Dean Abbott, President, Abbott Analytics
Dean Abbott is President of Abbott Analytics in San Diego, California. Mr. Abbott has over 21 years of experience applying advanced data mining, data preparation, and data visualization methods in real-world data intensive problems, including fraud detection, risk modeling, text mining, response modeling, survey analysis, planned giving, and predictive toxicology. In addition, Mr. Abbott serves as chief technology officer and mentor for start-up companies focused on applying advanced analytics in their consulting practices.
Mr. Abbott is a seasoned instructor, having taught a wide range of data mining tutorials and seminars for a decade to audiences of up to 400, including PAW, KDD, AAAI, IEEE and several data mining software users conferences. He is the instructor of well-regarded data mining courses, explaining concepts in language readily understood by a wide range of audiences, including analytics novices, data analysts, statisticians, and business professionals. Mr. Abbott also has taught applied data mining courses for major software vendors, including SPSS-IBM Modeler (formerly Clementine), Unica PredictiveInsight (formerly Affinium Model), Enterprise Miner (SAS), Model 1 (Group1 Software), and hands-on courses using Statistica (Statsoft), Tibco Spotfire Miner (formerly Insightful Miner), and CART (Salford Systems).
Case Study: Hiring and Selecting Key Personnel Using Predictive Analytics
Workshop: Advanced Methods Hands-on: Predictive Modeling Techniques
Kamaljit Anand, Global Head of Client Delivery, KIE Square Consulting
Kamal Anand is Global Head of Client Delivery at KIE Square Consulting. In the last several years, he has served as an Analytics and Business Solutioning Expert across CPG, Retail and BFSI. He is also Statistical Advisor to Revenue and Enforcement departments of Multiple Economies. He has been a Visiting Professor in the area of Marketing and Data Analytics with several reputed management institutions across the globe. Kamal has been a panel speaker in few key conferences and has also authored articles in refereed journals and magazines. Prior to KIE Square, Kamal was Practice Head and Director of Marketing Optimization and Financial Services Practices at Fractal Analytics. He has worked closely with several Fortune 100 companies. Prior to Fractal, Kamal worked for Gallup Organization in the area of Customer Engagement and Market Research Analytics. Kamal holds a Doctorate from Indian Institute of Management (IIM), Ahmedabad, with focus on Quantitative Marketing.
Case Study: Text Mining in Advanced Government Compliance Set-ups
John Bates, Adobe
Bio is Forthcoming
Case Study: Adobe
Richard Boire, Partner, Boire Filler Group
Richard Boire is a recognized authority on predictive analytics and is among a very few, select top five experts in this field in Canada, with expertise and knowledge that is difficult, if not impossible to replicate in Canada. This expertise has evolved into international speaking assignments and workshop seminars in the U.S., England, Eastern Europe, and Southeast Asia.
Within Canada, he gives seminars on segmentation and predictive analytics for such organizations as Canadian Marketing Association (CMA), Direct Marketing News, Direct Marketing Association Toronto and the Association for Advanced Relationship Marketing (AARM.). His written articles have appeared in numerous Canadian publications such as Direct Marketing News, Strategy Magazine, and Marketing Magazine. He has taught applied statistics, data mining and database marketing at a variety of institutions across Canada which includes University of Toronto, George Brown College, Seneca College, etc. Richard is currently Chair at the CMA's Customer Insight and Analytics.
Case Study: Insurance Pricing Models using Predictive Analytics
Richard Brath, Partner, Oculus
Richard Brath is a partner at Oculus, a firm focused on research and
consulting on the strategic uses of information visualization to Fortune
500 and software companies with an emphasis on innovative visual solutions for
real-world business problems from conception to enterprise implementation.
Richard helps customers maximize the value of visualization by addressing the many facets of creating effective visualizations: visual representations, graphics technologies, mobile devices, predictive models, and tight analytic integration.
