June 19-22, 2017
Chicago, IL
Delivering on the promise of data science

Speakers Predictive Analytics World Chicago 2017
 Halim Abbas

Halim Abbas

Vice President of Data Science

Cognoa

Halim is a high tech innovator who spearheaded world-class data science projects at game changing tech firms such as eBay and Quixey. Formally educated in Machine Learning, his professional expertise span Information Retrieval, Natural Language Processing, and Big Data. Halim has a proven track record of applying state of the art data science techniques across industry verticals such as eCommerce, web & mobile services, airline, BioPharma, and the medical technology industry.

He currently leads the Data Science department at Cognoa, a data driven behavioral health care Palo Alto startup.

Session: Early Screening for Autism By Combining Question-Based and Video-Based Predictors

 Dean Abbott

Dean Abbott

Co-Founder and Chief Data Scientist

SmarterHQ

@deanabb

Dean Abbott is Co-Founder and Chief Data Scientist of SmarterHQ, and President of Abbott Analytics, Inc. in San Diego, California. Mr. Abbott is an internationally recognized data mining and predictive analytics expert with over two decades of experience applying advanced data mining algorithms, data preparation techniques, and data visualization methods to real-world problems, including fraud detection, risk modeling, text mining, personality assessment, response modeling, survey analysis, planned giving, and predictive toxicology.

Mr. Abbott is the author of Applied Predictive Analytics (Wiley, 2014) and co-author of IBM SPSS Modeler Cookbook (Packt Publishing, 2013). He is a highly-regarded and popular speaker at Predictive Analytics and Data Mining conferences and meetups, and is on the Advisory Boards for the UC/Irvine Predictive Analytics Certificate as well as the UCSD Data Mining Certificate programs.

He has a B.S. in Mathematics of Computation from Rensselaer (1985) and a Master of Applied Mathematics from the University of Virginia (1987).

Keynote: How Predictive Modelers Can Benefit from Big Data without Big Headaches

Workshop: Supercharging Prediction with Ensemble Models

Workshop: Advanced Methods Hands-on: Predictive Modeling Techniques


 Afsheen Alam

Afsheen Alam

Program Manager Marketing Analytics and Big Data

Allstate Insurance

Afsheen Alam has successfully implemented several data and analytics solutions to drive results. She has lead successful teams and implementations through collaborative processes. Afsheen has been quick to introduce new technologies in the newly changing environment to drive change.

Session:  Case Study: Allstate Insurance - Our Success with Agile Analytics

 Bryan Bennett

Bryan Bennett

Professor

Northwestern University

Bryan Bennett is a professor for Northwestern University's School of Professional Studies where he is a predictive analytics subject matter expert and is responsible for the development and teaching of predictive analytics courses domestically and internationally. Additionally, he teaches leadership, healthcare marketing and consumer behavior courses at the graduate and undergraduate levels for other Universities.

Professor Bennett is also the Executive Director for the Healthcare Center of Excellence (healthcarecoe.org), a privately-funded healthcare research and consulting firm, where he researches and consults on transformation, advanced analytics and leadership issues for healthcare organizations. He is the author of the books, "Competing on Healthcare Analytics: The Foundational Approach to Population Health Analytics" and "Prescribing Leadership in Healthcare", as well as contributing the Data Stewardship chapter to the book "Adaptive Health Management Information Systems".

Bryan's work has appeared in several publications such as, DM News, Health Data Management, Becker's Hospital Review and Capco's Journal of Financial Transformation.

Session: Visualization of Analytics Results - Critical for Communication

 Richard Boire

Richard Boire

Senior Vice President

Environics Analytics

Richard Boire, B.Sc. (McGill), MBA (Concordia), is the founding partner at the Boire Filler Group, a nationally recognized expert in the database and data analytical industry and is among the top experts in this field in Canada, with unique expertise and background experience. Boire Filler Group was recently acquired by Environics Analytics where I am currently senior vice-president.

Mr. Boire's mathematical and technical expertise is complimented by experience working at and with clients who work in the B2C and B2B environments. He previously worked at and with Clients such as: Reader's Digest, American Express, Loyalty Group, and Petro-Canada among many to establish his top notch credentials.

