Weekly interdisciplinary seminars from speakers in academia, government labs, and industry involved in the computing sciences.
Thursdays from 3:05-4:05 in Riverfront Hall room 222 during the fall 2017 semester.
August 24, 2017
August 31, 2017
Dr. Francesca Spezzano – Assistant Professor, Dept. of Computer Science, Boise State University
Ensuring the Integrity of Wikipedia: A Data Science Approach
Wikipedia is the world’s biggest free encyclopedia read by many users every day. Thanks to the mechanism by which anyone can edit, its content grows and is kept constantly updated. However, malicious users can take advantage of this open editing mechanism to seriously compromise the quality of Wikipedia articles. The main form of content damaging is vandalism, defined by Wikipedia itself as “any addition, removal, or change of content, in a deliberate attempt to compromise the integrity of Wikipedia”. Other forms of damaging edits are page spamming and dissemination of false information, e.g. through hoax articles. In this talk, we discuss two research efforts that have the common goal of ensuring the content integrity of Wikipedia and show that our approach significantly beat tools currently running on Wikipedia to detect damaging edits. First, we introduce DePP, the state-of-the-art tool detecting article pages to protect. Page protection is a mechanism used by Wikipedia to place restrictions on the type of users that can make edits to prevent vandalism, libel, or edit wars. Second, we present our work on malicious users identification such as vandals and spammers. Our approach looks at users’ edit behavior and not at edit content, then it can be used independently of the Wikipedia language version.
September 7, 2017
Dr. Robert Lund – Professor, Dept. of Mathematical Sciences, Clemson University
Bayesian Multiple Breakpoint Detection: Mixing Documented and Undocumented Changepoints
This talk presents methods to estimate the number of changepoint time(s) and their locations in time-ordered data sequences when prior information is known about some of the changepoint times. A Bayesian version of a penalized likelihood objective function is developed from minimum description length (MDL) information theory principles. Optimizing the objective function yields estimates of the changepoint number(s) and location time(s). Our MDL penalty depends on where the changepoint(s) lie, but not solely on the total number of changepoints (such as classical AIC and BIC penalties). Specifically, configurations with changepoints that occur relatively closely to one and other are penalized more heavily than sparsely arranged changepoints. The techniques allow for autocorrelation in the observations and mean shifts at each changepoint time. This scenario arises in climate time series where a “metadata” record exists documenting some, but not necessarily all, of station move times and instrumentation changes. Applications to climate time series are presented throughout.
Dr. Robert Lund is Professor of Mathematical Sciences at Clemson University and a Fellow of the American Statistical Association. He obtained his PhD in 1993 from the University of North Carolina and has published over 100 papers, co-authored one book, and supervised 20 PhD students to date. His research interests include Markov chains, statistical climatology, and time series. Presently, Dr. Lund is serving as a NSF Program Director for the Division of Mathematical Sciences.
September 14, 2017
Dr. Natasha Flyer – Scientist III, Institute for Mathematics Applied to the Geosciences, NCAR
Radial Basis Function-Generated Finite Differences (RBF-FD): New Opportunities for Applications in Scientific Computing
Radial Basis Function – generated Finite Differences (RBF-FD), a novel mesh-free method, has the ease of classical FD, yet combines high levels of accuracy with complete geometric flexibility, essential for both local refinement and to accurately handle irregular boundaries/surfaces. Furthermore, algorithmic complexity does not increase with dimension and the method inherently lends itself to short simple codes. It is also highly competitive compared to other state-of-the-art numerical methods. In recent benchmarking tests on the three dominant architectures composing high-performance computing systems today, Intel Multicore, Manycore and Nvidia GPUs, it has demonstrated excellent performance due to the very sparse compact structure of its differentiation matrices. In this presentation, we highlight some recent RBF-FD calculations.
Dr. Natasha Flyer received her Ph.D. from University of Michigan, Ann Arbor. She is a staff scientist at the National Center for Atmospheric Research in Boulder, Colorado. Her research interests focus on the development of computational methods for solar physics and geosciences.
September 21, 2017
J.R. Tietsort – Chief Information Security Officer at Micron
A look at interesting cyber events, why they happen, and how a large enterprise deals with these risks.
J.R. Tietsort is the Chief Information Security Officer for Micron Technology and has been with the company since 1995. In this role, he is responsible for Micron’s enterprise information security program for the protection of intellectual property. He is a Certified Information Security Manager, holds a Bachelor’s degree in Management of Information Systems, and a Master’s degree in Business Administration from Boise State University. Mr. Tietsort is an advisory member of the IT Leadership Roundtable of the Treasure Valley and the Idaho Governor’s Cybersecurity Task Force. He speaks at various events in and outside of Idaho, including the BSU Undergraduate Research Conference. His current focus is on advanced cyber threats, including the prevention of cyber espionage and intellectual property theft.