Richard is an ongoing contributor to information visualization research with more than 10 peer-reviewed published papers and was the opening keynote for the IEEE Information Visualization conference IV09. Richard provides ongoing visualization strategy consulting to a top 5 NYC financial services firm; a top 10 pharmaceutical company; and a top 10 US CRM software organization. Richard holds degrees in Architecture and Computer Science.
Case Study: Visualizing Forecast Models with Interactive Scenario Analysis to Optimize Profitability
Ozgur Cetiner, Director of Statistical Analysis, Capital One Canada
Ozgur Cetiner is the Director of Statistical Analysis at Capital One Canada. He has over 15 years of experience in customer analysis and modeling, and has worked in a variety of organizations in Turkey, Germany, the US and Canada. He has B.Sc. in Industrial Engineering from the University of Bosphorus in Istanbul and an M.Sc. in the same field from Virginia Tech.
Ozgur first worked in the Marketing and Analysis Department of Capital One in Richmond, VA, before moving to Toronto to work for TD Canada Trust. There, he led a team of statisticians to focus on deepening customer relationships while optimizing customer profitability.
After returning to Capital One in 2007, Ozgur built a highly effective team of statisticians from the ground up. His team develops analytical algorithms and predictive models for each phase of the customer life cycle, from targeting to recoveries, for the entire line of business.
Case Study: Competitive Training of Predictive Modelers
Tim Daciuk, Business Development Manager, Advanced Analytics, IBM Global Business Services
Tim Daciuk is a Business Development Manager with IBM. Tim works to help customers, and potential customers better understand the value of Predictive Analytics; how that aligns with the IBM software family, and, how it aligns with customer business strategies. Tim provides customers with everything from a business understanding to in-depth technical demonstrations of the Predictive Analytics product suite in action. Additionally, Tim is an accomplished speaker and has spoken at conferences, meetings and professional seminars throughout Canada, the U.S., Europe and Asia. Tim also leads several seminars in Predictive Analytics across North America for audiences from technical specialists to business decision makers.
Tim Daciuk has a 30 year history in statistics, data mining and predictive analytics. He has worked in roles as a consultant, trainer, pre-sales, and marketing. Tim has worked with both Public Sector and Commercial endeavors, as well as serving as an advisor to many academic research projects. Of late, Tim has specialized in the use of data and text mining and how these technologies can be applied in a business context, across industries. Tim works closely with industry and software leaders to help business government and institutions understand and unlock the power of predictive analytics.
Case Study: If We Host It; Will They Come? Predictive Modeling for Event Marketing
Jeff Deal, Vice President of Operations, Elder Research, Inc.
Jeff Deal is the Vice President of Operations at Elder Research Inc. where he manages client engagements from the business perspective. In his four years at Elder Research, 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 mining engagements. From his vantage point, Jeff sees the common business mistakes that are made by businesses and by the data mining experts and will share these insights in his talk. Prior to joining Elder Research, Jeff worked in the health care field as a hospital administrator and with his own health care consulting business. Jeff has a Master of Health Administration degree from Virginia Commonwealth University and an undergraduate degree from the College of William and Mary.
Case Study: Top 10 Data Mining Business Mistakes
John Elder, CEO & Founder, Elder Research, Inc.
Dr. John Elder heads a data mining consulting team with offices in Charlottesville, Virginia and Washington DC. Founded in 1995, Elder Research, Inc. focuses on investment, commercial and security applications of advanced analytics, including text mining, forecasting, stock selection, image recognition, process optimization, cross-selling, biometrics, drug efficacy, credit scoring, market timing, and fraud detection.
John obtained a BS and MEE in Electrical Engineering from Rice University, and a PhD in Systems Engineering from the University of Virginia, where he's an adjunct professor teaching Optimization or Data Mining. Prior to 15 years at ERI, he spent 5 years in aerospace defense consulting, 4 heading research at an investment management firm, and 2 in Rice University's Computational & Applied Mathematics department. Dr. Elder has authored innovative data mining tools, is a frequent keynote speaker, and was co-chair of the 2009 Knowledge Discovery and Data Mining conference, in Paris.
