Distinguished Colloquium Series Speaker

*This talk is open only to Princeton University faculty, staff, and students. 
Please register for this webinar here.

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In this lecture, I show how considering the intersection of collaborative and social scenarios with other domains of computing can reveal end-user needs and result in innovative technical systems. I give examples of this approach from my work in gesture interaction, information retrieval, and accessibility, focusing particularly on the topics of creating more efficient and expressive augmentative and alternative communication technologies and of making social media more accessible to screen reader users. I close by identifying future opportunities for creating inclusive collaborative and social technologies.

Bio:
Meredith Ringel Morris is a Sr. Principal Researcher at Microsoft Research and Research Area Manager for Interaction, Accessibility, and Mixed Reality. She founded Microsoft Research’s Ability research group and is a member of the lab’s Leadership Team. She is also an Affiliate Professor at the University of Washington in the Allen School of Computer Science & Engineering and in The Information School. Dr. Morris is an expert in Human-Computer Interaction; in 2020, she was inducted into the ACM SIGCHI Academy in recognition of her research in collaborative and social computing. Her research on collaboration and social technologies has contributed new systems, methods, and insights to diverse areas of computing including gesture interaction, information retrieval, and accessibility. Dr. Morris earned her Sc.B. in Computer Science from Brown University and her M.S. and Ph.D. in Computer Science from Stanford University.

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To request accommodations for a disability please contact Emily Lawrence, emilyl@cs.princeton.edu at least one week prior to the event.

The internal design of software—how the code is structured—is powered by familiar abstractions (such as abstract types, classes and modules). But the external design—how the software actually behaves—is usually viewed informally, without the guidance of robust abstractions.

For the last few years, I have been exploring a new abstraction that can shape how we think about software applications and systems, and that provides a way to organize behaviors, encapsulate reusable ideas, and evaluate designs.

In this talk, I’ll define this abstraction and show how it can be used to explain a variety of flaws in familiar applications. I’ll also present some general principles that attempt to capture some key aspects of good software design.

Bio: Daniel Jackson is a professor of computer science and MacVicar teaching fellow at MIT, and associate director of the Computer Science and Artificial Intelligence Laboratory. His research has focused primarily on software modeling and design, most notably the Alloy modeling language. He is also a photographer whose most recent project brings to light the experiences of those with depression and other mental health issues (http://portraitsofresilience.com).

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Just as you wouldn't want to be in a car without seat belts, even if it's a self-driving car, you probably don't want your Self-Driving Network (SelfDN) to rely completely on control-theory or ML, no matter how sophisticated or formally verified.   While autonomous systems promise to solve many problems that afflict networks, just as they promise to solve many problems that afflict our roadways, they are not immune from a multitude of practical problems and unexpected consequences. At Google, we have been moving towards highly-automated networking, and along the way we have learned a lot about the risks involved, and how to build robust networks in spite of the wonders of automation.  I will talk about some of the problems that are likely to afflict SelfDNs as they enter the real world.

Bio:
Jeff Mogul works on fast, cheap, reliable, and flexible networking infrastructure for Google. Until 2013, he was a Fellow at HP Labs, doing research primarily on computer networks and operating systems issues for enterprise and cloud computer systems; previously, he worked at the DEC/Compaq Western Research Lab. He received his PhD from Stanford in 1986, an MS from Stanford in 1980, and an SB from MIT in 1979. He is an ACM Fellow. Jeff is the author or co-author of several Internet Standards; he contributed extensively to the HTTP/1.1 specification. He was an associate editor of Internetworking: Research and Experience, and has been the chair or co-chair of a variety of conferences and workshops, including SIGCOMM, OSDI, NSDI, USENIX, HotOS, and ANCS.

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Machine learning algorithms are ubiquitous in most scientific, industrial and personal domains, with many successful applications. As a scientific field, machine learning has always been characterized by the constant exchanges between theory and practice, with a stream of algorithms that exhibit both good empirical performance on real-world problems and some form of theoretical guarantees. Many of the recent and well publicized applications come from deep learning, where these exchanges are harder to make, in part because the objective functions used to train neural networks are not convex. In this talk, I will present recent results on the global convergence of gradient descent for some specific non-convex optimization problems, illustrating these difficulties and the associated pitfalls (joint work with Lénaïc Chizat and Edouard Oyallon).