September 28, 2017
Dr. Scott Baden – UCSD and LBNL
How to Tolerate Communication Costs and How to Reduce Them
I will describe recent work in two complementary projects that each aim to diminish the impact of steadily increasing communication costs on scalable systems. The first project, Toucan, uses domain specific translation to restructure MPI applications to tolerate communication costs. The performance of Toucan’s translated code is competitive with that of manual restructuring. The second is a PGAS library, UPC++, that leverages the GASNet-EX communication library to deliver close to the metal communication performance.
October 5, 2017
Dr. Liljana Babinkostova- Boise State University
Lattice-based Cryptography: Short Integer Solution and Learning with Errors
Sustained advances in automating, interconnecting and miniaturizing technology and securely managing data assets requires a computing basis tuned to efficient use of limited energy and memory resources. Recent advances in quantum computing have triggered widespread interest and an urgent need for a new range of mathematical problems for post-quantum security. These not only present challenging and exciting opportunities for researchers from a wide range of fields, but offer an exceptional opportunity for the future generation of scientists to participate in groundbreaking work and to prepare for new scientific challenges.This talk features current developments and future directions of two problems used in modern lattice-based cryptographic schemes: Short Integer Solution (SIS) and Learning With Errors (LWE).
October 12, 2017
DaRon Huffaker – Data Science & Statistics Manager at Micron
Big Data and Data Science at Micron Technology, Inc
J. DaRon Huffaker manages Micron’s Global Quality Data Science and Statistics Team. He has been a Micron team member for 20+ years. Over the past 3 years, he has formed two data science teams focusing on big data analytics and predictive modeling/machine learning. Prior to that, he has worked on various projects in different departments and positions at Micron: created a production risk based sampling methodology as a Principal Statistician in Operations Central Teams; led global alignment of product critical parameters as an Assembly QA Lead/Engineer; implemented metrology, IQC, and SPC and performed failure analyses as a Transform Solar Senior Quality Engineer; assisted in creating and applying a statistical forecasting process for product demand as a Forecast Manager/Statistician in Supply Chain; formed and managed a QA Statistics Group focused on teaching engineering short courses in applied statistics, setting and monitoring DRAM product burn-in criteria, and providing statistical consulting on numerous projects related to product and process improvements. Mr. Huffaker has a B.Sc. degree in Statistics/Quality Science with an emphasis in Manufacturing Engineering Technology from Brigham Young University and a M.Sc. degree in Applied Statistics from Rochester Institute of Technology. He is a senior member of the American Society for Quality (ASQ) and holds six professional certifications in that organization.
October 19, 2017
Dr. Aaron Halfaker – Senior Research Scientist at Wikimedia Foundation
Engineering at the Intersection of Productive Efficiency, Ideology, and Ethical AI in Wikipedia
Wikipedia has become a dominant source of reference information for more than half a billion people every month. Through its improbable rise to popularity, this “free encyclopedia that anyone can edit” has become a synecdoche for open production communities online. In order to operate at massive scales (~160k edits per day), Wikipedians have embraced algorithmic technologies that bring efficiency and consistency to the wiki’s complex, distributed processes. These algorithms mediate social processes, governance decisions, and editors’ perceptions of each other. Specifically, so-called “black box” artificial intelligences have proven invaluable for supporting curation activities at scale, but they also have the potential to silence voices and perpetuate biases in insidious ways. Despite Wikipedians’ open/audit-able processes, that’s exactly what’s been happening. In this talk, I’ll introduce “ORES,” an open AI platform that is designed to enable Wikipedia’s technologists to enact alternative ideological visions and to enable researchers to easily perform audits. I’ll share some lessons that we’ve learned maintaining a large-scale, generalized AI service and discuss a call to action direct towards critical algorithms researchers to take advantage of this platform for their studies.
October 26, 2017
Dr. Timo Bauman – Systems Scientist, Language and Technologies Institute, School of Computer Science, Carnegie Mellon University
November 2, 2017
Dr. Wolfgang Bangerth – Professor, Department of Mathematics, Colorado State University.
November 9, 2017
Dr. Alessandra Scafuro – Assistant Professor, Department of Computer Science, North Carolina State University
November 16, 2017
Dr. Ryan Henry, Assistant Professor of Computer Science in the School of Informatics and Computing at Indiana University
November 23, 2017
November 30, 2017
Dr. Bart Knijnenburg – Assistant Professor in Human-Centered Computing, Clemson University
December 8, 2017
Dr. Karin Leiderman – Assistant Professor, Department of Mathematics, Colorado School of Mines