Ensembles for Outlier Evaluation

by Dr. Charu Aggarwal, IBM T. J. Watson Research Center

Abstract This talk explores the use of ensembles for outlier evaluation. Outlier evaluation is inherently complex because of its unsupervised nature and the unpredictable effects of different parameters. Ensembles are not only effective for improving the performance of outlier detection algorithms but also for evaluating them. An evaluation of several well known algorithms is presented.

Bio Dr. Charu Aggarwal is a Distinguished Research Staff Member (DRSM) at the IBM T. J. Watson Research Center in Yorktown Heights, New York. He completed his Bachelor of Technology in Computer Science from the Indian Institute of Technology at Kanpur in 1993 and his PhD in Operations Research (focus: mathematical optimization) from the Massachusetts Institute of Technology in 1996. He has worked extensively in the field of data mining, with particular interests in data streams, privacy, uncertain data and social network analysis. He is a recipient of an IBM Corporate Award (2003) for his work on bio-terrorist threat detection in data streams, a recipient of the IBM Outstanding Innovation Award (2008) for his scientific contributions to privacy technology, and a recipient of two IBM Outstanding Technical Achievement Awards (2008) for his scientific contributions to high-dimensional and data stream analytics. He has received two best paper awards and an EDBT Test-of-Time Award (2014). He is a recipient of the IEEE ICDM Research Contributions Award (2015) and the ACM SIGKDD Innovation Award (2019), which are the two most prestigious awards for influential research in data mining. He is also a recipient of the W. Wallace McDowell Award , the highest award given by the IEEE Computer Society across the field of computer science. He has served as the general or program co-chair of the IEEE Big Data Conference (2014), the ICDM Conference (2015), the ACM CIKM Conference (2015), and the KDD Conference (2016). He is a fellow of the IEEE (2010), ACM (2013), and the SIAM (2015) for "contributions to knowledge discovery and data mining algorithms."

Detecting Anomalies in Complex Systems with Higher Order Networks

by Dr. Nitesh Chawla, University of Notre Dame

Abstract In this talk, I will present the higher order network representation of complex systems, and why it is necessary to accurately capture the higher and variable order dependencies among components of a complex system. In this talk, I will present our higher order network algorithm and also show that incorporating higher-order dependencies improves the performance of existing network-based methods for detecting anomalous signals in a complex system.

Bio Nitesh Chawla is the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame and the Founding Director of the Lucy Family Institute for Data and Society. He is an expert in artificial intelligence, data science, and network science, and is motivated by the question of how technological innovations can advance the common good through interdisciplinary research. He is the recipient of multiple awards for research and teaching innovation including outstanding teacher awards (2007 and 2010), a National Academy of Engineering New Faculty Fellowship, and a number of best paper awards and nominations. He is a Fellow of IEEE. He also is the recipient of the 2015 IEEE CIS Outstanding Early Career Award; the IBM Watson Faculty Award; the IBM Big Data and Analytics Faculty Award; ; and the 1st Source Bank Technology Commercialization Award. He is co-founder of Aunalytics, a data science software and cloud computing company.

Acting in Open Worlds in the Face of Disruptive Novelties and Anomalies

by Dr. Bryan Loyall, Charles River Analytics

Abstract AI Systems today are often brittle to anomalies and novelties that were not previously anticipated by their designers. This brittleness can be a source of both reduced performance and (at times, catastrophic) failure. This brittleness is particularly acute in systems that sense and act autonomously (or semi-autonomously) in open worlds. In this presentation, we outline a range of challenges to creating AI systems that can rapidly and effectively adapt their decision-making and action choices to such unanticipated changes, and we describe research toward a general framework for AI systems that can automatically recognize and adapt to these changes when they arise.

Bio Dr. Bryan Loyall is Director of Technology Innovation and Principal Scientist at Charles River Analytics, where his research interests center on artificial intelligence and machine learning with a focus on: the intersection of symbolic AI and data-driven methods; AI that can interact robustly with open, real-world systems; and integrated human and AI systems. Before joining Charles River, he ran a 60-person AI research group at BAE Systems, Inc, with research across a wide range of AI and computer systems, and was V.P. of Research at RA Capital, founding an AI research group for augmenting human analysis, as well as co-founding two AI-based startup companies. Dr. Loyall received his PhD degree in computer science from Carnegie Mellon.