Anna Choromanska, Courant Institute of Mathematical Sciences, New York University
Title: Optimization for large-scale machine learning: large data and large model
Chief Scientist | Booz Allen Hamilton
The analytics community has invested significant resources in developing effective predictive analytical methods. However, even the most accurate predictive forecasts have limited value unless they can also provide clear action steps to bring about desired results. In other words, the cause of the data is more important than the data itself. Alex will introduce causal inference in the context of Bayesian Belief Networks (BBNs). BBNs produce accurate predictive forecasts, but with appropriate modeler input are also able to identify causal relationships between variables and pinpoint drivers of desired targets. With causal relationships identified, BBNs may be used in a prescriptive fashion in order to make actionable decisions. Alex will dive into a case study in the aviation space which identifies causal drivers of daily flight operations on flight delays and allows us to prescribe delay-reduction plans by acting on controllable drivers.
Abstract: High-dimensional discrete data are collected in many application areas, but have seen limited consideration in the literature.
Dr. David Heckerman
Distinguished Scientist, Microsoft Research
One obvious way to get more power out of association studies is to analyze more data. Large studies, however, are often confounded by relatedness among individuals, leading to the need for sophisticated statistical methods such as the linear mixed model. Unfortunately, these models have traditionally required an inordinate amount of computation.
Dr. Heckerman will show how these methods can be made very fast, facilitating their use on large data and will also describe some additional tricks to get more power out of association studies.
IP Primer and Considerations for Inventors, Entrepreneurs, and Startups
IP Considerations for Inventors, Entrepreneurs, and Startups
Director of Basketball Analytics, NBA
Michael Lewis's 2003 bestseller Moneyball illustrated the power of effectively utilizing analytics in baseball. His book ignited a sports analytics revolution by showing how data-driven decision making processes could be as valuable as a multimillion dollar advantage in player spending.
In the past several years, an online basketball analytics community (APBR) has flourished, developing tools to evaluate teams, players and strategies. Basketball teams have been hiring analysts at an ever-increasing rate, and even the style of NBA play is changing before our eyes.
Furthermore, starting in 2013, every NBA arena became equipped with the SportVU software. Utilizing six cameras installed in the catwalks of every NBA arena, SportVU tracks the movement of every player on the court and the basketball 25 times per second.
DARPA GRAPHS/SIMPLEX Workshop:
Data, Algorithms and Problems on Graphs
The goals of the Engineering for Natural Hazards (ENH) program are to prevent natural hazards from becoming disasters, and to broaden consideration of natural hazards independently to the consideration of the multi-hazard environment within which
This workshop will highlight the benefits of integrating data science methodologies with those in the natural sciences and feature invited speakers in the field.
Join us for our interactive online information session which provides an overview of the Certification of Professional Achievement and the Master of Science in Data Science programs, including admission criteria, financial aid and student life!