Statistics Research

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Our research covers a broad spectrum in theory, methodology and computing in statistics and applied probability, including a rich variety of collaborations with other research disciplines. As part of the Information and Software Systems Lab in AT&T Labs, we have strong connections with computer scientists, mathemeticians, database experts, and leading researchers in software and programming languages. Our research cuts across the entire spectrum of statistical application and theory: time series, spatial statistics, Bayesian analysis, machine learning, artificial intelligence, queueing theory, and applied probability. Our research projects are typically motivated by the real-world issues generated in our dynamic industry. Current examples of domains where our research is applied include:   Recommender Systems. Recommender systems are models which predict items that a customer may like, based on previous purchase or rating behavior. We have been doing research in these collaborative filtering models to predict movie and television preferences. This work resulted in our sharing the $1M Grand Prize in the Netflix Prize competition. Interactive Data Analysis and Statistical Computing with R Our rich history in statistical computing continues to this day with research into the statistical computing tools of the future. One specific focus is interactive data analysis tools, such as GGobi and iPlots. We are a Supporting Institution of the R Foundation and have a leadership role in the R community. Fraud Detection in Telecommunications. AT&T is the industry leader in protecting our networks from fraud. Fraud occurs in many forms: hackers breaking into lines, subscription fraud, spoofed cell phones, etc. Each of these requires large-scale data mining to determine the proper model fro detecting the fraud while minimizing false positives. Read more. . Social Networks. Our communications network defines a rich social network in both the consumer and business spaces. Our research focuses on extracting information through these networks by looking at information flow through the network or viral marketing opportunities. For an example, see our work on Proximity Graphs and our paper on network based marketing. Data Quality and Anomaly Detection in Data Streams. Monitoring vast data streams is a constabnt problem for large corporations. We model the incoming data as a multivariate data stream and build models to cluster and alert on those time series. Modelling data quality of the stream draws on expertise in Statstics, AI, Software engineering and Database Research to develop efficient analyses, algorithms and tools for data auditing, cleaning and repair. Fluid Flow Models Fluid flow models are used in a wide variety of areas: network performance; storage and inventory processes; financial and insurance risk models. Recently, they have been used as models in the area of forest fire containment. An active area of research in the department is the development of powerful algorithms for stochastic fluid flow models to determine their time dependent performance measures, and their applications to the performance of high speed networks and call centers. Computational Advertising As one of the largest advertisers on the web, as well as the owner of advertising channels like U-Verse and yellowpages.com, AT&emp;T is always looking for ways to optimize ad placement. Our efforts here include machine learning algorithms to estimate ad click through rates and spatial models to aid in local search. We wrote the book on it! Members of our department have written well-regarded books on their topics of interest. Take a look: Interactive Graphics for Data Analysis: Principles and Examples by Martin Theus and Simon Urbanek. Discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets. Interactive and Dynamic Graphics for Data Analysis: With R and GGobi by Dianne Cook and Deborah Swayne. Describes the use of interactive and dynamic graphics as part of multidimensional data analysis. Exploratory Data Mining and Data Cleaning by Tamraparni Dasu and Theodore Johnson. Serves as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining. An Introduction to Matrix Analytic Methods in Stochastic Modeling by V. Ramaswami and Guy Latouche. The authors present the basic mathematical ideas and algorithms of matrix analytic theory in a readable, up-to-date, and comprehensive manner. The New S Language: A Programming Environment for Data Analysis and Graphicsby Richard A. Becker, John M. Chambers, and Allan Wilks. Our history See our history page for information about a research tradition in statistics that goes back to Shewhart and Tukey.

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