February 2019 - talks - lists.cs.princeton.edu

Reminder: Colloquium Speaker Elissa Redmiles tomorrow at 12:30pm
by Emily C. Lawrence 20 Feb '19

20 Feb '19

Colloquium Speaker Elissa Redmiles from University of Maryland Thursday, February 21 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan [ http://www.cs.princeton.edu/events/25817 | http://www.cs.princeton.edu/events/25817 ] Security for All: Modeling Structural Inequities to Design More Secure Systems Users often fall for phishing emails, reuse simple passwords, and fail to effectively utilize "provably" secure systems. These behaviors expose users to significant harm and frustrate industry practitioners and security researchers alike. As consequences of security breaches become ever more grave, it is important to study why humans behave seemingly irrationally. In this talk, I will illustrate how modeling the effects of structural inequities -- variance in skill, socioeconomic status, as well as culture and gender identity -- can both explain apparent irrationality in users’ security behavior and offer tangible improvements in industry systems. Modeling and mitigating security inequities requires a combination of techniques from economic, data scientific, and social science methodologies to develop new tools for systematically understanding and mitigating insecure behavior. Through novel experimental methodology, I empirically show strong evidence of bounded rationality in security behavior: Users make mathematically modelable tradeoffs between the protection offered by security behaviors and the costs of practicing those behaviors, which even in a highly usable system may outweigh the benefits, especially for less resourced users. These findings emphasize the need for industry systems that balance structural inequities and accommodate behavioral variance between users rather than one-size-fits-all security solutions. More broadly, my techniques for modeling and accounting for inequities have offered key insights in growing technical areas beyond security, including algorithmic fairness. Bio: Elissa Redmiles is a Ph.D. Candidate in Computer Science at the University of Maryland and has been a visiting researcher with the Max Planck Institute for Software Systems and the University of Zurich. Elissa’s research interests are broadly in the areas of security and privacy. She uses computational, economic, and social science methods to conduct research on behavioral security. Elissa seeks to understand users’ security and privacy decision-making processes and specifically investigate inequalities that arise in these processes and to mitigate those inequalities through the design of systems that facilitate safety equitably across users. Elissa is the recipient of a NSF Graduate Research Fellowship, a National Science Defense and Engineering Graduate Fellowship, and a Facebook Fellowship. Her work has appeared in popular press publications such as Scientific American, Business Insider, Newsweek, and CNET and has been recognized with the John Karat Usable Privacy and Security Student Research Award, a Distinguished Paper Award at USENIX Security 2018, and a University of Maryland Outstanding Graduate Student Award.

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Sachin Ravi will present his Pre FPO "Meta-Learning for Data and Processing Efficiency" on Tuesday, February 26, 2019 at 9am in CS 301.
by Nicki Mahler 19 Feb '19

19 Feb '19

Sachin Ravi will present his Pre FPO "Meta-Learning for Data and Processing Efficiency" on Tuesday, February 26, 2019 at 9am in CS 301. The members of his committee are as follows: Kai Li (adviser), Jonathan Cohen (PNI), Hugo Larochelle (Google Brain), Jia Deng, and Karthik Narasimhan. All are welcome to attend. Please see below for talk title and abstract. Title : Meta-Learning for Data and Processing Efficiency Abstract : Deep learning models have shown great success in a variety of areas, including computer vision, natural language processing, and reinforcement learning. However, deep learning methods still lack the efficiency and flexibility of human learning. Current deep learning methods involve training on a large amount of data to produce a model that can then specialize to the specific task encoded by the training data. Humans, on the other hand, are constantly learning and mastering new concepts throughout our lives with comparatively little experience. In order to bridge this gap, previous work has suggested the use of meta-learning. Rather than learning how to do a specific task, meta-learning involves learning how-to-learn and utilizing this knowledge to learn new tasks more effectively. This talk focuses on work using meta-learning to improve the data and processing efficiency of deep learning models when learning new tasks. In the first part, we discuss a meta-learning model for the few-shot learning problem, where the aim is to be able to learn a new classification task having unseen classes with few labeled examples. We use a LSTM-based meta-learner model to learn both the initialization and the optimization algorithm used to train another neural network in the few-shot regime and show that our method compares favorably to nearest-neighbor approaches. The second part of the thesis deals with improving the predictive uncertainty of models in the few-shot learning setting. Proper predictive uncertainty is key to deploying machine learning models in the wild (especially in settings with little data), as proper human intervention can be applied when a model’s prediction is known to be uncertain. Using a Bayesian perspective, we propose a meta-learning method which efficiently amortizes hierarchical variational inference across tasks, learning a prior distribution over neural network weights so that a few steps of gradient descent will produce a good task-specific approximate posterior. Finally, we focus on applying meta-learning in the context of making choices that impact processing efficacy. When training a network on multiple tasks, we have a choice between interactive parallelism (training on different tasks one after another) and independent parallelism (using the network to process multiple tasks concurrently). For the simulation environment we consider, we show that there is a trade-off between these two types of processing choices in deep neural networks, where one can learn faster by using interactive parallelism but at the cost of having to do tasks serially. We then discuss a meta-learning algorithm for an agent to learn how to train itself with regard to this trade-off in an environment with unknown serialization cost.

