Predictive Analytics World for Healthcare 2022

Peter Bak is the CIO at Humber River Hospital. He is responsible for the digital vision, on-going operations of that vision, and the innovation agenda. With over 30 years of experience in healthcare informatics, IT operations, software development, and executive leadership, Peter has contributed to the Canadian healthcare system in many ways: driving 100% adoption of digital medical imaging in Canada, creating awareness of digital transformation and Command Centres around the world, and lecturing on Technology in Healthcare. Peter has a PhD in applied engineering from Imperial College, London. He has published various papers on health care-related topics and was recognized as the Canadian public sector CIO for 2016.

Anasse Bari is a professor of computer science and director of the Predictive Analytics and AI research lab at New York University. Prof. Bari teaches computer science and leads a multidisciplinary research team that designs specialized Artificial Intelligence and Predictive Analytics to help solve problems in healthcare, finance, politics and social good. Prof. Bari has been advising leading healthcare organizations and financial firms on their data strategy, and he recently played the role of Artificial Intelligence advisor to major international organizations including the United Nations, the World Bank and the International Finance Corporation. Prof. Bari is the recipient of several teaching and research awards including the NYU Marateck Prize for his outstanding teaching in computer Science and the World Bank Spot Award for his extraordinary efforts in service of the organization. Prof. Bari is the co-author of the book “Predictive Analytics for Dummies”. Prof. Bari was featured in the Science magazine, CNBC, The Times, the Hill and France24 among others for his work on specialized Artificial Intelligence.  

Clinton Brownley, Ph.D., is a data scientist at WhatsApp, where he’s responsible for a variety of analytics projects designed to improve messaging and VoIP calling performance and reliability.  Before WhatsApp, Clinton was a data scientist at Facebook, working on large-scale infrastructure analytics projects to inform hardware acquisition, maintenance, and data center operations decisions.  As an avid student and teacher of modern analytics techniques, Clinton is the author of two books, “Foundations for Analytics with Python” and “Multi-objective Decision Analysis,” and also teaches Python programming and data science courses at Facebook and in the Bay Area. Clinton is a past-president of the San Francisco Bay Area Chapter of the American Statistical Association and is a council member for the Section on Practice of the Institute for Operations Research and the Management Sciences. Clinton received degrees from Carnegie Mellon University and American University.

Tom Chi has worked in roles ranging from astrophysical researcher to Fortune 500 consultant to corporate executive leading hardware/software team to develop innovative products & services. He pioneered and practices a unique approach to rapid prototyping and leadership that can jumpstart innovative new ideas and move large organizations at unprecedented speeds. He was head of Product Experience and a founding member of Google X, and currently works to accelerate a future where humanity becomes a net positive to nature as Managing Partner of At One Ventures.

Kelley Counts is Director of Data Science at OneBlood. He leads teams that develop and deploy innovative machine learning applications to increase blood donations, anticipate hospital demand for blood products, and optimize the supply chain.  Kelley also has expertise in marketing analytics, data visualization, and the development of statistical business solutions. In addition to his work at OneBlood, Kelley supervises teams of students in Data Science graduate programs. He has also worked on committees to design Python-based Machine Learning graduate courses. Kelley is a frequent speaker on practical applications of machine learning and has published articles on analytics-driven organizational change and workforce adoption. He has served on international professional boards as an advisor on analytics, digital technology, and information systems. Kelley has a Bachelors in Biochemistry, and a MS and MBA in Business Analytics.

Chris is currently the Direct of Advanced Analytics at OSF Healthcare where he leads a team of data scientists and statisticians who create and deploy industry leading advanced analytics solutions.  He has more than 12 years of experience working in healthcare and holds a MS in Predictive Analytics from Northwestern University, where he focused on advanced modeling techniques and predictive text mining.

Siddha Ganju, an AI researcher who Forbes featured in their 30 under 30 list, is a Self-Driving Architect at Nvidia. As an AI Advisor to NASA FDL, she helped build an automated meteor detection pipeline for the CAMS project at NASA, which ended up discovering a comet. Previously at Deep Vision, she developed deep learning models for resource constraint edge devices. Her work ranges from Visual Question Answering to Generative Adversarial Networks to gathering insights from CERN's petabyte-scale data and has been published at top-tier conferences including CVPR and NeurIPS. She has served as a featured jury member in several international tech competitions including CES. As an advocate for diversity and inclusion in technology, she speaks at schools and colleges to motivate and grow a new generation of technologies from all backgrounds. She is also the author of O'Reilly's Practical Deep Learning for Cloud, Mobile and Edge.

Robert Grossman is a partner at Analytic Strategy Partners and a professor at the University of Chicago.  From 2002 to 2016, he was the Founder and Managing Partner of Open Data Group, which provided data science consulting services to a wide variety of companies, including those in financial services, location services, computational advertising and cybersecurity. From 1996 to 2001, he was the Founder and CEO of Magnify, which developed predictive analytic software for the financial services and computational advertising industries.  Magnify was sold to ChoicePoint in 2003 and is now part of the RELX Group.  He is the Frederick H. Rawson Professor of Medicine and Computer Science and the Jim and Karen Frank Director of the Center for Translational Data Science at the University of Chicago, where he leads a data science research group that is developing systems and algorithms for managing, analyzing and sharing large biomedical and environmental datasets.

Dohyeong Kim, Ph.D. is currently a Professor of public policy, geospatial information sciences and social data analytics and research in the School of Economic, Political and Policy Sciences at the University of Texas at Dallas. He also serves as an Associate Dean of Graduate Education and works as the Director of the Geospatial Health Research Group. His multidisciplinary and multinational collaborative research projects have been funded by the U.S. National Institute of Health, World Health Organization, and the National Research Foundation of Korea. Most of his research fundings have been published in numerous leading refereed journals in public and environmental health fields, and presented in over 200 national and international meetings. His research interests include highly interdisciplinary, including global health and safety, environmental health informatics, and spatiotemporal big data analysis and machine learning. He was a recipient of the 2008 New Investigators in Global Health Award. He holds a Ph.D. in health planning from the University of North Carolina at Chapel Hill.

