- talks - lists.cs.princeton.edu

Irene Lo, Jan 23rd (tomorrow) @ 12:30pm, Sherrerd 101
by Emily Lawrence 22 Jan '18

22 Jan '18

Irene Lo is giving a colloquium in ORFE on Tuesday January 23rd at 12:30pm in Sherrerd 101. She works broadly in mechanism design, with a focus on matching markets. Title and abstract are below if anyone is interested in attending. Thanks! -Matt Abstract: As market design theory increasingly shapes the design and operations of real-life marketplaces, it is important for designers to provide simple policy levers that practitioners can use to optimize platform objectives. In the school choice market, where scarce public school seats are assigned to students, a key operational issue is how to reassign seats that are vacated after an initial round of centralized assignment. Practical solutions to the reassignment problem must be simple to implement, truthful, efficient and fair while also alleviating costly student movement between schools. In this talk, I will propose and axiomatically justify a class of reassignment mechanisms, the Permuted Lottery Deferred Acceptance (PLDA) mechanisms. Our mechanisms generalize the commonly used Deferred Acceptance (DA) school choice mechanism to a two-round setting and retain its desirable incentive, fairness and efficiency properties. School choice systems typically run Deferred Acceptance with a lottery number assigned to each student to break ties in school priorities. I will show that under natural conditions on demand, correlating the tie-breaking lotteries across rounds preserves allocative welfare, and reversing the first-round lottery order minimizes reassignment among all PLDA mechanisms. Empirical investigations based on data from NYC high school admissions support our theoretical findings. This is based on joint work with Itai Feigenbaum, Yash Kanoria and Jay Sethuraman. Bio: Irene Lo is a 5th year Ph.D. student in the Industrial Engineering & Operations Research department at Columbia University, advised by Jay Sethuraman, Jacob Leshno and Yash Kanoria. Her main research interests are in operational and algorithmic issues in marketplace design, particularly in using theory to provide practical solutions for markets with social value. Her current focus is on operational challenges in school choice mechanisms. She is also interested in platform-based marketplaces, mechanism design for social good, graph theory, and games on networks. She graduated from Princeton University in 2013 with an A.B. in mathematics.

1 0

Colloquium Speaker Natalie Enright Jerger, TODAY at 11:00AM
by Emily C. Lawrence 18 Jan '18

18 Jan '18

Joint EE and CS Colloquium Speaker Prof. Natalie Enright Jerger, University of Toronto Thursday, January 18, 2018 - 11:00AM *please note, lunch will NOT be served at this talk* Computer Science - Room 105 Host: Prof. Margaret Martonosi “Architectural Techniques to Efficiently Handle Big Data Challenges” As Moore’s Law continues in the post-Dennard scaling era, architects and programmers must consider energy efficiency even more carefully as part of their designs. The energy cost of moving and storing data exceeds that of computing with that data. At the same time, we expect to see 100s of zettabytes of digital data in the next decade. This explosion of data that must be analyzes creates numerous challenges for current designs. In this talk, we will look at two techniques to address big data challenges facing computer architecture. One promising approach to boost both energy efficiency and performance is approximate computing. The approximate computing paradigm trades-off correctness for improvements in energy and/or performance by targeting key applications that do not require 100% accurate execution such as image processing and machine learning. We propose a microarchitectural technique, load value approximation that selectively predicts memory values in order to forego expensive accesses to the memory hierarchy. By predicting instead of moving data, we can save energy and improve the performance when a small amount of error is tolerable. In the second part of my talk, I will discuss the performance-cost trade-offs of interposer-based multi-chip, multi-core systems. Connecting multiple disparate chips via a silicon interposer allows us to tightly couple processors and memory within the same package for efficient data movement. I will briefly present our network solutions to realize these systems. Considering solutions that span technology, architecture and software opens up new opportunities to solve energy and performance challenges facing next generation systems. Bio: Natalie Enright Jerger is the Percy Edward Hart Professor of Electrical and Computer Engineering at the University of Toronto. Prior to joining the University of Toronto, she received her MSEE and PhD from the University of Wisconsin-Madison in 2004 and 2008, respectively. She received her Bachelor's degree from Purdue University in 2002. She is a recipient of the Ontario Ministry of Research and Innovation Early Researcher Award in 2012, the 2014 Ontario Professional Engineers Young Engineer Medal recipient and the 2015 Borg Early Career Award winner. She served as the program co-chair of the 7th Network-on-Chip Symposium and as the program chair of the 20th International Symposium on High Performance Computer Architecture. She is currently serving as the ACM SIGMICRO Vice Chair and an ACM SIGARCH Executive Committee member. Her current research explores on-chip networks, approximate computing, IoT architectures and machine learning acceleration. She is also passionate about increasing the representation of women in computing, particular in computer architecture. She currently chairs the organizing committee for the Women in Computer Architecture group (WICARCH). In 2017, she co-authored the second edition of the Computer Architecture Synthesis Lecture on On-Chip Networks with Li-Shiuan Peh and Tushar Krishna. Her research is supported by NSERC, Intel, CFI, AMD, Huawei and Qualcomm.

