2016-2017 Distinguished Lecture Abstracts

Constrained Low Rank Approximations for Scalable Data Analytics

Haesun Park
Professor, Computational Science and Engineering
Georgia Institute of Technology

4:10pm Wednesday, January 25, 2017
Room 124 HRBB


Constrained low rank approximations have been widely utilized in large scale data analytics where the applications reach far beyond the classical areas of scientific computing, e.g. text analysis, social network analysis, computer vision, and bioinformatics.  

We present some fundamental properties, algorithms, and applications of Nonnegative Matrix factorization (NMF) and several of its variants to illustrate the importance of constrained low rank approximations for effective problem formulation and scalable algorithm design in Big Data analytics. We discuss some of the NMF-based algorithms for scalable clustering and topic modeling. Some substantial experimental results illustrate the utility of our recent methods and the open source software, SmallK, for hierarchical/non-hierarchical and data/network clustering for text, social network, and image analysis. Finally, we briefly introduce visual analytics systems that allow interactive analysis for more informed decisions in many applied domains.


Dr. Haesun Park is a professor in the School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia, U.S.A. She was elected as a SIAM Fellow 2013 and IEEE Fellow 2017 for her outstanding contributions in numerical computing, data analysis, and visual analytics.  She has published extensively in the areas of numerical computing, large-scale data analysis, visual analytics, text mining, and parallel computing. Before joining Georgia Tech, she was a professor in the Department of Computer Science and Engineering, University of Minnesota, Twin Cities 1987- 2005 and a program director in the Computing and Communication Foundations Division at the National Science Foundation, Arlington, VA, U.S.A., 2003 - 2005. She received a Ph.D. and an M.S. in Computer Science from Cornell University, Ithaca, NY in 1987 and 1985, respectively, and a B.S. in Mathematics from Seoul National University, Seoul, Korea in 1981 with the Presidential Medal for the top graduate.

Park was the Executive Director of the Center for Data Analytics 2013-2015 and was the director of the NSF/DHS FODAVA-Lead (Foundations of Data and Visual Analytics) Center 2008-2014. She was the conference co-chair for the SIAM International Conference on Data Mining in 2008 and 2009 and an editorial board member of leading journals in computational science and engineering such as SIAM Journal on Matrix Analysis and Applications, SIAM Journal on Scientific Computing, and IEEE Transactions on Pattern Analysis and Machine Intelligence. She was the plenary keynote speaker at numerous international conferences including SIAM Conference on Applied Linear Algebra in 1997 and 2015, and SIAM International Conference on Data Mining in 2011.

Faculty Contact: Dr. Nick Duffield

Big Data in Climate: Opportunities and Challenges for Machine Learning and Data Mining

Vipin Kumar
Regents Professor
University of Minnesota

4:10pm Monday, January 30, 2017
Room 124 HRBB


This talk will present an overview of research being done in a large interdisciplinary project on the development of novel data mining and machine learning approaches for analyzing massive amount of climate and ecosystem data now available from satellite and ground-based sensors, and physics-based climate model simulations. These information-rich data sets offer huge potential for monitoring, understanding, and predicting the behavior of the Earth’s ecosystem and for advancing the science of global change. This talk will discuss challenges in analyzing such data sets and some of our research results in mapping the dynamics of surface water globally as well as detecting deforestation and fires in tropical forests using data from Earth observing satellites.


Vipin Kumar is a Regents Professor and holds William Norris Chair in the department of Computer Science and Engineering  at the University of Minnesota.  His research interests include data mining, high-performance computing, and their applications in Climate/Ecosystems and health care. He is currently leading an NSF Expedition project on understanding climate change using data driven approaches.  He has authored over 300 research articles, and co-edited or coauthored 10 books including the widely used text book "Introduction to Parallel Computing", and "Introduction to Data Mining".  Kumar co-founded SIAM International Conference on Data Mining and served as a founding co-editor-in-chief of Journal of Statistical Analysis and Data Mining (an official journal of the American Statistical Association).  Kumar is a Fellow of the ACM, IEEE and AAAS.  He received the Distinguished Alumnus Award from the Indian Institute of Technology (IIT) Roorkee (2013) and the Distinguished Alumnus Award from the Computer Science Department, University of Maryland College Park (2009).  Kumar's foundational research in data mining and high performance computing has been honored by the ACM SIGKDD 2012 Innovation Award, which is the highest award for technical excellence in the field of Knowledge Discovery and Data Mining (KDD), and the 2016 IEEE Computer Society Sidney Fernbach Award, one of IEEE Computer Society's highest awards.