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Yatin Manerkar will present his Pre-FPO "Hierarchical Memory Consistency Verification of Parallel System Implementations" on Tuesday, February 26th at 10am in CS 302
by Nicki Mahler 19 Feb '19

19 Feb '19

Yatin Manerkar will present his Pre-FPO on Tuesday, February 26, 2019 at 10am in CS 302. The members of his committee are as follows: Margaret Martonosi (Adviser), Aarti Gupta (reader), Daniel Lustig (NVIDIA, reader), Sharad Malik (EE, non-reader), and Zachary Kincaid (non-reader) All are welcome to attend. The talk title and abstract follow below. Title: Hierarchical Memory Consistency Verification of Parallel System Implementations Abstract: With the end of Moore's Law and Dennard scaling, system developers have turned to parallel architectures and languages to continue to improve performance. Memory consistency models (MCMs) specify the ordering requirements for memory operations used for synchronization in these parallel systems, so MCM verification is critical to overall system correctness. My work enables efficient formal MCM modelling and verification across the stack, from high-level languages down to Verilog RTL. In this talk, I will discuss two components of my thesis: PipeProof and RTLCheck. PipeProof offers the first-ever automated verification of a hardware design specification against its instruction-level MCM across all possible programs (an infinite space). PipeProof uses a combination of abstraction refinement and happens-before graphs that are checked using an SMT (Satisfiability Modulo Theories) solver, and enables hardware architects to automatically ensure that their design respects its MCM before even a line of RTL (Register Transfer Language) is implemented. Once the processor's low-level Verilog RTL exists, the RTLCheck approach I developed can automatically check this Verilog against the hardware specification verified by PipeProof for a set of test programs. A key contribution of RTLCheck is its set of novel algorithms for connecting the temporal execution of RTL to the axiomatic happens-before graphs used by PipeProof. The composition of PipeProof and RTLCheck allows designers to automatically check their low-level RTL against an instruction-level MCM specification.

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TODAY at 4:30pm in B205 E-Quad - Petter Nillson, California Institute of Technology
by Emily Lawrence 18 Feb '19

18 Feb '19

EE SEMINAR SERIES Speaker: Petter Nilsson, California Institute of Technology Title: Specification-Driven Design for Modular and Safe Robotics Day: Monday, February 18, 2019 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. Gerard Wysocki Robotic systems of tomorrow will be increasingly interconnected and operate amongst us, which implies a two-fold engineering challenge of great complexity and no tolerance for mistakes. This talk will explore specification-driven design methods that enforce or utilize formally written specifications for principled design, modularity, and decision-making. The first part will be centered on safety-critical control via invariance: I will show how invariance specifications in the form of assume-guarantee contracts can be leveraged to decompose problems and thus enable modular design, and how certificates for invariance can be used to formally relate low-level dynamics to a high-level abstract roadmap for planning. The second part of the talk will cover specification-guided methods for multi-robot systems, and how problem structure can be leveraged to overcome scalability challenges. The talk will be concluded with a few words about current research topics and directions for the future. Bio: Petter Nilsson received his B.S. in Engineering Physics in 2011, and his M.S. in Optimization and Systems Theory in 2013, both from KTH Royal Institute of Technology in Stockholm, Sweden, and his Ph.D. in Electrical Engineering in 2017 from the University of Michigan. In addition to his technical degrees, he holds a B.S. in Business and Economics from the Stockholm School of Economics. He is currently a postdoctoral scholar at the California Institute of Technology where he conducts research on specification-driven control and autonomy for safety-critical cyber-physical systems, with applications in autonomous driving, space exploration, and multi-agent coordination.

