Abstract: High-dimensional discrete data are collected in many application areas, but have seen limited consideration in the literature. We focus in particular on two related problems: (i) high-dimensional categorical data that can be orga-nized as a many way contingency table; and (ii) sequential categorical data having a complex dependence structure. The first problem arises in numerous applications ranging from survey research in social sciences and epidemiology to genomics and marketing.
IP Primer and Considerations for Inventors, Entrepreneurs, and Startups
IP Considerations for Inventors, Entrepreneurs, and Startups
Speaker: Sergei Vassilvitskii (Google)
Retail Assortment Optimization
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.
Professor Paul Ginsparg, Cornell University
I will give a very brief sociological overview of the current metastable state of scholarly research communication, and then a technical discussion of the practical implications of literature and usage data considered as computable objects, using arXiv as exemplar.
Some of these algorithms scale to larger data sets.
A New Wireless Frontier --- In Vivo Communications and Networking
Leveraging Big Data Analytics for Compliance in Financial Institutions
It is critical for a financial institution to comply with government regulations. The cost of non-compliance can result in criminal indictment, multi-billion dollar fines and loss of banking and other licenses. Employees of Compliance departments are responsible for implementation of proper policies, procedures and monitoring to ensure compliance with regulations. This discussion will focus on how Compliance leverages large quantities of data to establish monitoring controls. In particular, we will discuss specific business problems and show how they map into problems in natural language processing, outlier detection, and graph analytics. No prior knowledge of finance will be assumed.
Big Data and Data Science from a European Industry Perspective
Carme Artigas, Co-Founder and Partner | Synergic Partners
Big data and data science are not only revolutionizing the way in which companies process and analyze information but are also transforming business models. But are they developing at the same speed and in the same way all over the world and in all sectors? What is the maturity level of big data and data science in Europe?
Many important applications can be formulated as large-scale optimization problems, including classification in machine learning, data assimilation in weather prediction, inverse problems, and medical and seismic imaging. While first-order methods have proven widely successful in recent years, recent developments suggest that matrix-free second-order methods, such as interior-point methods, can be competitive.