1 0

Reminder: Colloquium Speaker Natalie Enright Jerger, tomorrow at 11:00AM
by Emily C. Lawrence 17 Jan '18

17 Jan '18

Joint EE and CS Colloquium Speaker Prof. Natalie Enright Jerger, University of Toronto Thursday, January 18, 2018 - 11:00AM *please note, lunch will NOT be served at this talk* Computer Science - Room 105 Host: Prof. Margaret Martonosi “Architectural Techniques to Efficiently Handle Big Data Challenges” As Moore’s Law continues in the post-Dennard scaling era, architects and programmers must consider energy efficiency even more carefully as part of their designs. The energy cost of moving and storing data exceeds that of computing with that data. At the same time, we expect to see 100s of zettabytes of digital data in the next decade. This explosion of data that must be analyzes creates numerous challenges for current designs. In this talk, we will look at two techniques to address big data challenges facing computer architecture. One promising approach to boost both energy efficiency and performance is approximate computing. The approximate computing paradigm trades-off correctness for improvements in energy and/or performance by targeting key applications that do not require 100% accurate execution such as image processing and machine learning. We propose a microarchitectural technique, load value approximation that selectively predicts memory values in order to forego expensive accesses to the memory hierarchy. By predicting instead of moving data, we can save energy and improve the performance when a small amount of error is tolerable. In the second part of my talk, I will discuss the performance-cost trade-offs of interposer-based multi-chip, multi-core systems. Connecting multiple disparate chips via a silicon interposer allows us to tightly couple processors and memory within the same package for efficient data movement. I will briefly present our network solutions to realize these systems. Considering solutions that span technology, architecture and software opens up new opportunities to solve energy and performance challenges facing next generation systems. Bio: Natalie Enright Jerger is the Percy Edward Hart Professor of Electrical and Computer Engineering at the University of Toronto. Prior to joining the University of Toronto, she received her MSEE and PhD from the University of Wisconsin-Madison in 2004 and 2008, respectively. She received her Bachelor's degree from Purdue University in 2002. She is a recipient of the Ontario Ministry of Research and Innovation Early Researcher Award in 2012, the 2014 Ontario Professional Engineers Young Engineer Medal recipient and the 2015 Borg Early Career Award winner. She served as the program co-chair of the 7th Network-on-Chip Symposium and as the program chair of the 20th International Symposium on High Performance Computer Architecture. She is currently serving as the ACM SIGMICRO Vice Chair and an ACM SIGARCH Executive Committee member. Her current research explores on-chip networks, approximate computing, IoT architectures and machine learning acceleration. She is also passionate about increasing the representation of women in computing, particular in computer architecture. She currently chairs the organizing committee for the Women in Computer Architecture group (WICARCH). In 2017, she co-authored the second edition of the Computer Architecture Synthesis Lecture on On-Chip Networks with Li-Shiuan Peh and Tushar Krishna. Her research is supported by NSERC, Intel, CFI, AMD, Huawei and Qualcomm.