After 12 years of progressive data mining and analytical experience, Mr. Boire established his own consulting company - Boire Direct Marketing in 1994. He writes numerous articles for industry publications, is a well-sought after speaker on data mining, and works closely with the Canadian Marketing Association on a number of areas including Education and the Database and Technology councils. He is currently the Chair of Predictive Analytics World Toronto.

Session: Integrating Segmentation with Predictive Models-Building More Robust Solutions

 Thomas Brandenburger

Thomas Brandenburger

Associate Professor

South Dakota State University

Dr Thomas Brandenburger is an Associate Professor of Statistics at South Dakota State University and credit risk researcher and consultant. He has developed new methods for credit risk scorecards that address many of the unique issues encountered which are unique to scorecards. Edward Krueger is a senior manager of credit risk at Bluestem Brands, Inc. Allison Lempola is a Senior Data Scientist and consultant for RProfet specializing in credit risk scorecards. They collaborate to build open source credit scoring tools, which address issues in credit scorecards that often are overlooked in proprietary platforms.

Session: Crediting Scoring; Advanced Methods - An R Based Variable Transformation and Selection Tool for Credit Scorecards

 James Casaletto

James Casaletto

Senior Solutions Architect

MapR Technologies

James Casaletto is a senior solutions architect at MapR Technologies where he designs, implements, and deploys complete solution frameworks for big data. He has written and delivered courses on MapReduce programming, data engineering, and data science on Hadoop. Today, he is also teaching a graduate course in these topics for the computer science department at San Jose State University.

Workshop: Hadoop for Predictive Analytics: Hands-On Lab

 Morgane Ciot

Morgane Ciot

Data Visualization Engineer

DataRobot

Morgane Ciot is a data visualization engineer at DataRobot, where she specializes in creating interactive and intuitive D3 visualizations for data analysis and machine learning. Morgane studied computer science and linguistics at McGill University in Montreal. Previously, she worked in the Network Dynamics Lab at McGill, answering questions about social media behavior using predictive models and statistical topic models.

Panelist: Women in Predictive Analytics: Opportunities and Challenges

Dr. John Elder

Dr. John Elder

CEO & Founder

Elder Research, Inc.

@johnelder4

John Elder chairs America’s most experienced Data Science consultancy. Founded in 1995, Elder Research has offices in Virginia, Maryland, North Carolina and Washington DC. 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, and was named by President Bush to serve 5 years on a panel to guide technology for national security.

Keynote: What to Optimize? The Heart of Every Analytics Problem

 Robert Grossman

Robert Grossman

Partner

Open Data Group

Robert L. Grossman has been building and deploying predictive models for over 20 years.  He is the Founder and Chief Data Scientist at Open Data Group, which develops technology for deploying predictive models. He is also a partner at Analytic Strategy Partners, which provides consulting in the strategy and practice of analytics.  He is the Frederick H. Rawson Professor of Medicine and Computer Science and the Director of the Center for Data Intensive Science (CDIS) at the University of Chicago, where he focuses on the applications of data science to problems in biology, medicine and health care.  He has been involved in the development of standards for analytics, including the Predictive Model Markup Language (PMML) and the Portable Format for Analytics (PFA).

Session: What is the Analytic Maturity of Your Company and Five Ways to Improve It

 Jeanne G. Harris

Jeanne G. Harris

Faculty

Columbia University of New York

Jeanne G. Harris is on the faculty of Columbia University of New York, where she teaches a graduate level course on Business Analytics Management.  Jeanne is also executive research fellow emerita of the Accenture Institute for High Performance. Before retiring, she was the Global Managing Director of Information Technology Research at the Accenture Institute for High Performance in Chicago. At Accenture, she led the Institute's global research agenda in the areas of information, technology, and analytics.

She is the co-author with Tom Davenport of the extensively updated new edition of "Competing on Analytics: The New Science of Winning" which will be published by Harvard Business Review Press, September, 2017. "Competing on Analytics, 2nd ed." demonstrates how high performance businesses are successfully leveraging big data, machine learning, AI, optimization and other analytical techniques; thereby building competitive strategies around data-driven insights that are generating outstanding business performance. Harvard Business Review editors named the first edition of the book one of the top breakthrough ideas of the 21st Century.  In 2009, Jeanne received Consulting Magazine's Women Leaders in Consulting award for Lifetime Achievement.

