If you have been following the intense debate on statistics vs. data science and wondering about the increasing use of phrases like machine learning and artificial intelligence in the business world, you will definitely want to take advantage of a free webinar on September 27 between 12 and 2 pm EDT.
The 2-hour webinar is sponsored by the National Institute of Statistical Sciences and will feature 4 prominent leaders in statistics and data analytics. The 4 speakers, Vincent Granville, Victor Lo, Hal Stern, and Lee Wilkinson are pioneers in their distinct areas of expertise. The webinar will be moderated by Dan Jeske. A short bio of each participant is given at the end of the program.
Registration for this event is required. Register Today!
Following is the lineup of speakers for the webinar (all times are ET).
12:00 Dan Jeske, (University of California, Riverside)
Introductions and moderator guidelines
12:05 Victor Lo, (Fidelity Investment)
"History of evolving terms"
12:25 Hal Stern, (University of California, Irvine)
“The role of statistics in modern data analysis”
12:45 Lee Wilkinson, (H2O)
"Visualization for data science"
13:05 Vincent Granville, (Data Science Central)
“Applications of data analytics“
13:25 Q & A with the audience and speakers
Moderated by Dan Jeske
Short Biographical Summaries of the Speakers
(in the order of appearance on the program):
Daniel Jeske is a Professor in the department of statistics at the University of California, Riverside (UCR), where he served as the department chair 2008-2015. He is also the Vice-Provost of Administrative Resolution at UCR. He is a fellow of the American Statistical Association (ASA) and served on the ASA board 2014-2016. He is the current Vice-President of Membership and President-Elect for the International Society of Business and Industrial Statistics, one of the Associations of the International Statistical Institute (ISI). He is the current Editor-in-Chief of The American Statistician. His research interests include classification and prediction methodologies, statistical process control methodologies, biostatistics applications, and reliability modeling.
Victor Lo has managed analytics teams in multiple organizations. Currently, he leads the Center of Excellence for AI and Data Science in Workplace Investments at Fidelity Investments. Previously he managed advanced analytics teams in Personal Investing, Corporate Treasury, Managerial Finance, and Healthcare and Total Well-being at Fidelity Investments. Prior to Fidelity, he was VP and Manager of Modeling and Analysis at FleetBoston Financial (now Bank of America). Victor has 25 years of extensive consulting and corporate experience employing data-driven solutions in a wide variety of business areas including marketing and finance. He is a pioneer of Uplift/True-lift modeling, a key subfield of data science.
Hal Stern is Chancellor’s Professor of Statistics at the University of California, Irvine (UCI). He served as founding chair of the department of statistics for 8 years and then served 6.5 years as dean of the UCI School of Information and Computer Sciences. Hal is known for his research in Bayesian statistics and for collaborative projects in the life sciences and social sciences. He has published more than 100 refereed journal articles and is a co-author of the book Bayesian Data Analysis. Hal is a Fellow of the American Association for the Advancement of Science (AAAS), ASA and the Institute for Mathematical Statistics.
Leland Wilkinson is Chief Scientist at H2O and Adjunct Professor of Computer Science at the University of Illinois Chicago. He founded SYSTAT Inc. in 1984 and wrote the SYSTAT statistical package. Lee is a Fellow of ASA, an elected member of ISI, and a Fellow of AAAS. He has won best speaker award at the National Computer Graphics Association and the Youden prize for best expository paper in Technometrics. Lee owns several patents on visualization and distributed analytic computing. Lee is the author of The Grammar of Graphics, the foundation for several commercial and opensource visualization systems (e.g., IBMRAVE, Tableau, Rggplot2, and PythonBokeh).
Vincent Granville is a former post-doctorate of Cambridge University and NISS. He is a data science pioneer with proven success in bringing value to companies ranging from startups to fortune 100 across multiple industries. Vincent developed and deployed a set of statistical / machine learning techniques such as hidden decision trees, automated tagging, indexing and clustering of large document repositories, Jackknife Regression, model-free confidence intervals, and combinatorial feature selection algorithms. Vincent also invented many synthetic metrics, some of which have been implemented in a Map-Reduce Hadoop-like environment. He created the first IoT platform to automate growth and content generation for digital publishers.