Webinar: How will data science and AI shape the post-pandemic future of healthcare?
|Event Date:||January 27, 2021|
|School or Program:||College of Engineering
The 2020 pandemic has challenged our healthcare systems but it has also opened the door to a greater acceptance of virtual-clinic-visit and AI-enabled automation. What will the future of healthcare look like in the AI era? How data science and AI will shape the post-pandemic future of healthcare?
Guang Lin is an Associate Professor in the School of Mechanical Engineering and Department of Mathematics at Purdue University. Prof. Guang Lin is the Director of Data Science Consulting Service that performs cutting-edge research on data science and provides hands-on consulting support for data analysis and business analytics in all areas to overcome data science challenges arising in research, education, and business and organization management. His research interests include diverse topics in computational and data science both on algorithms and applications. His main current thrust is machine learning, data-driven modeling, stochastic simulation, and multiscale modeling of interconnected, physical, and biological systems. Prof. Lin is currently a member of Purdue Engineering Initiative in Data Engineering and Application. Prof. Lin has received various awards, such as the NSF CAREER Award, Mid-Career Sigma Xi Award, University Faculty Scholar, Mathematical Biosciences Institute Early Career Award, and Ronald L. Brodzinski Award for Early Career Exception Achievement.
As a Director of vaccine modeling team at Economic Data Sciences of The Center for Observational and Real-world Evidence within Merck Research Laboratories, I construct, calibrate, and validate sophisticated infectious disease models that integrate both clinical trial and real-world data. My major technical responsibilities include applying modeling techniques to evaluate health economic impact of various vaccination strategies, as well as to inform indication/site selection for vaccine clinical trial planning. Since joining Merck nearly three years ago, I have led the development of analytical modeling activities for four vaccines against Cytomegalovirus, Rotavirus, and SARS-CoV-2.
Eugenio Culurciello (S'97-M'99) received the Ph.D. degree in Electrical and Computer Engineering in 2004 from the Johns Hopkins University, Baltimore, MD. Since July 2019 Eugenio Culurciello works as a Fellow at Micron Inc. He was an associate professor of the School of Electrical and Computer Engineering, the Weldon School of Biomedical Engineering, the School of Mechanical Engineering, and of Psychological Sciences in the College of Health & Human Sciences at Purdue University, where he directed the ‘e-Lab’ laboratory. His research focus is in artificial vision systems, deep learning, hardware acceleration of vision algorithms. His research interests include: analog and mixed-mode integrated circuits for biomedical instrumentation, synthetic vision, bio-inspired sensory systems and networks, biological sensors, silicon-on-insulator design. Eugenio Culurciello is the recipient of The Presidential Early Career Award for Scientists and Engineers (PECASE), the Distinguished Lecturer of the IEEE (CASS), and is the author of the book "Silicon-on-Sapphire Circuits and Systems, Sensor and Biosensor interfaces" published by McGraw Hill in 2009, and "Biomedical Circuits and System, Integrated Instrumentation" published by Lulu in 2013. In 2013 Dr. Culurciello founded TeraDeep, a company focused on the design of deep neural network processors. In 2016 Dr. Culurciello founded FWDNXT to deliver the next generation synthetic brains for artificial intelligence (acquired by MIcron Inc.).
Mona is the Global Head of Medical AI at NVIDIA. She brings a unique perspective with her varied experience in clinical medicine, medical applications, and business. She is a board certified cardiac surgeon and the previous Chief Medical Officer of a digital health company. She holds an MBA in Management Information Systems, and has worked on Wall Street. Her ultimate goal is the betterment of medicine through AI.
Toyya is an Assistant Professor in the School of Industrial Engineering at Purdue University. Her PhD is in Industrial Engineering from Georgia Institute of Technology (GT) with a concentration in Statistics and a minor in Biomedical Informatics. Her doctoral research focuses on analytics and machine learning applied to health data. She leverages methods from data science, statistics, and network science with application to various populations including pregnant women, infants, and chronic opioid users. She is an awardee of the National Institute of Health Training Grant, an Alfred P. Sloan Scholar Fellow and has received various other awards including from poster sessions and INFORMS. During her PhD, she also had the opportunity to serve as a visiting scholar at Harvard Medical School in the Health Care Policy Department where she completed research in causal inference.
Before her PhD, Toyya did cost modeling for the US Air Force where she predicted acquisition costs to acquire communication systems and satellites. Toyya received a Bachelor of Science in Management Science from the Massachusetts Institute of Technology. She also obtained two Master of Science degrees, one in Operations Research from Northeastern University, and one in Statistics from GT. She has been a passionate volunteer and mentor for students from underrepresented minorities through her work with the Latino Organization of Graduate Students (LOGRAS) and teaching and mentoring programs through Georgia Tech’s Office of Minority Education. Now as an Assistant Professor, Toyya plans to continue data-driven research that supports vulnerable groups within healthcare. Additional information about Toyya can be found on her personal website at www.toyya-pujol.com.
Mario is currently an Associate Professor of Industrial Engineering at Purdue University. His broad interests generally fall in the area of computational science and engineering, and across multiple application domains. Prior to joining Purdue he held postdoctoral positions at The University of Toronto and Cambridge, which followed the completion of his PhD in Systems Design Engineering from the University of Waterloo. His undergraduate and masters degrees are in computer science from Brock and Guelph Universities, respectively.