Panelist:   Women in Predictive Analytics: Opportunities and Challenges

 Lauren Haynes

Lauren Haynes

Senior Project Manager

Center for Data Science and Public Policy at The University of Chicago

Lauren Haynes is Senior Project Manager at DSaPP, where she serves as a translator between data scientists and non-profit and government agency personnel. Before joining DSaPP, Lauren was Product Manager at GiveForward, supporting an agile team developers and designers. She was previously IT Manager and interim CIO at the Ounce of Prevention Fund, a non-profit focused on early childhood education and advocacy with 250 staff and a $50 million operating budget. She was a consultant with Accenture's Technology Labs where her projects focused on Healthcare, Knowledge Management, and Collaboration while working with clients including Shell and DuPont.

Lauren is chair of the Board of Directors for Break Away, a national nonprofit focused on service learning trips for college students. Lauren also sits on the Board of Trustees for the University YMCA at the University of Illinois at Urbana Champaign. In her spare time, Lauren travels to Lindy Hop dance events. She holds a BS in General Engineering from UIUC, where her secondary field was in Human Computer Interaction.

Session: Data Science for Social Good: How Predictive Analytics Can Help Governments and Non-Profits
Panelist: Women in Predictive Analytics: Opportunities and Challenges

 Mark Heiler

Mark Heiler

Data Scientist

Paychex Inc.

Mark Heiler is a data scientist at Paychex Inc., a leading provider of payroll, human resource, insurance, and benefits outsourcing solutions for small- to medium-sized businesses. Mark's role is creating statistical and predictive models to assist in a wide array of business needs, including: text analysis, predictive retention/upsell models, and statistical inference on the effect of business decisions. Mark received his Master's degree in Biostatistics from University at Buffalo, where he focused on using machine learning techniques for classification on high-throughput biological data.

Session: Retention Modeling in Uncertain Economic Times

 Herman Jopia

Herman Jopia

First Vice President and Data Analytics Manager

American Savings Bank

Herman Jopia is First Vice President and Data Analytics Manager at American Savings Bank. Industrial engineer with a master’s degree in statistics and an MBA, Herman has ten years of experience in retail banking in Chile and US, leading the analytic units of credit risk and marketing, managing several data driven projects such as credit scoring, response and attrition models, loss forecasting, and price optimization; monitoring key performance metrics of the portfolio, handling credit bureaus and analytic vendors, and reporting on a regular basis to top management and regulators.

Herman is also author of the R package "Optimal Binning for Scoring Modeling", an open source code that reduces the time consuming process of generating predictive characteristics for modeling.

Herman also develops talent through the Analytics Internship at ASB for top students in Hawaii and leads the Honolulu R Users Group, which he founded early in 2015.

Keynote: Driving Growth And Profitability Through Scoring Modeling, Programming, and Price Optimization

 William Komp

William Komp

Senior Data Scientist

SmarterHQ

Dr William Komp is a senior data scientist at SmarterHQ. In addition to marketing analytics he also has experience with healthcare analytics and is an adjunct professor of theoretical physics at the University of Louisville. He was technical editor for Applied Predictive Analytics: Principles and Techniques for the Professional Data Analyst by Dean Abbott for J. Wiley and Sons and a peer reviewer for the Canadian Journal of Physics.

Session: Automated Retail Analytics - Omni-Channel and at Scale

 Max Kuhn

Max Kuhn

Software Engineer

RStudio

Max Kuhn is a software engineer at RStudio. a leading company for R software and tools. He is currently working on improving R's modeling capabilities. He has a Ph.D. in Biostatistics.

Max was a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max is the author of eight R packages for techniques in machine learning and reproducible research and is an Associate Editor for the Journal of Statistical Software. He, and Kjell Johnson, wrote the book Applied Predictive Modeling, which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015.

He has taught courses on modeling, including many classes for Predictive Analytics World, the useR! conference, the Open Data Science Conference, the India Ministry of Information Technology, and others.

Workshop:  R Bootcamp: For Newcomers to R

Workshop:  R for Predictive Modeling:A Hands-On Introduction

 Kwan Lee

Kwan Lee

CTO

AcademicMerit

Kwan is currently CTO at AcademicMerit leading efforts in architecting software to understand student outcomes and student/teacher interactions in large scale online environments.  In his previous role he has been engineering and architecting data driven software to enhance sourcing and tracking of growth stage B2B software companies targeting the US market. He is an expert in social computing and has published peer reviewed papers and built many systems that provide insights through computational algorithms, distributed computing and human participation. Prior to AcademicMerit, he was at OpenView Venture Partners and Redstar Ventures that inspired his work on analyzing data and building software to make better decisions and enhance team, product and technology for startup companies. He has also worked at Bose, Intuit, Bank of America and GTE. Kwan completed his S.M. and Ph.D. degrees from MIT Media Lab and M.Eng. and B.S. degrees in computer science from Cornell University.