John's courses on analysis techniques – taught at dozens of universities, companies, and government labs – are noted for their clarity and effectiveness. Dr. Elder was honored to serve for 5 years on a panel appointed by the President to guide technology for National Security. His book with Bob Nisbet and Gary Miner, Handbook of Statistical Analysis & Data Mining Applications, won the PROSE award for Mathematics in 2009. His book with Giovanni Seni, Ensemble Methods in Data Mining: Improving Accuracy through Combining Predictions, was published in February 2010. His (1,000-page) book on Practical Text Mining, with Dr. Andrew Fast and four others, was published by Elsevier in January. John is a follower of Christ and the proud father of 5.
Special Featured Session, Multiple Case Studies: The High ROI of Data Mining for Innovative Organizations
Sergo Grigalashvili, Vice President of Architecture, Analytics, Crawford & Company
Sergo Grigalashvili leads global efforts in enterprise architecture, analytics, and systems road mapping at Crawford & Company. His responsibilities include providing direction and leadership for development of business intelligence technology and statistical, data mining, and predictive analytic models for claim operations and client stewardship. Sergo has 16 years of technology industry experience; he has proven success in increasing maturity of enterprise technology architecture and in wide adoption of advanced analytics and business intelligence at organizations of various sizes; he earned a master's degree in management science and a bachelor's degree in applied mathematics and computer science.
Case Study: Predictive Analytics in Property Insurance Claims: Findings and Lessons Learned
Bob Humphreys, Country Leader for Demand Programs, IBM Canada
Bio is forthcoming.
Case Study: If We Host It; Will They Come? Predictive Modeling for Event Marketing
Piyanka Jain, CEO, Aryng.com, Former PayPal Business Analytics Head
Piyanka Jain's interest lies in deriving actionable insights from data to enable informed trade-offs and decision making. She enjoys problem solving and finds herself driven towards empowering business professionals to make better data driven business decision through Aryng's "Data to Decisions"™ framework she teaches. With Aryng, she is creating an organization to drive business transformation through the power of analytics.
Before founding Aryng, she was heading the NA Business Analytics at PayPal, leading strategic analytics, managing and setting agenda for the team, defining strategic roadmap to find NA business drivers. At PayPal, she and her team have delivered several high impact projects including product portfolio analysis, merchant lifecycle analysis, Voice of Customer analysis, Next Best Product Model for Merchant with $84+ mm revenue impact.
Prior to this, Piyanka drove direct measurable revenue impact of $18 mm through Strategic/Marketing analytics in partnership with Adobe Product Marketing and Relationship Marketing team. Within Marketing Operations and Analytics department, her role was to lead the organization into learning more about their products and customers through establishing appropriate engagement model with BU and rigorous mining of data. Within Relationship Marketing, Piyanka and her team's role involved designing and analyzing campaigns, creating and executing appropriate segmentation and targeting strategy, Fine tuning messaging, creatives and offers by Test & Control and improving targeting and increasing marketable universe by building response models and propensity models.
Keynote: The Five Myths of Predictive Analytics
Rumit Jain, Senior Business Analyst, Ontos Information Systems
Rumit is a data mining specialist with extensive experience in application of statistical analysis and machine learning algorithms to business process improvement in wide range of domains like insurance, financial, human resource, retail, telecom, and agriculture industries as well as government agencies. He has published several case studies on application of data mining in retail, telecom and finance as well as research papers in international research conferences. He holds a bachelor's degree in computer science.
Case Study: Predictive Analytics in Property Insurance Claims: Findings and Lessons Learned
Mike Kimel, Principal Consultant, Analytics Economics
Mike Kimel is Principal Consultant at Analytic Economics. In addition to eight years of experience as a consultant, Mike has worked at two Fortune 500 companies holding various management level positions. He also taught economics and advanced statistics to MBA students at Pepperdine University's Graziadio School of Business and Management in Malibu, CA.
Mike has a Ph.D. in Economics from UCLA.
