CIS Seminars & Events
Spring 2017 Colloquium Series
Unless otherwise noted, our lectures are held weekly on Tuesday and/or Thursday from 3:00 p.m. to 4:15 p.m. in Wu and Chen Auditorium, Levine Hall.
Wednesday, January 18th
Senior Research Microsoft New York
Berger Auditorium, Skirkanich Hall
3:00 pm - 4:15 pm
"Machine Learning for Social Science"
In this talk, I will introduce the audience to the emerging area of computational social science, focusing on how machine learning for social science differs from machine learning in other contexts. I will present two related models -- both based on Bayesian Poisson tensor decomposition -- for uncovering latent structure from count data. The first is for uncovering topics in previously classified government documents, while the second is for uncovering multilateral relations from country-to-country interaction data. Finally, I will talk briefly about the broader ethical implications of analyzing social data.
Hanna Wallach is a Senior Researcher at Microsoft Research New York City and an Adjunct Associate Professor in the College of Information and Computer Sciences at the University of Massachusetts Amherst. She is also a member of UMass's Computational Social Science Institute. Hanna develops machine learning methods for analyzing the structure, content, and dynamics of social processes. Her work is inherently interdisciplinary: she collaborates with political scientists, sociologists, and journalists to understand how organizations work by analyzing publicly available interaction data, such as email networks, document collections, press releases, meeting transcripts, and news articles. To complement this agenda, she also studies issues of fairness, accountability, and transparency as they relate to machine learning. Hanna's research has had broad impact in machine learning, natural language processing, and computational social science. In 2010, her work on infinite belief networks won the best paper award at the Artificial Intelligence and Statistics conference; in 2014, she was named one of Glamour magazine's "35 Women Under 35 Who Are Changing the Tech Industry"; in 2015, she was elected to the International Machine Learning Society's Board of Trustees; in 2016, she was named co winner of the 2016 Borg Early Career Award; and in 2017, she will be program co-chair of the Neural Information Processing Systems conference. She is the recipient of several National Science Foundation grants, an Intelligence Advanced Research Projects Activity grant, and a grant from the Office of Juvenile Justice and Delinquency Prevention. Hanna is committed to increasing diversity and has worked for over a decade to address the underrepresentation of women in computing. She co-founded two projects---the first of their kind---to increase women's involvement in free and open source software development: Debian Women and the GNOME Women's Summer Outreach Program. She also co-founded the
annual Women in Machine Learning Workshop, which is now in its twelfth year.
Electrical Engineering and Computer Sciences
"Resurrecting Laplace's Demon: The Case for Deterministic Models
In this talk, I will argue that deterministic models have historically proved proved extremely valuable in engineering, despite fundamental limits. I examine the role that models play in engineering and contrast it with the role that they play in science, and I argue that determinism is an extraordinarily valuable property in engineering, even more than science. I will then show that deterministic models for cyber-physical systems, which combine computation with physical dynamics, remain elusive in practice. I will argue that the next big advance in engineering methods must include deterministic models for CPS, and I will show that such models are both possible and practical. I will then examine some fundamental limits of determinism, showing that chaos limits its utility for prediction, and that incompleteness means that at least for CPS, nondeterminism is inevitable.
Edward A. Lee is the Robert S. Pepper Distinguished Professor in the Electrical Engineering and Computer Sciences (EECS) department at U.C. Berkeley. His research interests center on design, modeling, and analysis of embedded, real-time computational systems. He is the director of the nine-university TerraSwarm Research Center (http://terraswarm.org), a director of Chess, the Berkeley Center for Hybrid and Embedded Software Systems, and the director of the Berkeley Ptolemy project. From 2005-2008, he served as chair of the EE Division and then chair of the EECS Department at UC Berkeley. He is co-author of six books and hundreds of papers. He has led the development of several influential open-source software packages, notably Ptolemy and its various spinoffs. He received his BS degree in 1979 from Yale University, with a double major in Computer Science and Engineering and Applied Science, an SM degree in EECS from MIT in 1981, and a PhD in EECS from UC Berkeley in 1986. From 1979 to 1982 he was a member of technical staff at Bell Labs in Holmdel, New Jersey, in the Advanced Data Communications Laboratory. He is a co-founder of BDTI, Inc., where he is currently a Senior Technical Advisor, and has consulted for a number of other companies. He is a Fellow of the IEEE, was an NSF Presidential Young Investigator, won the 1997 Frederick Emmons Terman Award for Engineering Education, and received the 2016 Outstanding Technical Achievement and Leadership Award from the IEEE Technical Committee on Real-Time Systems (TCRTS).
Department of Computer Science
Heterogeneous parallelism and specialization have become widely-used design levers for achieving high computer systems performance and power efficiency, from smartphones to datacenters. Unfortunately, heterogeneity greatly increases complexity at the hardware-software interface, and as a result, it brings increased challenges for software reliability, interoperability, and performance portability Over the past three years, my group has explored a set of issues for heterogeneously parallel systems, particularly related to specifying and verifying memory consistency models (MCMs), from high-level languages, down through compilers and operating systems and ISAs, and eventually to heterogeneous platforms comprised of CPUs, GPUs, and accelerators. The suite of tools we have developed (http://check.cs.princeton.edu ) offers comprehensive and fast analysis of memory ordering behavior across multiple system levels. As such, our tools have been used to find bugs in existing and proposed processors and in commercial compilers. They have also been used to identify shortcomings in the specifications of high-level languages (C++11) and instruction set architectures (RISC-V). Although memory models are traditionally considered nitpicky, boring, and even soul-crushing, my talk will show why they are of central importance for both hardware and software people today, and will also look forward to discuss future work related to MCM verification for accelerator-oriented parallelism and IoT devices.
Margaret Martonosi is the Hugh Trumbull Adams '35 Professor of Computer Science at Princeton University, where she has been on the faculty since 1994. Martonosi's research focuses on computer architecture and mobile computing, particularly power-efficient systems. Past projects include the Wattch power modeling tool and the ZebraNet mobile sensor network, which was deployed for wildlife tracking in Kenya. Martonosi is a Fellow of both IEEE and ACM. Her major awards include Princeton University's 2010 Graduate Mentoring Award, the Anita Borg Institute's 2013 Technical Leadership Award, NCWIT's 2013 Undergraduate Research Mentoring Award, and ISCA’s 2015 Long-Term Influential Paper Award.
Computer Science Department
University of California, Los Angeles
Variation in human DNA sequences account for a significant amount of genetic risk factors for common disease such as hypertension, diabetes, Alzheimer's disease, and cancer. Identifying the human sequence variation that makes up the genetic basis of common disease will have a tremendous impact on medicine in many ways. Recent efforts to identify these genetic factors through large scale association studies which compare information on variation between a set of healthy and diseased individuals have been remarkably successful. However, despite the success of these initial studies, many challenges and open questions remain on how to design and analyze the results of association studies. Many of these challenges involve analysis of recently developed
but revolutionary sequencing technologies. In this talk, I will discuss a few of the computational and statistical challenges in the design and analysis of genetic studie
Eleazar Eskin’s research focuses on developing computational methods for analysis of genetic variation. He is currently a Professor in the Computer Science and Human Genetics departments at the University of California Los Angeles. Previously, he was an Assistant Professor in Residence in Computer Science Engineering at the University of California, San Diego. Eleazar completed his Ph. D. in the Computer Science Department of Columbia University in New York City. After graduation, he spent one year in the Computer Science Department at the Hebrew University in Jerusalem, Israel.