Bio:
Francis Bach is a researcher at INRIA, leading since 2011 the SIERRA project-team, which is part of the Computer Science Department at Ecole Normale Supérieure, and a joint team between CNRS, ENS and INRIA. Since 2016, he is an adjunct Professor at Ecole Normale Supérieure. He completed his Ph.D. in Computer Science at U.C. Berkeley, working with Professor Michael Jordan, and spent two years in the Mathematical Morphology group at Ecole des Mines de Paris, he then joined the WILLOW project-team at INRIA/Ecole Normale Superieure/CNRS from 2007 to 2010. He obtained in 2009 a Starting Grant and in 2016 a Consolidator Grant from the European Research Council, and received the Inria young researcher prize in 2012, the ICML test-of-time award in 2014, as well as the Lagrange prize in continuous optimization in 2018. In 2015, he was program co-chair of the International Conference in Machine learning (ICML), and general chair in 2018; he is now co-editor-inchief of the Journal of Machine Learning Research. Francis Bach is primarily interested in machine learning, and especially in graphical models, sparse methods, kernel-based learning, large-scale convex optimization, computer vision and signal processing.

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Determining whether online users are authorized to access digital objects is central to preserving privacy. This talk presents the design, implementation, and deployment of Zanzibar, a global system for storing and evaluating access control lists. Zanzibar provides a uniform data model and configuration language for expressing a wide range of access control policies from hundreds of client services at Google, including Calendar, Cloud, Drive, Maps, Photos, and YouTube. Its authorization decisions respect causal ordering of user actions and thus provide external consistency amid changes to access control lists and object contents. Zanzibar scales to trillions of access control lists and millions of authorization requests per second to support services used by billions of people. It has maintained 95th-percentile latency of less than 10 milliseconds and availability of greater than 99.999% over 3 years of production use.
 

Bio: Ramón Cáceres is a software engineer at Google. He was previously a researcher at AT&T Labs and IBM Research. His areas of focus include computer systems and networks, mobile computing services and applications, and location data analysis and modeling. He holds a PhD in Computer Science from the University of California at Berkeley. He is an IEEE Fellow and an ACM Distinguished Scientist.

To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

The network edge—where the access networks that connect homes, businesses, and mobile users to the Internet are implemented—is in the midst of a transformation. Network operators (Telcos and CableCos) are transitioning from closed and proprietary hardware to disaggregated and virtualized software running on white-box servers, switches, and access devices. This transformation is partly motivated by the CAPEX savings that come from replacing purpose-built appliances with commodity hardware, but it is mostly driven by the need to increase feature velocity through the softwarization of the access network. The goal is to enable new classes of edge services—e.g., Autonomous Vehicles, Internet-of-Things (IoT), Immersive User Interfaces—that benefit from low latency connectivity. This is all part of the growing trend to move functionality to the network edge.

This transformation faces two technical challenges. The first is to disaggregate and virtualize existing hardware appliances. This is done, for example, by applying SDN principles (decoupling the network control and data planes) and by breaking monolithic applications into a set of micro-services. The second is to integrate the resulting disaggregated components back into a functioning system, packaged with sufficient configuration and life cycle management tools to make it deployable in a production environment. This talk describes CORD, a community-based open source effort to address these challenges. CORD is being deployed in Telco field trials, but more interestingly, it lowers the barrier for anyone (not just global carriers) to deploy edge solutions that incorporate the latest 5G (wireless) and XGS-PON (fiber) access technologies. The talk concludes with some remarks on the opportunity this democratization of the network edge opens for the research community.

Bio:
Larry Peterson is the Robert E. Kahn Professor of Computer Science, Emeritus at Princeton University, where he served as Chair from 2003-2009. He is a co-author of the best selling networking textbook Computer Networks: A Systems Approach (5e). His research focuses on the design, implementation, and deployment of Internet-scale distributed systems, including the widely used PlanetLab and MeasurementLab platforms. He is currently working on a new access edge cloud called CORD, an open source project of the Open Networking Foundation

Professor Peterson is a former Editor-in-Chief of the ACM Transactions on Computer Systems, and served as program chair for SOSP, NSDI, and HotNets. He is a member of the National Academy of Engineering, a Fellow of the ACM and the IEEE, the 2010 recipient of the IEEE Kobayashi Computer and Communication Award, and the 2013 recipient of the ACM SIGCOMM Award. He received his Ph.D. degree from Purdue University in 1985.