Session: Predicting the Future Success of B2B Software Companies

 Nick Lucius

Nick Lucius

Data Scientist, Advanced Analytics

City of Chicago

Nick is data scientist for the City of Chicago, and before that he spent a decade as a government attorney and senior official in both state and local government. Nick has spent considerable time working on litigation related to the foreclosure crisis and advising cabinet-level officials on policy development and legal issues. Nick also served as a chief administrative law judge, overseeing Illinois' legal appeals system for millions of people enrolled in Medicaid and many other federal healthcare and human service programs.

As a data scientist, Nick joins his operational and technical knowledge to identify impactful projects, and applies advanced analytics to create insights tailored for human-actionable decisions. Throughout his time in government, Nick has used data analytics to bring about quick and significant impact, streamlining processes and improving services.

Nick has a law degree and a master's degree in computer science from DePaul University, and a bachelor's degree from Ohio State University.

Session: Predicting Water Quality in Lake Michigan's Swimming Beaches

 Holly Lyke-Ho-Gland

Holly Lyke-Ho-Gland

Principal Research Lead

APQC

Holly Lyke-Ho-Gland is a principal research lead at APQC, with over ten years of business research and consulting experience. Her focus has predominantly been on best practices in business processes, change management, corporate strategy, and R&D. In her role as principal research lead for process and performance management at APQC, she is responsible for conducting, publishing, and presenting on research in process management and improvement, quality, project management, measurement, and benchmarking.

Session: Change Management for Establishing a Data-Driven Culture

 Haile Owusu

Haile Owusu

Chief Data Scientist

Mashable

@hailekofi

Haile Owusu is Chief Data Scientist at Mashable where his main responsibility is the development and refinement of the company's proprietary Velocity technology, which predicts and tracks the viral life-cycle of digital media content. Haile specializes in statistical learning as applied to predictive analytics and has a background in theoretical physics, including a Ph.D from Rutgers University, a Masters of Science from King's College, University of London and a B.A. from Yale University.

Keynote: The Centrality of a Detailed Understanding of your Audience

 Allison Pelletier

Allison Pelletier

Senior Data Scientist

RProfet

Allison is deeply involved in all aspects of the modeling process. She is a subject matter expert in credit modeling as well as the development of the regular reporting processes and documentation necessary to create data driven decisions.


Her deep data skills combined with expert modeling techniques is a rarity in the analytics world. Allison earned a BA in Mathematics Education and an MS in Statistics. In her research she developed new statistical power calculations for measuring mixtures of non-normal distributions to measure the profitability in A/B testing in credit card customer behavior.

Session: An R Based Variable Transformation and Selection Tool for Credit Scorecards

 Jennifer Lewis Priestley

Jennifer Lewis Priestley

Professor of Applied Statistics and Data Science

Kennesaw State University

Dr. Jennifer Lewis Priestley, Ph.D., is a Professor of Applied Statistics and Data Science at Kennesaw State University, where she is the Director of the Center for Statistics and Analytical Services. She oversees the Ph.D. Program in Advanced Analytics and Data Science, and teaches courses in Applied Statistics at the undergraduate, Masters and Ph.D. levels. In 2012, the SAS Institute recognized Dr. Priestley as the 2012 Distinguished Statistics Professor of the Year. She served as the 2012 and 2015 Co-Chair of the National Analytics Conference. Datanami recognized Dr. Priestley as one of the top 12 "Data Scientists to Watch in 2016."

She has authored dozens of articles on Binary Classification, Risk Modeling, Sampling, Applications of Statistical Methodologies for Problem Solving as well as several textbook manuals for Excel, SAS, JMP and Minitab. Prior to receiving a Ph.D. in Statistics, Dr. Priestley worked in the Financial Services industry for 11 years. Her positions included Vice President of Business Development for VISA EU in London, where she was responsible for developing the consumer credit markets for Irish and Scottish banks. She also worked for MasterCard International as a Vice President for Business Development, where she was responsible for banking relationships in the Southeastern US. She also held positions with AT&T Universal Card and with Andersen Consulting.

