2018

Tensor Decompositions for Big Multi-aspect Data Analytics

February 23

Abstract

Tensors and tensor decompositions have been very popular and effective tools for analyzing multi-aspect data in a wide variety of fields, ranging from Psychology to Chemometrics, and from Signal Processing to Data Mining and Machine Learning. Using tensors in the era of big data presents us with a rich variety of applications, but also poses great challenges such as the one of scalability and efficiency. In this talk I will first motivate the effectiveness of tensor decompositions as data analytic tools in a variety of exciting, real-world applications. Subsequently, I will discuss recent techniques on tackling the scalability and efficiency challenges by parallelizing and speeding up tensor decompositions, especially for very sparse datasets, including the scenario where the data are continuously updated over time. Finally, I will discuss open problems in unsupervised tensor mining and quality assessment of the results, and present work-in-progress addressing that problem with very encouraging results.

Biography

Evangelos (Vagelis) Papalexakis is an Assistant Professor of the CSE Dept. at University of California Riverside. He received his PhD degree at the School of Computer Science at Carnegie Mellon University (CMU). Prior to CMU, he obtained his Diploma and MSc in Electronic & Computer Engineering at the Technical University of Crete, in Greece.

Broadly, his research interests span the fields of Data Mining, Machine Learning, and Signal Processing. His research involves designing scalable algorithms for mining large multi-aspect datasets, with specific emphasis on tensor factorization models, and applying those algorithms to a variety of real world multi-aspect data problems. His work has appeared in KDD, ICDM, SDM, ECML-PKDD, WWW, PAKDD, ICDE, ICASSP, IEEE Transactions of Signal Processing, and ACM TKDD. He has a best student paper award at PAKDD’14 and SDM’16, finalist best papers for SDM'14 and ASONAM'13 and he was a finalist for the Microsoft PhD Fellowship and the Facebook PhD Fellowship. Besides his academic experience, he has industrial research experience working at Microsoft Research Silicon Valley during the summers of 2013 and 2014 and Google Research during the summer of 2015. Finally, his doctoral dissertation received the 2017 SIGKDD Doctoral Dissertation Award (runner up).


University Vision Based Navigation and Tracking with Small UAVs

January 26

Abstract

This talk will describe our current work on vision based autonomous navigation and tracking using small UAVs. We will overview two on-going projects. The first is relative navigation in GPS degraded environments. There are many applications where GPS is either restricted or denied. We have developed an architecture that uses a relative front end to navigate relative to key frames, and then opportunistically uses GPS measurements and SLAM-style loop closures in a back end process to provide global context. We will show some recent flight results that demonstrate robustness to GPS failure and degradation.

The second project that we will discuss is robust tracking of multiple ground based targets from an airborne platform. We will present a new multiple target tracking algorithm that is based on the random sample consensus (RANSAC) algorithm that is widely used in computer vision. A recursive version of the RANSAC algorithm will be discussed, and its extension to tracking multiple dynamic objects will be explained. The performance of R-RANSAC will be compared to state of the art target tracking algorithms in the context of problems that are relevant to UAV applications.

Biography

Randal W. Beard received the B.S. degree in electrical engineering from the University of Utah, Salt Lake City in 1991, the M.S. degree in electrical engineering in 1993, the M.S. degree in mathematics in 1994, and the Ph.D. degree in electrical engineering in 1995, all from Rensselaer Polytechnic Institute, Troy, NY. Since 1996, he has been with the Electrical and Computer Engineering Department at Brigham Young University, Provo, UT, where he is currently a professor. In 1997 and 1998, he was a Summer Faculty Fellow at the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA. In 2006 and 2007 he was a visiting research fellow at the Air Force Research Laboratory, Munitions Directorate, Eglin AFB, FL. His primary research focus is autonomous control of small air vehicles and multivehicle coordination and control. He is a past associate editor for the IEEE Transactions on Automatic Control, the IEEE Control Systems Magazine, and the Journal of Intelligent and Robotic Systems. He is a fellow of the IEEE, and an associate fellow of AIAA.