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Logan Stafman will be presenting his Pre FPO on Friday, February 22, 2019 at 3pm in CS 302.
by Nicki Mahler 15 Feb '19

15 Feb '19

Logan Stafman will be presenting his Pre FPO on Friday, February 22, 2019 at 3pm in CS 302. The members of his committee are as follows: Michael Freedman (adviser), Wyatt Lloyd, Kyle Jamieson, Amit Levy, and Jennifer Rexford. All are welcome to attend. Please see below for talk title and abstract. Utility Scheduling in Multi-Tenant Clusters The rapid increase in data size along with the complex patterns of data usage amongst data scientists presents new challenges for large-scale data analytics systems. Modern distributed computing frameworks must support complex applications that range from answering database queries to training machine learning models. As data centers have grown, managing their resources has become an increasingly important task. New applications have become popular that make traditional scheduling systems inadequate. In this thesis, we present distributed scheduling systems aimed at increasing cluster resource utilization by taking advantage of specific characteristics of data processing applications. We present the following systems: (i) SLAQ: a cluster scheduling system for machine learning (ML) training jobs that aims to maximize the qualities of all models trained. In exploratory model training, models can be improved more quickly by redirecting resources to jobs with the highest potential for improvement. SLAQ reduces latency and maximizes the quality of models being trained by a distributed ML cluster. (ii) ReLAQS: a cluster scheduling system for incremental approximate query processing (AQP) systems that aims to minimize the error of all approximate results. In AQP, queries compute approximate results by sampling data. In AQP, error can be reduced more quickly by allocating resources to queries with higher error. ReLAQS reduces the latency required to reach a query result with a given level of error in a shared AQP environment. These works demonstrate a novel set of methods that can be used in fine-grained scheduling to build responsive, efficient distributed systems. We have evaluated these systems on standard benchmark workloads and datasets, as well as popular ML algorithms, and show both reduced latency and increased accuracy of intermediary results.

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Colloquium Speaker-Elissa Redmiles, Thursday Feb 21
by Emily C. Lawrence 15 Feb '19

15 Feb '19

Colloquium Speaker Elissa Redmiles from University of Maryland Thursday, February 21 - 12:30pm Computer Science - Room 105 Host: Arvind Narayanan [ http://www.cs.princeton.edu/events/25817 | http://www.cs.princeton.edu/events/25817 ] Security for All: Modeling Structural Inequities to Design More Secure Systems Users often fall for phishing emails, reuse simple passwords, and fail to effectively utilize "provably" secure systems. These behaviors expose users to significant harm and frustrate industry practitioners and security researchers alike. As consequences of security breaches become ever more grave, it is important to study why humans behave seemingly irrationally. In this talk, I will illustrate how modeling the effects of structural inequities -- variance in skill, socioeconomic status, as well as culture and gender identity -- can both explain apparent irrationality in users’ security behavior and offer tangible improvements in industry systems. Modeling and mitigating security inequities requires a combination of techniques from economic, data scientific, and social science methodologies to develop new tools for systematically understanding and mitigating insecure behavior. Through novel experimental methodology, I empirically show strong evidence of bounded rationality in security behavior: Users make mathematically modelable tradeoffs between the protection offered by security behaviors and the costs of practicing those behaviors, which even in a highly usable system may outweigh the benefits, especially for less resourced users. These findings emphasize the need for industry systems that balance structural inequities and accommodate behavioral variance between users rather than one-size-fits-all security solutions. More broadly, my techniques for modeling and accounting for inequities have offered key insights in growing technical areas beyond security, including algorithmic fairness. Bio: Elissa Redmiles is a Ph.D. Candidate in Computer Science at the University of Maryland and has been a visiting researcher with the Max Planck Institute for Software Systems and the University of Zurich. Elissa’s research interests are broadly in the areas of security and privacy. She uses computational, economic, and social science methods to conduct research on behavioral security. Elissa seeks to understand users’ security and privacy decision-making processes and specifically investigate inequalities that arise in these processes and to mitigate those inequalities through the design of systems that facilitate safety equitably across users. Elissa is the recipient of a NSF Graduate Research Fellowship, a National Science Defense and Engineering Graduate Fellowship, and a Facebook Fellowship. Her work has appeared in popular press publications such as Scientific American, Business Insider, Newsweek, and CNET and has been recognized with the John Karat Usable Privacy and Security Student Research Award, a Distinguished Paper Award at USENIX Security 2018, and a University of Maryland Outstanding Graduate Student Award.

