Predictive Analytics World for Healthcare 2020

John Ainsworth is a senior data scientist employed by the Universityof Virginia Health System since July of 2014. He is currently workingwith the UVa Medical Center on a variety of predictive analyticprojects including the CMS AI Challenge where his team was selected asone of 25 competitors. Prior to coming to UVA, John designed,implemented, deployed, and monitored predictive analytic solutions fora wide variety of industries for Elder Research, a predictiveanalytics consulting company.

Michael Ashby, M.D. retired as Sentara Martha Jefferson Hospital's Vice President, Medical Affairs in October 2019.  He served as a liaison between the Medical Staff, the Administration, and the Hospital Board for 16 years.  He assisted with the coordination of performance improvement, quality and safety related activities, utilization management and risk management activities at the Hospital.  He was the Hospital’s Patient Safety Officer.  He served as staff and coordinated the Hospital Board Quality Care Committee.  He participated in strategic planning, especially as it pertained to the Medical Staff, as well as planning, physician education and implementation of the Hospital electronic medical record.  He coordinated continuing medical education for physicians at the hospital.  He assured accurate and complete Medical Staff credentialing and recredentialing. 

Dr. Ashby worked in the Martha Jefferson Emergency Department 1989 through 2014.  Prior to becoming Vice President, Medical Affairs in 2003, he was Medical Director of the Emergency Department.  He has served as Emergency Medicine Section Chief, Secretary, Vice President, and President of the Medical Staff as well as Chair of the Medical Executive Committee.   He is Board Certified in Family Medicine and is a Fellow of the American College of Emergency Physicians and the American Academy of Family Physicians.  He served as a Board Member of the Jefferson Area Board for Aging.  He has served as the Virginia College of Emergency Physicians representative on the Health and Medical Sub-panel, Governor’s Secure Commonwealth Initiative and the Pan Flu Advisory Committee. He served in the U.S. Army Medical Corps from 1982-1989.  He is enjoying retirement, spending time with his family, traveling with his wife, fishing, learning to play mandolin, and has photos of his new granddaughter to share. 

Ali Boolani, PhD is an Associate Professor at Clarkson University. He completed his Bachelors and Master degrees at Tulane University in International Relations with a focus on South East Asia. He changed career paths and went on to receive his Masters in Applied Physiology at University of New Orleans, a PhD in Applied Physiology at Oklahoma State University and completed a post-doctoral fellowship at the University of Georgia in Exercise Psychology. He is currently pursuing a Master of Data Science with a certificate in Artificial Intelligence at The Johns Hopkins University. Ali’s research focuses on improving objective and subjective measurements of energy and fatigue, predicting moods using human movement, the influence of mood traits on mood states and interventions to improve moods. His most recently published work showed that energy and fatigue are physiologically distinct moods with distinct interventions to improve each mood state. He also recently published work that used machine learning to identify changes in feelings of energy through changes in postural control.

Daniel Chertok, Ph.D., is a Sr. Data Scientist at NorthShore University HealthSystem. He is responsible for developing predictive models for population health management, operational cost containment and staffing optimization. Additionally, Daniel provides thought leadership on best analytical practices and has authored an internal standard operating procedures manual implemented by the Clinical Analytics team. His previous experience includes work in quantitative finance. Daniel holds a Ph.D. in Applied Mathematics from Simon Fraser University in Canada and an Eng. Math in Applied Mathematics from Peter the Great St. Petersburg Polytechnic University.

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.

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.

Colleen Farrelly is a co-founder and the Chief Scientist of Quantopo, LLC.  She was the lead statistician working with the Malian government on the 2014 Ebola outbreak and public health response, and her prior industry work has spanned TRICARE contracts for the US Navy, research and development at Cypher Genomics for the Genome England Competition and Celgene failed drug revival pipeline, consulting work within cardiology analytics and clinical trials, and predictive analytics work at Graham Holdings (Kaplan Higher and Professional Education), as well as educational work at Purdue University Global on their new analytics program. She is a writer for KDnuggets and is attempting her first lay audience analytics book at the moment.

Jen Gennai leads Google’s Responsible Innovation team which is responsible for operationalizing Google’s AI Principles, ensuring that Google’s products have fair and ethical outcomes on individual users and the world. Her team works with product and engineering, leveraging a multidisciplinary group of experts in ethics, human rights, user research, racial justice and gender equity to validate that products and outputs align with our commitments to fairness, privacy, safety, societal benefit and more. Before she co-authored the AI Principles and founded Responsible Innovation, Jen worked on machine learning fairness and founded the Ethical ML team in Trust & Safety.

Michael has a track record of translating organizational priorities into coherent, analytics strategies that are enabling better care for patients. Prior Front Health, he spent 2 years leading analytics and technology for the Midwest Health CollaborativeHe has also worked at ICC, a technology consulting company that acquired his analytics startup Farsite.

Rick is a technology entrepreneur and consultant working with analytics-focused startups and mature firms. Rick’s experience includes a healthcare workforce analytics start-up, a venture-backed firm focused on online investment and financial planning, an online political prediction market startup, and a leading Microsoft partner focused on cloud-based productivity solutions. He’s passionate about how design, data, and analytics can help solve some of our toughest societal challenges. Rick received an MBA from George Washington University and a BA in Government & Politics from the University of Maryland. He serves as a member of the leadership council for the Virginia Center for Health Innovation, and Board Chair for Smart C-ville.  A DC transplant, he lives in Charlottesville with his wife and dog and, on occasion, can be found running the hilly streets of C-ville.