In May 2013, Georgia Tech together with its partners, Udacity and AT&T, announced a new online master’s degree in computer science delivered through the platform popularized by massively open online courses (MOOCs). This new online MS CS— or OMSCS for short — costs less than $7,000 total, compared with a price tag of $40,000 for an MS CS at comparable public universities and upwards of $70,000 at private universities.

The first-of-its-kind program was launched in January 2014 and has sparked a worldwide conversation about higher education in the 21st century. President Barack Obama praised OMS CS by name twice, and more than 1,200 news stories mentioned the program. It has been described as a potential "game changer" and "ground zero of the revolution in higher education”.  Harvard University researchers concluded that OMSCS is “the first rigorous evidence showing an online degree program can increase educational attainment” and predicted that OMSCS will single handedly raise the number of annual MS CS graduates in the United States by at least 7 percent.

OMSCS started in 2014 with an enrollment of 380; in this semester (spring 2019) enrollment is close to 9,000; OMSCS is apparently the biggest MS in CS program in the world. So far more than 2,000  students have graduated from OMSCS; more than 1,000 will graduate each year from now on. The program has also paved the way for at least 22 similar, MOOC-based online MS programs. According to the Bureau of Labor Statistics and the National Science Foundation, there is a shortage of 500,000 computer professionals in the US, a shortage that will reach one million by 2020. In other words, OMSCS is satisfying a great national need.
 
The talk will describe the OMSCS program, how it came about, its first five years, and what Georgia Tech has learned from the OMSCS experience. We will also discuss its potential effect on higher education.

Bio:
Dr. Zvi Galil, Dean of the College of Computing, Georgia Institute of Technology, was born in Tel Aviv, Israel. He earned BS and MS degrees in Applied Mathematics from Tel Aviv University, both summa cum laude. He then obtained a PhD in Computer Science from Cornell University. After a post-doctorate in IBM's Thomas J. Watson research center, he returned to Israel and joined the faculty of Tel-Aviv University. He served as the chair of the Computer Science department in 1979-1982.  In 1982 he joined the faculty of Columbia University. He served as the chair of the Computer Science Department in 1989-1994 and as dean of The Fu Foundation School of Engineering & Applied Science in 1995-2007. Galil was appointed Julian Clarence Levi Professor of Mathematical Methods and Computer Science in 1987, and Morris and Alma A. Schapiro Dean of Engineering in 1995. In 2007 Galil returned to Tel Aviv University and served as president. In 2009 he resigned as president and returned to the  faculty as a professor of Computer Science. In July 2010 he became The John P. Imlay, Jr. Dean of Computing at Georgia Tech.   

Dr. Galil's research areas have been the design and analysis of algorithms, complexity, cryptography and experimental design. In 1983-1987 he served as chairman of ACM SIGACT, the Special Interest Group of Algorithms and Computation Theory. He has written over 200 scientific papers, edited 5 books, and has given about 250 lectures in 25 countries (over 60 in 15 countries on OMSCS). Galil has served as editor in chief of two journals and as the chief computer science adviser in the United States to the Oxford University Press. He is a fellow of the ACM and the American Academy of Arts and Sciences and a member of the National Academy of Engineering. In 2008 Columbia University established the Zvi Galil Award for Improvement in Engineering Student Life. In 2009 the Columbia Society of Graduates awarded him the Great Teacher Award.  In 2012 the University of Waterloo awarded him an honorary doctorate in mathematics. 

Zvi Galil is married to Dr. Bella S. Galil, a marine biologist. They have one son, Yair, a corporate lawyer in New York City.

Lunch for talk attendees will be available at 12:00pm. 
To request accommodations for a disability, please contact Emily Lawrence, emilyl@cs.princeton.edu, 609-258-4624 at least one week prior to the event.

Despite rapidly increasing enrollment in CS courses, the academic CS community is failing to keep pace with demand for trained CS students, leading to escalating starting salaries for our students. Further, the knowledge of how to teach students up to the state of the art is increasingly segregated into a small cohort of schools who mostly cater to students from families in the top 10% of the income distribution.