1 0

Timothy Murphy will present his General Exam on Tuesday January 23, 2018 at 12pm in CS 401.
by Nicki Gotsis 16 Jan '18

16 Jan '18

Timothy Murphy will present his General Exam on Tuesday January 23, 2018 at 12pm in CS 401. The members of his committee are as follows: Zak Kincaid (Advisor), Aarti Gupta, and David Walker. Please find his reading list and abstract below. Papers / Chapters: 1. "Well Structured Transition Systems Everywhere." A Finkel and Ph. Schnoebelen. 1999. 2. "An axiomatic Proof Technique for Parallel Programs I." Susan Owici and David Gries. 1976. 3. "Resources, Concurrency and Local Reasoning." Peter W O'Hearn. 2004. 4. "Abstract Interpretation: A Unified Lattice Model for Static Analysis of Programs by Construction or Approximation of Fixpoints." Patric Cousot and Radhia Cousot. 1977. 5. "Lazy Abstraction with Interpolants." K. L. McMillan. 2006. 6. "Proof Spaces for Unbounded Parallelism." Azadeh Farzan, Zak Kincaid, and Andreas Podelski. 2015. 7. "The rely-Gaurantee Method for Verifying Shared Variable Programs." Qiwen Xu, Willem-Paul de Rolver and Jifeng He. 1997. 8. "A filtering Algorithm for Constraints of difference in CSPs." Jean-Charles Regin. 1996. 9. "Synethesizing Software Verifies from Proof Rules." Sergey Grebenshchikov, Nuno P. Lopes, Corneliu Popeea, and Andrey Rybalchenko. 2012. 10. "Artificial Intelligence: A Modern Approach" Chapter 6. (CSPs). Peter Norvig and Stuart J. Russel. 1994. Textbooks: 1. "The Calculus of Computation." Bradley and Manna. 2. "Principles of Program Analysis." Nielson, Nielson, and Hankin Abstract: I will start by introducing the concept of Predicate Automata and their use in concurrent program verification. Next, I'll describe the challenge of emptiness checking for such Predicate Automata --- How does one determine when to stop expanding a configuration of states? To do this, I introduce the notion of covering. If a configuration is covered by another already expanded configuration, then we can safely stop expanding. I will then show that the problem of determining coverability between two configurations is equivalent to the structure embedding problem. Finally, I’ll present MatchEmbeds, an algorithm for the structure embedding problem: given two finite first-order structures over a common relational vocabulary, does there exist an injective homomorphism from one to the other? The structure embedding problem is NP-complete in the general case, but for monadic structures (each predicate has arity 1) I observe that it can be solved in polytime by reduction to bipartite graph matching. My algorithm extends the bipartite matching approach to the general case by using it as the foundation of a backtracking search procedure. I show that MatchEmbeds outperforms state-of-the-art constraint satisfaction solvers on difficult random instances and significantly improves the performance of a client model checker for multi-threaded programs.

1 0

Yuanzhi Li will present his Pre FPO on Friday, January 19, 2018 at 1:30pm in CS 302.
by Nicki Gotsis 16 Jan '18

16 Jan '18

Yuanzhi Li will present his Pre FPO on Friday, January 19, 2018 at 1:30pm in CS 302. The members of his committee are as follows: Sanjeev Arora (advisor), Elad Hazan (reader), Yingyu Liang (U. Wisconsin, reader), Matt Weinberg (non-reader), Yoram Singer (non-reader) Please see below for talk abstract. All are welcome to join. Abstract: The recent success of machine learning has posed a great challenge for theoreticians: we lack a strong theoretical understanding of non-linear models and non-convex objective functions, which, in turn, are widely used in practice. My research goal is to bridge the gap between theory and practice in machine learning by building new and principled methods to better explain and enhance the empirical success of these models and algorithms. In the first part I will talk about modeling. In particular, I will focus on over-parametrization: A widely used method to build models with more parameters than the number of training examples. I will show, through the example of matrix sensing, how (stochastic) gradient descent can learn an over-parametrized model much faster than the under-parametrized one, without over-fitting the data. In the next part I will talking about algorithms for non-convex machine learning tasks. I will show, through the example of non-negative matrix factorization, how to use special structures of the data to design simple, provable, efficient and practical algorithms for the learning tasks.

1 0

Colloquium Speaker Natalie Enright Jerger, Thursday Jan 18
by Emily Lawrence 09 Jan '18