Faculty Contact: Dr. Nick Duffield

The Resurgence of Software Performance Engineering

Charles Leiserson
Professor, Computer Science and Engineering
Massachusetts Institute of Technology

4:10pm Wednesday, February 8, 2017
Room 124 HRBB


Today, most application developers write code without much regard for how quickly it will run. Moreover, once the code is written, it is rare for it to be reengineered to run faster. But two technology trends of historic proportions are instigating a resurgence in software performance engineering, the art of making code run fast. The first is the emergence of cloud computing, where the economics of renting computation, as opposed to buying it, heightens the utility of application speed. The second is the end of Moore's Law, the 50-year technology trend which has, until recently, relentlessly doubled the number of transistors on a semiconductor chip every two years. With the attenuation of this major source of computing performance, application programmers will increasingly find themselves turning to software performance engineering in order to develop innovative products and applications. 


Charles E. Leiserson received his B.S. from Yale University in 1975 and his Ph.D. from Carnegie Mellon University in 1981. He joined the faculty of the Massachusetts Institute of Technology in 1981, where he is now the Edwin Sibley Webster Professor in MIT’s Electrical Engineering and Computer Science Department and head of the Supertech research group in the MIT Computer Science and Artificial Intelligence Laboratory. He is a Margaret MacVicar Faculty Fellow at MIT, the highest recognition at MIT for undergraduate teaching.  He is a Fellow of four professional societies — AAAS, ACM, IEEE, and SIAM — and a member of the National Academy of Engineering. Awards include the ACM-IEEE Computer Society Ken Kennedy Award, the IEEE Computer Society Taylor L. Booth Education Award, ACM Paris Kanellakis Theory and Practice Award, and the ACM and Hertz Foundation Doctoral Dissertation Awards.

Faculty Contact: Dr. Lawrence Rauchwerger 

Physical Computing for Everyone

Tom Ball
Research Manager and Principal Researcher
Microsoft Research

4:10pm Monday, February 13, 2017
Room 124 HRBB


Thanks to Moore’s Law, embeddable microcontroller-based devices continue to get cheaper, faster, and include more integrated sensors and networking options. In 2016, the BBC and a host of technical partners, including Microsoft, delivered such a physical computing device, the micro:bit, to every 5th grader in the UK.  Microsoft Research helped to make the micro:bit easy to program for novices. The non-profit Micro:bit Education Foundation (microbit.org), of which Microsoft is a founding partner, was recently created to take the micro:bit global. Over the last year, Microsoft has invested in a new web-based programming platform for physical computing, called PXT, with the micro:bit being the first target (pxt.microbit.org).

In this talk, I’ll describe the design and implementation of PXT, focusing specifically on its web-based approach to physical computing. PXT supports rapid script development and testing within the confines of a modern web browser, via a novel combination of Blockly, TypeScript and hardware simulation. A browser-based compilation toolchain targets both the Thumb and AVR instruction sets and links against pre-compiled C++ code. PXT uses a bespoke C++ runtime from Lancaster University that provides a set of useful abstractions, including events, a message bus, and fibers.  


Thomas (Tom) Ball is a principal researcher and manager at Microsoft Research. In 1999, Tom initiated the SLAM software model-checking project with Sriram Rajamani. This led to the creation of the Static Driver Verifier tool for finding defects in Windows device drivers. Tom is a 2011 ACM Fellow for “contributions to software analysis and defect detection.” As a manager, he has nurtured research areas such as automated theorem proving, program testing/verification, and empirical software engineering. His current focus is CS education and the PXT platform for physical computing.

Faculty Contact: Dr. Jeff Huang

Fusion of Qualitative Knowledge and Big Data for Predictive Analytics in Dynamic Networks

Zoran Obradovic
Temple University

4:10pm Monday, April 17, 2017
Room 124 HRBB


An overview of our ongoing projects aimed to facilitate predictive analytics in partially observed evolving networks will be presented in this talk. Challenges and the proposed solutions will be discussed related to effective fusion of domain knowledge and data for joint learning of representation and structure in big multiscale networks. Additional challenges involve joint modeling of positive and negative influences in presence of multiple types of interactions and uncertainty propagation related to long-term forecasting in evolving networks. The proposed methods will be discussed in context of large-scale healthcare, climate and marketing applications.