Dr. Priestley received an MBA from The Pennsylvania State University, where she was president of the graduate student body, and a BS from Georgia Tech. She also received a certification from the ABA Bankcard School in Norman, OK, and a Certification in Base SAS Programming, and a Business Analyst Certification from the SAS Institute.

Expert Panel: Women in Predictive Analytics: Opportunities and Challenges

 Sri Raghavan

Sri Raghavan

Senior Product Marketing Manager Teradata Aster Analytics

Teradata Aster

Sri Raghavan is a Senior Global Product Marketing Manager for Teradata with more than 20 years of experience developing products, leading advanced analytics/data science agendas, and delivering marketing and sales initiatives that drive the performance and profitability of organizations across the Big Data Applications, Financial Services, Healthcare, and Management Consulting industries.


Sri has a history of managing multiple data science and advanced analytics projects across industries and big data programs to effectively align technology with business goals and financial objectives. Sri has built, trained and supported top-performing global IT teams and has presented and demonstrated a variety of analytic functionality and solutions to customers and in conferences across the U.S. and overseas

Session: Driving high-impact business outcomes with the Art of Analytics

 Steven Ramirez

Steven Ramirez

CEO

Beyond the Arc

@beyondthearc

Steven J. Ramirez is the chief executive officer of Berkeley, Calif.-based Beyond the Arc, Inc., a firm recognized as a leader in helping companies transform their customer experiences by leveraging advanced analytics.

In addition to developing and executing the vision for Beyond the Arc, Ramirez leads teams of data and strategy consultants committed to client success. They analyze customer and social media data, combined with text analysis, to drive customer growth, improve customer retention, understand service breaks and build stronger customer loyalty.

Prior to leading Beyond the Arc, Ramirez served as an executive with Time Warner, where he was responsible for creating and successfully implementing marketing and corporate development strategies.

Ramirez earned a bachelor's degree and master's in Business Administration from the University of California at Berkeley. He as also created and taught courses in business management for UC Berkeley and been a guest speaker at the university's Haas School of Business.

Session: Measuring the Impact of Culture Change Using Advanced Analytics

 Greta Roberts

Greta Roberts

Co-Founder & CEO

Talent Analytics, Corp.

@GretaRoberts

Greta Roberts is an acknowledged influencer in the field of predictive workforce analytics. Her continued vision is to bridge the gap between the business, predictive analytics and workforce communities. Since co-founding Talent Analytics in 2001, Greta has established Talent Analytics, Corp. as the globally recognized leader in predicting employee performance, pre-hire.

In addition to being a contributing author to numerous predictive analytics books, she is regularly invited to comment in the media and speak at high end predictive analytics and business events around the world. Through recognition of her commitment and leadership, Greta was elected and continues to be Chair of Predictive Analytics World for Workforce. Additionally, she is a Faculty Member with the International Institute for Analytics (IIA) and an Analytics Certification Board Member of INFORMS.

Moderator Expert Panel: Women in Predictive Analytics: Opportunities and Challenges

 Pasha Roberts

Pasha Roberts

Co-Founder and Chief Scientist

Talent Analytics, Corp.

@pasharoberts

Pasha Roberts is chief scientist at Talent Analytics Corp., a company that uses data science to model and optimize employee performance in areas such as call center staff, sales organizations and analytics professionals. He wrote the first implementation of the company’s software over a decade ago and continues to drive new features and platforms for the company. He holds a bachelor’s degree in economics and Russian studies from The College of William and Mary, and a master of science degree in financial engineering from the MIT Sloan School of Management.

Session: Using Survival Analytics for Predicting Churn

 Federico Rosenhain

Federico Rosenhain

Chief Data Officer

Banco Hipotecario

Federico Rosenhain has been working in the information business for the last 15 years and specifically in finance for the last 10. He currently leads big data and data science projects, and their integration with data warehouse development by way of a variety of tools.

He also designed and coordinates the Big Data and Analytics program at the Universidad de Palermo.