Case Study: Revenge of the Clueless: Combining Many Poor Estimates into an Expert Forecast
Max Kuhn, Director of Nonclinical Statistics, Pfizer
Max Kuhn is a Director of Nonclinical Statistics at Pfizer Global R&D in Connecticut. He has been apply models in the pharmaceutical industries for over 15 years.
He is a leading R developer and the author of several R packages including the CARET package that provides a simple and consistent interface to over 100 predictive models available in R.
Mr. Kuhn has taught courses on modeling within Pfizer and externally, including a class for the India Ministry of Information Technology.
Case Study: Right Medicine, Right Patient
Workshop: R for Predictive Modeling: A Hands-On Introduction
Daymond Ling, Senior Director of Modelling & Analytics, CIBC
Daymond Ling is Senior Director of Modelling & Analytics at Canadian Imperial Bank of Commerce (CIBC) where he is responsible for customer analytics for the Retail Bank which has 8 million retail customers and half a million small business customers. Daymond oversees:
- Marketing Propensity Models to drive sales revenue;
- In-depth Customer Insight of customer acquisition, lifecycle, lifestyle, and triggers to uncover new sales opportunities;
- Customer Segmentation to drive customer management strategy; Optimization to maximize Revenue and ROI across business processes.
Daymond has 30 years of experience in Data Mining and Analytics. He holds a Bachelor of Science degree in Honours Physics, and a Master of Science degree in Operations Research, both from University of British Columbia in Canada.
Case Study: Creating Value Segmentation to Drive Business Strategies
Edward Nazarko, Client Technical Advisor, IBM
Ed Nazarko is an IBM Client Technical Advisor who works with healthcare payers on applying innovative technologies to solve customer problems, and industry problems. His focus is on combining technology innovation with customer-focused business and operations strategy. Recent projects have included performance engineering of large systems, application of combinatorial test design to optimization of ICD-10 test cases, creation of benefit rule abstraction and change validation tools, and traditional system design and build. As a consultant he has worked with pharmaceutical, device, healthcare delivery, and health insurers on a wide range of operational and strategic technology issues. He has also been in startups in life sciences, e-business and research. Ed has a BA from Reed College in Portland OR and an MBA from Boston University.
Keynote: Putting IBM Watson to Work
Leonard Roseman, Vice President of Statistics in Consumer Credit Risk Management and the Deputy Chief Scoring Officer, Capital One Financial
Leonard Roseman is a Vice President of Statistics in Consumer Credit Risk Management and the Deputy Chief Scoring Officer at Capital One Financial. He has been at Capital One, based in Richmond, Virginia since 2004.
Dr. Roseman has worked as a statistician, technical consultant, and strategy consultant in industry and business for over thirty years, teaching and applying experimental design, predictive modeling, and strategic analysis to high technology, bio-technology, medicine, marketing, and financial services. Before he came to Capital One, he worked at Bolt, Beranek, and Newman, W. L. Gore and Associates, the Mitchell Madison Group, Arthur Andersen, Seurat, and Catalina Marketing.
Leonard has an M.A. and Ph.D. in Statistics from Harvard University, and a B.A. in Physics from Swarthmore College.
Case Study: Competitive Training of Predictive Modelers
Eric Siegel, Program Chair, Predictive Analytics World
The president of Prediction Impact, Inc., Eric Siegel is an expert in predictive analytics and data mining and a former computer science professor at Columbia University, where he won the engineering school's award for teaching, including graduate-level courses in machine learning and intelligent systems - the academic terms for predictive analytics. After Columbia, Dr. Siegel co-founded two software companies for customer profiling and data mining, and then started Prediction Impact in 2003, providing predictive analytics services and training to mid-tier through Fortune 100 companies.
Dr. Siegel is the instructor of the acclaimed training program, Predictive Analytics for Business, Marketing and Web, and the online version, Predictive Analytics Applied. He has published over 20 papers and articles in data mining research and computer science education, has served on 10 conference programme committees, has chaired a AAAI Symposium held at MIT, and is the founding chair of Predictive Analytics World.
Keynote: Persuasion by the Numbers: Optimize Marketing Influence by Predicting It