Danita Kiser is a Director of Applied Research and AI/ML in Optum Technology, part of UnitedHealth Group. She is an innovation leader with more than 20 years’ experience in companies such as AOL, Turner Broadcasting, Cox Communications, and numerous technology start-ups. For the past six years, Danita has held roles at Optum in applied research, data science, product management and led delivery of innovative products in big data, infectious disease forecasting and voice processing. She holds a Bachelor of Science degree from the University of Virginia and a PhD from Emory University.

Karl Rexer founded Rexer Analytics in 2002. He and his teams have built an outstanding reputation providing predictive modeling and analytic consulting to clients across many industries. Recent clients include OneBlood, PwC, Boston Scientific, Redbox, ADT Security, Interamericana University, MIT, Forward Financing, SharkNinja, and many smaller companies. In addition to leading client engagements and hands-on data work, Karl is a predictive analytics evangelist, frequently speaking at conferences, colleges, and other events. He also serves on Advisory Boards for the Business Analytics programs at both Babson College and Bentley University. Since 2007 Rexer Analytics has conducted surveys of analytic professionals, asking them about their algorithms, tools, behaviors and  views. Summary reports from these surveys are available as a free download from the Rexer Analytics website. Prior to founding Rexer Analytics, Karl held leadership positions at several consulting firms and two multi-national banks. Karl holds a PhD from the University of Connecticut.

Sung-Chul Seo received a PhD in environmental health and occupational hygiene with strong background in aerosol science, extensive experiences in project management and laboratory works, and exposure assessment for radon and air pollutants, as well as chemical/biological agents. Since 2018, he has been the President of Korea Association of Radon. He is currently a Professor with Seokyeong University, Seoul, South Korea. He has served on the executive boards of Korean Society of Indoor Environment, and for over 20 years he is dedicated to the improvement of indoor air related to environmental health, including radon, particulate matters, and bioaerosols. Recently, he is focusing on establishment of prevention platform using deep learning technology based on big data attributed to real-time the IoT devices. He has many funding as well as academic articles. His research interest includes development of monitoring system for the level of environmental risk factors (i.e., particulate matters) to prevent diseases exacerbations.

Tom (PhD, Physics) has spent most of his career building computational tools and applying them to complex problems. As a Lead Scientist at Elder Research, Tom contributes to a diverse collection of projects and clients across the company. Highlights include Bayesian multifactor modeling with Stan, object detection with PyTorch, and network science and graphs research. He most enjoys the opportunity to work with many other Elder Research scientists to learn, experiment, and solve interesting problems.

Tom joined Elder Research after completing a PhD in Physics at the University of North Carolina, where he applied high performance computing systems to the study of nuclear radioactive decays. This radioactivity occurs in massive quantities during supernovae and neutron star mergers like those events observed by gravitational wave detectors in recent years.

Eric Siegel, Ph.D., founder of the Predictive Analytics World conference series and executive editor of The Machine Learning Times, makes the how and why of predictive analytics understandable and captivating. He is the author of the award-winning Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, a former Columbia University professor who used to sing to his students, and a renowned speaker, educator, and leader in the field.

Eric has appeared on Bloomberg TV and Radio, Business News Network (Canada), Fox News, Israel National Radio, NPR Marketplace, Radio National (Australia), and TheStreet. He and his book have been featured in Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, The Financial Times, Forbes, Forrester, Fortune, Harvard Business Review, The Huffington Post, The New York Review of Books, Newsweek, Quartz, Salon, Scientific American, The Seattle Post-Intelligencer, The Wall Street Journal, The Washington Post, and WSJ MarketWatch. Follow him at @predictanalytic.

Owen is the Director of Machine Learning for Revenue and Access at Epic systems, where he has work for over seven years, working on predictive analytics for population health, acute care, and operations. He has helped healthcare organizations across the world gain value from implementing predictive analytics within workflows. He has also served on the advisory board for multiple colleges, including UW-Platteville and Dickinson College, to help them develop Data Science major programs. Owen holds a Ph.D. in Mathematics from UCLA and before working at Epic he held a faculty position at UW-Madison and visiting scholar positions at the University of Tokyo and the Sorbonne.

David Talby is a chief technology officer at John Snow Labs, helping healthcare & life science companies put AI to good use. David is the creator of Spark NLP – the world’s most widely used natural language processing library in the enterprise. He has extensive experience building and running web-scale software platforms and teams – in startups, for Microsoft’s Bing in the US and Europe, and to scale Amazon’s financial systems in Seattle and the UK. David holds a PhD in computer science and master’s degrees in both computer science and business administration.

Nick has spent the past 11 years at OSF HealthCare developing and implementing analytical solutions in a variety of roles. He began his career building the tools that maintain, process, and visualize data as a data engineer and business intelligence developer to building machine learning and optimization models as a data scientist. He currently serves as the Manager of Data Science Services where he and his team leverage their diverse quantitative backgrounds to solve critical and complex problems across all areas of the healthcare system. Nick holds bachelor’s and master’s degrees in Industrial and Systems Engineering from Northern Illinois University.

Co-founder of MIM Solutions. A graduate from the University of Warsaw with a Ph.D. in Informatics and double master's degree in Informatics and Mathematics. Assistant professor at the University of Warsaw. Experienced both in the theoretical and commercial aspects of machine learning. His principal focus is on delivering deep learning solutions in socially important areas, predominantly medicine.