015, Tom completed his PhD at University College London, UK, where he explored models of health in critical care patients. He has a broad interest in how we can improve the way that health data is collected and reused for the benefit of patients, and he is a Fellow of the Software Sustainability Institute in the UK

Dr. Joy Wu MBChB, MPH

Joy, MBChB MPH, is an Otago medical grad who completed two years of clinical training in her hometown of Christchurch. Her search for better ways of using data to improve clinical workflow and patient care saw her pursuing a Master of Public Health from Harvard University. Now she is a mother of two and is working as a Clinical Informatics Research Scientist at the IBM Almaden Research Center in Silicon Valley.  Her research spans clinical natural language processing, computer vision with deep learning, and multimodal clinical reasoning/decision making. Her dream is one day to be able to help patients find the best solution for their particular problems through working with an AI system that is capable of helping the physician to harness the prior experiences of other doctors and similar patients from across the world.

Omar Badawi

Omar Badawi, PharmD, MPH, FCCM is the Head of Health Data Science and AI in the Philips Patient Care Analytics business and leads the research for developing and validating product-related predictive algorithms and decision support tools.  He is also an Adjunct Assistant Professor with the University  of Maryland School of Pharmacy and Research Affiliate at the Massachusetts Institute of Technology. He earned a Master in Public Health degree with a focus in Epidemiology and Biostatistics from The Johns Hopkins Bloomberg School of Public Health and is currently the Program Manager for the Philips eICU Research Institute which supports collaborative research between industry, academia and clinicians using de-identified clinical data representing over 3.5 million ICU patients. Dr. Badawi is also a Fellow of the American College of Critical Care Medicine.

Jonathan Rubin

Jonathan Rubin, PhD is Senior Scientist at Philips Research North America and a Research Affiliate of the ALFA Group at MIT’s Computer Science and Artificial Intelligence Laboratory. Jonathan received his PhD in Computer Science from the University of Auckland in 2013. His PhD focused on the use of artificial intelligence in computer games. After his PhD, Jonathan worked as a researcher at Silicon Valley’s Palo Alto Research Center before joining Philips Research in 2016. In his current work, he develops algorithms using deep learning to automatically analyze medical data, including physiological waveforms and medical images.

 

 

 

 

 

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