Session: Building Your Own Real-Time Decision System - Lessons Learned

 Thomas Schleicher

Thomas Schleicher

Sr. Director, Measurement Science

National Consumer Panel

Thomas Schleicher is a results-focused executive with nearly 20 years of experience in delivering actionable, data-based insights and profit-optimizing results for various high-profile clients across multifaceted, competitive industries. He is currently Senior Director of Measurement Science at National Consumer Panel, a joint venture of Nielsen and IRI. NCP is the largest longitudinal consumer panel in the world, and it provides the quality data its parent companies leverage to share consumer insights with their respective clients.

Prior to his current role, Tom has had stints at Ipsos-ASI, Bayer HealthCare (Pharmaceuticals), and smaller analytic shops including Symphony Marketing Solutions and Spire, a Loyalty Marketing Analytics firm. Trained as an Experimental Social Psychologist (Ph.D. earned at the University of Wisconsin - Milwaukee), he is currently augmenting his statistical and social research methods expertise as he nears completion of his online Certificate in Predictive Analytics at UC-Irvine.

Session: Combining Inferential Statistics with Predictive Modeling to Evaluate Changes in Your Business

 Edward Shihadeh

Edward Shihadeh

Chief Data Officer

Auspice Analytics, LLC

As Chief Data Officer at Auspice Analytics, Edward S. Shihadeh, PhD is internationally recognized for the results of his cutting-edge predictive analytic techniques. During his tenure as Professor of Sociology at Louisiana State University, he published extensively in leading journals, won numerous university awards, including the exclusive Senior Rainmaker Scholar Award and the Chancellor's Technology Transfer Award for his college student retention algorithms. He has also amassed an impressive record of external funding totaling in the millions. His data science techniques, which are unique in their consideration of structural factors, are used in the most accurate election predictions this election cycle, reduced the homicide rate in Baton Rouge by 26% in 2013 and by 48% in 2016, increased college student retention in a major university to record levels, and optimized student recruitment in that university.

Session: How to Revolutionize Your Model Optimization

 Michael Sims

Michael Sims

Research Analyst

APQC

Michael Sims is researcher and writer who explores data and analytics and process and performance management best practices and innovations. With an MBA from Rice University and a background in business analysis and process improvement, Michael considers himself a lifelong consumer and sharer of knowledge. Michael's passion for analytics stems from love for efficiency, his undying basketball fandom, and analytics' unparalleled ability to optimize both.

Session: Change Management for Establishing a Data-Driven Culture

 Steven Ulinski

Steven Ulinski

Security Data Scientist

Health Care Service Corporation

Steve Ulinski has over 22 years' experience in information technology. He has a bachelor's degree in General Studies with minors in Computer Information Systems and Psychology. For the past 11 years' he has worked at Health Care Service Corporation in the security operations center supporting the security analysts. Currently he is focusing on researching and applying AI, knowledge management, big data, and predictive analysis to cyber security.

Session: Challenges of Information and Cyber Security Using Predictive Analytics

 Jim Whiting, Ed.D., SPHR

Jim Whiting, Ed.D., SPHR

Global Program Manager in Organizational Development

Nokia

Jim is a Human Resources Business Partner and Organizational Development leader and practitioner, with approximately 20 years of experience at AT&T, America Online, Microsoft, Nokia, and Nokia Siemens Networks. Jim is presently a Global Program Manager in Organizational Development at Nokia focusing on HR Analytics; he has experience on enterprise-wide projects which span 150 countries. Jim has worked on the cultural integrations of divisions of Siemens and Motorola, and Alcatel-Lucent into Nokia, along with other innovative change management initiatives.

Jim has a Master's Degree in Human Relations from the University of Oklahoma and a doctorate degree in Organizational Leadership from Argosy University-Sarasota; his dissertation study focused on creative problem solving within a business context.

Session: Measuring the Impact of Culture Change Using Advanced Analytics

 Chao Zhong

Chao Zhong

Senior Data Scientist

Microsoft

Chao is a Senior Data Scientist at Microsoft. Prior to Microsoft, Chao was the Lead Data Scientist at Scopely, a mobile gaming company in LA. Chao was a Ph.D. ABD (all but dissertation) in Mathematics from Michigan Technological University. He holds a M.S. degree in Financial Engineering from Temple University, and a B.S. degree in Computer Science from Beijing University of Aeronautics and Astronautics. His current research interests include (deep) machine learning for customer journey and customer lifetime value, (deep) reinforcement learning for interactive customer behavior modeling.

Session: Using Predicting Customer Lifetime Value for a Subscription Based Business

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