Even in the best case, those schools lack the aggregate capacity to teach more than a small fraction of the nation's need for engineers and computer scientists.  MOOCs can help, but they are mainly effective at retraining existing college graduates. In practice, most low and middle income students need a human teacher. In this talk I argue for building an open source CS curriculum, with autograded projects, instructional software, textbooks, and slideware, as an aid for teachers who want to improve the education in advanced CS topics at schools attended by the children of the 90%. I will give as an example our work on replicating teaching advanced operating systems and distributed systems.

Bio:
Tom Anderson is the Warren Francis and Wilma Kolm Bradley Chair in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His research interests span all aspects of building practical, robust, and efficient computer systems, including distributed systems, operating systems, computer networks, multiprocessors, and security. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, as well as winner of the USENIX Lifetime Achievement Award, the USENIX STUG Award, the IEEE Koji Kobayashi Computer and Communications Award, the ACM SIGOPS Mark Weiser Award, and the IEEE Communications Society William R. Bennett Prize. He is also an ACM Fellow, past program chair of SIGCOMM and SOSP, and he has co-authored twenty-one award papers and one widely used undergraduate textbook.

The ongoing shift of enterprise computing to the cloud provides an opportunity to rethink operating systems for this new setting.  I will discuss two specific technologies, kernel bypass for high performance networking and low latency non-volatile storage, and their implications for operating system design. In each case, delivering the performance of the underlying hardware requires novel approaches to the division of labor between hardware, the operating system kernel, and the application library. 

Bio:
Tom Anderson is the Warren Francis and Wilma Kolm Bradley Chair in the Paul G. Allen School of Computer Science and Engineering at the University of Washington. His research interests span all aspects of building practical, robust, and efficient computer systems, including distributed systems, operating systems, computer networks, multiprocessors, and security. He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences, as well as winner of the USENIX Lifetime Achievement Award, the USENIX STUG Award, the IEEE Koji Kobayashi Computer and Communications Award, the ACM SIGOPS Mark Weiser Award, and the IEEE Communications Society William R. Bennett Prize. He is also an ACM Fellow, past program chair of SIGCOMM and SOSP, and he has co-authored twenty-one award papers and one widely used undergraduate textbook.

Lunch will be available to talk attendees at 12:00pm.

When software engineers re-implement a high-performance research prototype code, one often observes one to two orders of magnitude drop in performance. This holds even if both implementations use the same language. The gap is even wider when one goes from a simple language (e.g., C++) to a more sophisticated one (e.g., Java).

One of the root causes of this phenomenon is the misinterpretation by software engineers of what they learn in school. Theoretical computer scientists ignore constant factors for the sake of machine-independent analysis. Programming language researchers focus on compilers that automatically handle low-level OS and architectural issues such as memory management. Software engineering professors emphasize abstraction and re-usability. Many software engineers learn to ignore constant factors, rely on compilers for the low-level efficiency, and use generic primitives for re-usability. This is tempting to do as one has to worry about fewer issues when coding, and one needs to know fewer primitives and data structures.

However, in practice constant factors do matter, compilers do not always take advantage of computer architecture features, and generic primitives may be less efficient than the simple ones sufficient for the task. Ignoring these issues can lead to significant loss of computational efficiency and increased memory consumption. Power consumption also increases significantly.

In this talk we give several examples of inefficient program fragments and discuss them. These examples show that software engineers need to pay attention to low-level details when choosing data structures and programming primitives, and avoid some inefficient coding practices.

Bio:
Andrew Goldberg is a Senior Principal Scientist at Amazon.com, Inc. His research interests are in design, analysis, and computational evaluation of algorithms and data structures, algorithm engineering, computational game theory, electronic commerce, and parallel and distributed algorithms, and complexity theory. His algorithms are widely used in industry and academia. Goldberg got his Ph.D. degree in Computer Science from MIT in 1987, where he was a Hertz Foundation Fellow. Before joining Amazon in 2014, he worked at GTE Laboratories, Stanford University, NEC Research Institute, InterTrust Technologies, Inc., and Microsoft Research. Goldberg received a number of awards for his research contributions, including the NSF Presidential Young Investigator Award and the ONR Young Investigator Award. He is a Fellow of ACM and SIAM.

Lunch available to talk attendees at 12:00pm