Data Scientist with 7+ years of experience developing and implementing predictive analytic solutions. Worked on BI teams across different industries and organizational verticals. Developed R Programming Learning Studio for Northwestern University. Led data science teams at two large healthcare providers. Worked on BI and data science teams in the pharmaceutical and payer spaces as an external consultant.

Trained originally in Ecosystem Analysis & Modeling, Dr. Nisbet modeled forests at the University of California, Santa Barbara.  He moved to NCR in 1994, developing configurable data mining applications for customer Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications.  He has worked also in Insurance, Banking, Credit, membership organizations (e.g. AAA), and Health Care industries. He is lead author of the award-winning Handbook of Statistical Analysis & Data Mining Applications (Academic Press, 2009, 2017), and a co-author and general editor of the award-winning "Practical Text Mining" (Academic Press, 2012) and Practical Predictive Analytics and Decisioning Systems in Medicine (Academic Press, 2015).  A new book on Effective Data Preparation is available from with Cambridge University Press.  Currently, he serves as an Instructor in the University of California at Irvine Predictive Analytics Certificate Program, teaching many online and on-campus courses each year in Effective Data Preparation and Predictive Analytics Applications.

Dr. Zeydy Ortiz is the co-founder & CEO of DataCrunch Lab, LLC.  She has been helping teams and organizations transform data into value across many industries including IT,  financial, retail, and the manufacturing sectors.  Her team built an innovative, award-winning digital assistant that was recognized as 'Highest Potential Value to Manufacturers' for increasing visibility of real-time production and plant operations. 

She started her career as a Performance Engineer at IBM building predictive models to inform business strategy.  She worked on multiple projects focused on improving performance & efficiency.  She earned recognition for innovation on Smarter Planet/IoT solutions and for her industry contributions defining resource efficiency metrics. Dr. Ortiz obtained her bachelor's degree from the University of Puerto Rico, master's from Texas A&M University, and Ph. D. in Computer Science from North Carolina State University.

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.

Fred Rahmanian is Geneia’s chief analytics and technology officer. He brings more than 20 years of experience as a healthcare data scientist and software architect and a track record of building applications that integrate diverse data sources to solve the most sophisticated problems in healthcare. Rahmanian was recently named to Industry Era’s list of 10 Best CTOs of 2019. Called a “true technological visionary,” he earned this distinction for “his decades of experience in software development, data analytics and artificial intelligence” and “his creation and leadership of the Geneia Data Intelligence Lab (GDI Lab).”

Before joining Geneia, Rahmanian was principal data scientist at IBM Watson Health. He also held leadership roles at KPMG and Siemens Healthcare. Rahmanian has been awarded patents for his work to improve patient data; two are granted and three are pending.
He earned a bachelor’s degree in computer and information science and a master’s degree in software engineering from the University of Maryland University College. He also has a graduate certificate in health informatics from Columbia University. He continues to be a guest lecturer in Columbia’s HIT certificate program, and has served on many governmental and industry expert panels, including the HIMSS Task Force on Healthcare Quality Measure Development.

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.

Vickie Rice is a 20-year veteran of the benefits business and an expert in healthcare claims, analytics and product management. Rice spent a decade in key administrative roles at Blue Cross and Blue Shield of Oklahoma and then served as Product Manager for Data and Analytics at Benefitfocus where she helped create innovative data tools to help both benefits administrators and consumers make fact-based decisions about their healthcare benefits.  

In her current role as VP of Innovative Strategies for CareATC, Rice has brought her passion of using data and technology to help patients live their healthiest lives to the Product Strategy team, leading them in their mission of offering world class healthcare services and solutions to our patients, providers and employer clients.

Mohammad is currently a Senior Data & Applied Scientist at Microsoft, and Instructor at Stanford University. He is a former Data Scientist at Apple and previously worked for Samsung, Bosch, General Electric and UCLA Research Labs. He received a PhD in Computer Science from the University of California, Riverside and B.Sc. from University of Tehran. Mohammad is the author of the book, ‘Applications of Mining Massive Time Series Data'. He has also been a keynote speaker at more than 40 Data Summits/Conferences around the globe. 

Eric Siegel, Ph.D., is a leading consultant and former Columbia University professor who helps companies deploy machine learning. He is the founder of the long-running Machine Learning Week conference series and its new sister, Generative AI Applications Summit, the instructor of the acclaimed online course “Machine Learning Leadership and Practice – End-to-End Mastery,” executive editor of The Machine Learning Times, and a frequent keynote speaker. He wrote the bestselling Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die, which has been used in courses at hundreds of universities, as well as The AI Playbook: Mastering the Rare Art of Machine Learning Deployment. Eric’s interdisciplinary work bridges the stubborn technology/business gap. At Columbia, he won the Distinguished Faculty award when teaching the graduate computer science courses in ML and AI. Later, he served as a business school professor at UVA Darden. Eric also publishes op-eds on analytics and social justice.

Eric has appeared on Bloomberg TV and Radio, BNN (Canada), Israel National Radio, National Geographic Breakthrough, NPR Marketplace, Radio National (Australia), and TheStreet. Eric and his books have been featured in Big Think, Businessweek, CBS MoneyWatch, Contagious Magazine, The European Business Review, Fast Company, The Financial Times, Forbes, Fortune, GQ, Harvard Business Review, The Huffington Post, The Los Angeles Times, Luckbox Magazine, MIT Sloan Management Review, The New York Review of Books, The New York Times, Newsweek, Quartz, Salon, The San Francisco Chronicle, Scientific American, The Seattle Post-Intelligencer, Trailblazers with Walter Isaacson, The Wall Street Journal, The Washington Post, and WSJ MarketWatch.

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.