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Colloquium Speaker-Zvi Galil, TODAY at 12:30pm
by Emily C. Lawrence 14 Feb '19

14 Feb '19

Distinguished Colloquium Speaker Zvi Galil from Georgia Tech, College of Computing Thursday, February 14 - 12:30pm Computer Science - Room 105 Host: Nick Feamster 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(a)cs.princeton.edu, 609-258-4624 at least one week prior to the event.

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The "D" word: solving for "diversity" on high-tech teams, Feb. 28th at 4:30pm
by Emily Lawrence 13 Feb '19

13 Feb '19

The "D" word: solving for "diversity" on high-tech teams Janet Vertesi, Assistant Professor of Sociology at Princeton University February 28th at 4:30 PM Bowen Hall 222 This talk gives an overview of the sociological factors that affect the construction of diverse, high-performing teams. Building on a summary of key issues that affect gender and racial disparities in high-performing occupations, the talk covers the current social science and vocabulary for addressing the problem, as well as strategies for moving forward, avoiding common traps, and protecting performance-based advancement. Bio: Dubbed "Margaret Mead among the Starfleet" by the Times Literary Supplement, Janet Vertesi is Assistant Professor of Sociology at Princeton University and an expert in the sociology of science, technology, and organizations. Vertesi's past decade of research, funded by the National Science Foundation, examines how distributed robotic spacecraft teams work together effectively to produce scientific and technical results. Her book Seeing Like a Rover (University of Chicago Press, 2015) describes the collaborative work of the Mars Exploration Rover mission including the people, the images, and the robots who do science on Mars. Vertesi is also a long-time contributor to the Association of Computing Machinery conferences on human-computer interaction and computer-supported cooperative work. She is an advisory board member of the Data and Society institute in New York City and is a member of Princeton University's Center for Information Technology Policy. To join the talk, please email seasdiversity(a)princeton.edu <mailto:seasdiversity@princeton.edu> . This event co-sponsored by School of Engineering and Applied Science and Department of Computer Science.

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Reminder: Colloquium Speaker-Zvi Galil, tomorrow at 12:30pm
by Emily C. Lawrence 13 Feb '19

13 Feb '19

Distinguished Colloquium Speaker Zvi Galil from Georgia Tech, College of Computing Thursday, February 14 - 12:30pm Computer Science - Room 105 Host: Nick Feamster 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(a)cs.princeton.edu, 609-258-4624 at least one week prior to the event.

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TODAY at 4:30pm in B205 E-Quad - Suriya Gunasekar, Toyota Technological Institute at Chicago
by Emily Lawrence 11 Feb '19

11 Feb '19

Speaker: Suriya Gunasekar, Toyota Technological Institute at Chicago Title: Rethinking the Role of Optimization in Learning Day: Monday, February 11, 2019 Time: 4:30 pm Room: B205 Engineering Quadrangle Host: Prof. Peter Ramadge Abstract: In this talk, I will overview our recent progress towards understanding how we learn large capacity machine learning models, especially deep neural networks. In the modern practice of deep learning, many successful models have far more trainable parameters compared to the number of training examples. Consequently, the optimization objective for training such models have multiple minimizers that perfectly fit the training data. More problematically, while some of these minimizers generalize well to new examples, most minimizers will simply overfit or memorize the training data and will perform poorly on new examples. In practice though, when such ill-posed objectives are minimized using local search algorithms like (stochastic) gradient descent ((S)GD), the "special" minimizers returned by these algorithms have remarkably good performance on new examples. In this talk, we will explore the role optimization algorithms like (S)GD in learning overparameterized models. Bio: Suriya Gunasekar is a research assistant professor at the Toyota Technological Institute at Chicago. Prior to joining TTIC, she finished her PhD at the University of Texas at Austin advised by Prof. Joydeep Ghosh. Her research interests are broadly driven by statistical, algorithmic, and societal aspects of machine learning, including topics of optimization, high dimensional learning, and algorithmic fairness.

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