Predictive Analytics World for Healthcare 2021

Dean Abbott is an internationally recognized thought leader and innovator in data science and predictive analytics, with more than three decades of experience solving problems in customer analytics, fraud detection, risk modeling, text mining, survey analysis, and many more. He is frequently included in lists of the top pioneering and influential data scientists in the world. Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and coauthor of The IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a popular keynote speaker and bootcamp/workshop instructor at conferences worldwide and serves on advisory boards for the UC/Irvine Predictive Analytics and UC/San Diego Data Science Certificate programs. He holds a bachelors degree in computational mathematics from Rensselaer Polytechnic Institute and a masters degree in applied mathematics from the University of Virginia.

Javed Ahmed is a Senior Data Scientist with Metis, where he focuses on corporate training programs in Machine Learning and analytics. A financial economist by background, he has extensive experience developing analytic applications for large organizations including Amazon and the Federal Reserve Board of Governors. Javed holds a PhD in Finance and MA in Statistics from U.C. Berkeley, as well as undergraduate degrees in Finance and Systems Engineering from the University of Pennsylvania.

Benjamin Arias is Co-Founder and Head of Consultancy at Foresta.IO. He combines operational and information technology experience since he has led operational improvement teams for Nestle in the US, Europe, Asia and Latin America during seven years. Mr. Arias has also been the CIO for the biggest fresh fruit broker in the America’s Pacific Coast for ten years and also been the Corporate Head of Industrial Engineering for Unilever at its Heardquarters in Schaffhausen, Switzerland for three years, before returning back home to Chile.

Mr. Arias is an Industrial Engineer (1997) with a Master Degree in Information Technology and Artificial Intelligence (2012) and also a lecturer at his alma mater for last year undergraduate students and a presenter for the topic of operational efficiency with algorithms and AI at Berlin, Germany (2019) and Santiago de Chile (2020).

Robert is a Senior Data Scientist at SAS where he builds end-to-end artificial intelligence applications.  He also researches, consults, and teaches machine learning with an emphasis on deep learning and computer vision for SAS. Robert has authored a book on computer vision and has developed several professional courses on topics including neural networks, deep learning, and optimization modeling. Before joining SAS, Robert worked under the Senior Vice Provost at North Carolina State University, where he built models pertaining to student success, faculty development, and resource management. Robert also started a private analytics company while working at North Carolina State University that focused on predicting future home sales. Prior to working in academia, Robert was a member of the research and development group on the Workforce Optimization team at Travelers Insurance. His models at Travelers focused on forecasting and optimizing resources. Robert graduated with a master’s degree in Business Analytics and Project Management from the University of Connecticut and a master’s degree in Applied and Resource Economics from East Carolina University.

Jeff Deal is the Chief Operating Officer for Elder Research, the nation's leading data science, machine learning, and artificial intelligence consultancy. He has also been the Chair of the Predictive Analytics World for Healthcare conference since its inception in 2014.  In his role at Elder Research, Jeff oversees the operations of the business including contracting, finances, regulatory/legal issues and human resources. Jeff has worked with dozens of clients to understand their business needs and organizational goals and, in the process, has gained insight into organizational obstacles to successful data analytics engagements. His talk on the Top 10 Data Mining Business Mistakes has been well received at prior Predictive Analytics World conferences. In 2016, Jeff and the Elder Research President & CEO, Gerhard Pilcher, published, Mining Your Own Business: A Primer for Executives on Understanding and Employing Data Mining and Predictive Analytics.

Jeff has more than 30 years of experience in business operations, planning, and government relations, primarily in the health care industry. Prior to ERI, he was the president of a health planning consulting business that assisted hospitals and physicians with operational analysis, forecasting, and navigating through complex regulatory processes. Before that, Jeff spent 16 years in hospital administration with responsibility for clinical, support, and planning functions. Jeff has a Master of Health Administration degree from Virginia Commonwealth University and an undergraduate degree from the College of William and Mary.

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina, Washington DC, and London. Dr. Elder co-authored 3 award-winning books on analytics, was a discoverer of ensemble methods, chairs international conferences, and is a popular keynote speaker. John is occasionally an Adjunct Professor of Systems Engineering at the University of Virginia.