Zoran Obradovic an Academician at the Academia Europaea (the Academy of Europe) and a Foreign Academician at the Serbian Academy of Sciences and Arts. He is a L.H. Carnell Professor of Data Analytics at Temple University, Professor in the Department of Computer and Information Sciences with a secondary appointment in Department of Statistical Science, and is the Director of the Center for Data Analytics and Biomedical Informatics. His research interests include data science and information networks analytics in complex decision support systems. Zoran has served as chair/co-chair for many data science conferences and is currently the program co-chair for 2017 IEEE Big Data Conference. He is the executive editor at the journal on Statistical Analysis and Data Mining, which is the official publication of the American Statistical Association and is an editorial board member at eleven journals. His work is published in more than 340 articles and is cited more than 18,000 times (H-index 51). His research is currently funded by 3 DARPA, 3 NSF, ONR and industry grants. His most recent DARPA THOR project started in June 2016 is aimed to understand why some host organisms are tolerant to pathogenic infection, and to uncover which biological mechanisms are responsible for their resilience. This $9.9M multi-institutional interdisciplinary effort includes investigators from Harvard Medical School, the Mayo Clinic, Temple University, Tufts University, and Boston Children's Hospital. For more details see http://www.dabi.temple.edu/~zoran/

Faculty Contact: Nick Duffield

Side Channels in Multi-Tenant Environments

The Texas A&M Cybersecurity Distinguished Lecture Series

Michael Reiter
Lawrence M. Slifkin Distinguished Professor
University of North Carolina at Chapel Hill

4:10pm Tuesday, December 6, 2016
Room 124 HRBB


Due to the massive adoption of computing platforms that consolidate potentially distrustful tenants' applications on common hardware---both large (e.g., public clouds) and small (e.g., smartphones)---the security provided by these platforms to their tenants is increasingly being scrutinized.  In this talk we will review highlights from the last several years of research on a long-suspected but, until recently, largely hypothetical attack vector on such platforms, namely side-channel attacks.  In these attacks, one tenant learns sensitive information about another tenant simply by running on the same hardware with it, but without violating the logical access control enforced by the platform's isolation software (virtual machine monitor or operating system). We will then summarize various strategies we have explored to defend against side-channel attacks in their various forms, both inexpensive defenses against specific attacks and more holistic but expensive protections.


Michael Reiter is the Lawrence M. Slifkin Distinguished Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill.  His research interests include all areas of computer and communications security and distributed computing.  His professional responsibilities during his career so far have included Director of Secure Systems Research at Bell Labs; founding Technical Director of CyLab at Carnegie Mellon University; program chair for the flagship computer security conferences of the IEEE, the ACM, and the Internet Society; and Editor-in-Chief of ACM Transactions on Information and System Security, among others.  Dr. Reiter was named an ACM Fellow in 2008 and an IEEE Fellow in 2014, and he received the ACM SIGSAC Outstanding Contributions Award in 2016.

Faculty Contact: Dr. Riccardo Bettati

Solar Robot UAVs and their Application to Agriculture and Environmental Monitoring Applications

Nikos Papanikolopoulos
Distinguished McKnight University Professor
University of Minnesota

4:10pm Wednesday, December 7, 2016
Room 124 HRBB


Aerial robotic platforms are an increasingly sought-after solution for a variety of sensing, monitoring, and transportation challenges. However, as invaluable as unmanned aerial vehicles (UAVs) have been for these applications, fixed-wing and multi-rotor systems each has individual limitations. Fixed-wing UAVs are generally capable of high altitude surveillance and long flight times, while quad-rotors are most effective when used for their maneuverability and close-quarters surveying. This talk discusses several transformer prototypes developed at the University of Minnesota with the objective of studying the aspects of solar powered fixed-wing flight, quad-rotor flight, and transformation modes of the platforms. Improvements to the transformation mechanism, airframe design, planning, sensing, variable pitch propulsion system, and custom-designed power electronics are presented along with validation of the designs through empirical testing. The talk  also highlights the innovations of the SUAV:Q transformer robot that is propelled through solar power. Applications from precision agriculture to environmental monitoring are discussed.


Nikolaos P. Papanikolopoul os (IEEE Fellow) received the Diploma degree in electrical and computer engineering from the National Technical University of Athens, Athens, Greece, in 1987, the M.S.E.E. in electrical engineering from Carnegie Mellon University (CMU), Pittsburgh, PA, in 1988, and the Ph.D. in electrical and computer engineering from Carnegie Mellon University, Pittsburgh, PA, in 1992. Currently, he is a McKnight Presidential Endowed Professor in CS and Distinguished McKnight University Professor in the Department of Computer Science at the University of Minnesota and Director of the Center for Distributed Robotics and SECTTRA. His research interests include robotics, computer vision, sensors for transportation applications,  and control. He has authored or coauthored more than 350 journal and conference papers in the above areas (eighty refereed journal papers).

Faculty Contact: Dr. Nancy M. Amato