Chris is the Chair of Predictive Analytics World Healthcare and the Vice President of Advanced Analytics for OSF Healthcare where he leads a team of data scientists and automation engineers who create and deploy industry leading advanced analytics solutions.  He has more than 16 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.

Dr. Gudesblatt, the medical director of the Comprehensive MS Care Center at South Shore Neurologic Associates, P.C., graduated from Johns Hopkins University in 1976 and from Cornell University Medical College in 1980. His postgraduate training included a medical internship and neurologic residency at Mount Sinai Hospital, where he was the Chief Resident, as well as a Clinical Research Fellow in Neuromuscular Disease through the National Institute of Health. Dr. Gudesblatt is certified by the American Board of Psychiatry and Neurology in the specialty of Neurology, is a diplomate in the American Academy of Pain Management, and has had additional training in Neuro-Rehabilitation. Dr. Gudesblatt has authored articles appearing in professional literature on numerous topics, including movement disorders, tumors of the brain and spinal cord, neurologic complications of pregnancy, stroke, familial inheritance of neurologic illness and multiple sclerosis. He also participates in clinical research at South Shore Neurologic, involving MS and other neurologic diseases.

Veysel is a Lead Data Scientist at John Snow Labs, improving the Spark NLP for Healthcare library and delivering hands-on projects in Healthcare and Life Science. He is a seasoned data scientist with a strong background in every aspect of data science including machine learning, artificial intelligence and big data with over ten years of experience. He’s also pursuing his PhD in ML at Leiden University, Netherlands and delivering graduate level lectures in ML and Distributed Data Processing. Veysel has broad consulting experience on Statistics, Data Science, Software Architecture, DevOps, Machine Learning and AI to several start-ups, bootcamps and companies around the globe. He also speaks at Data Science & AI events, conferences and workshops, and has delivered more than 30 talks at International as well as national conferences and meet-ups.

Zhipeng is a principal data scientist at Geneia. His focus is creating AI models that identify people at high risk for developing chronic conditions, while also helping care managers provide personalized management for high-risk patients. 

Zhipeng is a researcher experienced in computational genomics, pharmacogenomics and causal inference. Before Geneia, he was an Insight Health Data Science-program Fellow, where he created a predictive model that uses EHR data to identify people at high risk for developing fatty liver diseases. At Purdue University, he studied how genetic factors contribute to the development of metabolic diseases and published six peer-reviewed papers in high-impact journals. Additionally, the highly-ranked Journal of Hepatology published part of his Ph.D. work. 

Zhipeng holds a Master of Science in statistics and a Ph.D. in pharmacology from Purdue University. He also earned a master’s degree in biochemistry and bachelor’s degree in bioengineering. 

Jorn op den Buijs, PhD is a senior scientist at Philips Research, carrying out research on predictive analytics to predict and prevent emergency hospitalizations in the frail and elderly. Jorn op den Buijs has a Master’s in Biomedical Engineering from the Eindhoven University of Technology and a PhD degree in Biomedical Engineering from the Mayo Clinic Graduate School, Rochester Minnesota.

Arjun is the founder and CEO of Diabetes Digital Media. Arjun launched DDM's first service, Diabetes.co.uk (the world’s largest diabetes community) whilst in his first year of university in 2003 as a result of his grandfather’s quadruple heart bypass and diagnosis of type 2 diabetes. Benefiting from a decade of experience in big data and affecting user outcomes, Arjun leads the organisation's development of intelligent, evidence-based digital health interventions that harness the power of big data and machine learning to provide precise and personalized care to patients, health agencies and governments worldwide.

DDM's Low Carb Program and Gro Health digital health interventions are regulated as Software as a Medical Device and redefining chronic disease and wellness.  Over 1.8 million members use a DDM service.

With advanced degrees in computers and secondary education, along with several IT certifications, Dr. Joe Perez, Industry Insights’ 2021 Thought Leader of the Year, brings 35-plus years’ experience to the stage as an IT/higher education professional. Having served as BI Specialist at NC State University and currently serving as Senior Systems Analyst/Team Lead at the NC Department of Health & Human Services (and CTO at SolonTek Corporation), Perez stays active in the IT community with more than 15,000 LinkedIn followers. A published author, talk show co-host, world-renowned international keynote speaker, data viz/analytics expert, and specialist in efficiency/process improvement, he is indeed a much sought-after resource, speaking at dozens of conferences across the globe annually, earning him coveted thought-leadership recognition by organizations such as DataManagement University, Engatica, Sessionize, and the Global AI Hub.

Matt earned a MS in Physical Chemistry from the University of Rochester and a PGDip with distinction from DeMontfort University in Industrial Data Modeling. He began his career interfacing and automating instrumentation and sensors. When he joined General Electric 2004, he focused his career on applying the scientific method to analytics problems. Matt has applied data science to a varied array of fields including chemometrics, sustainability, industrial internet, and compliance.

Maneesh Shrivastav is an accomplished, well rounded professional with exceptional academic credentials, deep clinical and technical knowledge and experience in Medical Devices spanning business development, market development, strategy, predictive analytics, and engineering across several therapeutic areas. Maneesh has a proven track record of successful international product launches, strategic partnerships deals, and commercialization schemes with start-ups and well established organizations. He is currently engaged in the intersection of market development and predictive analytics at Medtronic.

Sara Stevens is Vice President of Analytics Operations at Capital District Physicians’ Health Plan (CDPHP), a non-profit, physician-governed insurer in upstate New York.  In this role, Sara leads a team of data scientists, informatics analysts, and statisticians to provide analytical insights to internal and external constituents.  Her specialty is in the conception, implementation, and evaluation of value-based provider payment programs designed in pursuit of the quadruple aim.

Sara’s background in healthcare finance, along with her education in math and business, give her a unique and valuable perspective on alternate payment methods in healthcare.  Her ability to distil complex data sets down to consumable, actionable insights, coupled with her passion for impacting meaningful change in the healthcare landscape, give her a distinctive voice in the discussion around the use and adoption of analytics in this industry.

Previously Nathan was a data scientist on the Wells Fargo Enterprise Analytics and Data Science team where he led a small team as head of Natural Language Processing (NLP) and Speech Capabilities Development, and was focused on building out Wells Fargo's capabilities in areas related to NLP, deep learning and data science product design. Nathan holds a Masters in Predictive Analytics from Northwestern University and is working on his second Masters in Computer Science from Georgia Tech. He has been with Wells Fargo for the past five years and worked in marketing analytics prior to his current role.

Mike Thurber is the Lead Data Scientist in Elder Research's Commercial Analytics Group working across multiple teams and industries – including finance, retail, energy, and telecom – to deliver information products that drive business value.  Mike’s primary focus is healthcare and insurance, where his projects range from predicting extreme payouts on long-term care claims, and identifying healthcare provider fraud, to measuring the effect of Cesarean delivery on infant health. His expertise in collaboration, data exploration, predictive modeling and rigorous testing, and in remediating the selection bias common to analytic algorithms, creates confidence in the actions recommended by the analytic products of his team.

Mike earned a BS degree in Chemical Engineering from Brigham Young University and a Master's degree in Statistics from Virginia Commonwealth University. For the last four years, Mike has been teaching principles and best practices of predictive modeling to a broad audience of emerging data scientists.

Jaya Tripathi is a Principal Scientist and an advanced analytics expert in The MITRE Corporation’s Information Technology Technical Center. 

She is the principal investigator in MITRE's effort to apply health IT concepts to address prescription drug misuse and abuse. She has been working in this domain for many years and has collaborated with law enforcement officials, physicians, policy makers, Board of Pharmacy, academicians and state and Federal partners.

She has been at MITRE for 14 years. Before joining MITRE, Ms. Tripathi worked at several multinational corporations, applying big data analytics on projects such as a customer retention forecasting pilot for a major telecommunications company, and the origin-and-destination revenue management, one of the most significant innovations in the airline industry. She holds master’s degrees in physics and computer science from the University of Texas.