09 Jan '18

Colloquium Speaker Prof. Natalie Enright Jerger, University of Toronto Thursday, January 18, 2018 - 11:00AM *please note, lunch will NOT be served at this talk* Computer Science - Room 105 Host: Prof. Margaret Martonosi & Electrical Engineering "Architectural Techniques to Efficiently Handle Big Data Challenges" As Moore's Law continues in the post-Dennard scaling era, architects and programmers must consider energy efficiency even more carefully as part of their designs. The energy cost of moving and storing data exceeds that of computing with that data. At the same time, we expect to see 100s of zettabytes of digital data in the next decade. This explosion of data that must be analyzes creates numerous challenges for current designs. In this talk, we will look at two techniques to address big data challenges facing computer architecture. One promising approach to boost both energy efficiency and performance is approximate computing. The approximate computing paradigm trades-off correctness for improvements in energy and/or performance by targeting key applications that do not require 100% accurate execution such as image processing and machine learning. We propose a microarchitectural technique, load value approximation that selectively predicts memory values in order to forego expensive accesses to the memory hierarchy. By predicting instead of moving data, we can save energy and improve the performance when a small amount of error is tolerable. In the second part of my talk, I will discuss the performance-cost trade-offs of interposer-based multi-chip, multi-core systems. Connecting multiple disparate chips via a silicon interposer allows us to tightly couple processors and memory within the same package for efficient data movement. I will briefly present our network solutions to realize these systems. Considering solutions that span technology, architecture and software opens up new opportunities to solve energy and performance challenges facing next generation systems. Bio: Natalie Enright Jerger is the Percy Edward Hart Professor of Electrical and Computer Engineering at the University of Toronto. Prior to joining the University of Toronto, she received her MSEE and PhD from the University of Wisconsin-Madison in 2004 and 2008, respectively. She received her Bachelor's degree from Purdue University in 2002. She is a recipient of the Ontario Ministry of Research and Innovation Early Researcher Award in 2012, the 2014 Ontario Professional Engineers Young Engineer Medal recipient and the 2015 Borg Early Career Award winner. She served as the program co-chair of the 7th Network-on-Chip Symposium and as the program chair of the 20th International Symposium on High Performance Computer Architecture. She is currently serving as the ACM SIGMICRO Vice Chair and an ACM SIGARCH Executive Committee member. Her current research explores on-chip networks, approximate computing, IoT architectures and machine learning acceleration. She is also passionate about increasing the representation of women in computing, particular in computer architecture. She currently chairs the organizing committee for the Women in Computer Architecture group (WICARCH). In 2017, she co-authored the second edition of the Computer Architecture Synthesis Lecture on On-Chip Networks with Li-Shiuan Peh and Tushar Krishna. Her research is supported by NSERC, Intel, CFI, AMD, Huawei and Qualcomm.

1 0

Steven Engelhardt will present his Pre-FPO "Automated discovery of privacy violations on 1 million websites" on January 3, 2018 at 10am in CS 302
by Nicki Gotsis 29 Dec '17

29 Dec '17

Steven Engelhardt will present his Pre-FPO "Automated discovery of privacy violations on 1 million websites" on January 3, 2018 at 10am in CS 302. The members of his committee follow below. Committee members: Arvind Narayanan (adviser), readers: Ed Felten and Nick Feamster; nonreaders: Jen Rexford and Prateek Mittal Everyone is invited to attend his talk. The talk title and abstract follow below. Title: Automated discovery of privacy violations on 1 million websites Abstract: Online tracking is increasingly invasive. Gone are the days where a user can "reset" their online profile by clearing their browser's cookies. Instead, users face persistent, cross-device tracking which blends their offline activity with their online behavior. Tracking protection provided by browsers is often ineffective and circumventable, while solutions based on voluntary cooperation, such as Do Not Track, haven’t had meaningful adoption. Knowledgeable users may turn to anti-tracking tools and ad-blockers for protection, but we find that even these more advanced solutions fail to fully protect against the techniques we study. In this talk, I will present OpenWPM, a platform we developed for flexible and modular web measurement. We’ve used OpenWPM to run repeated, large-scale studies leading to the discovery of numerous privacy and security violations across the web and in emails. These discoveries have helped curtail the adoption of advanced tracking techniques, and have repeatedly informed policy debates and browser privacy decisions. OpenWPM has already been used in over 20 academic studies, including a number of which I’ll highlight in this talk. In particular, I'll discuss the detection of several persistent tracking techniques, including device fingerprinting, cookie syncing, and cookie respawning. We’ve found that nearly every new HTML5 API introduced ends up being misused by trackers for device fingerprinting. The misuse is often invisible to users and publishers alike, and in many cases was not anticipated by API designers. I’ll show how we’re able to use the structure of fingerprinting scripts to detect new techniques, and take a critical look at how the API design process can be changed to prevent such misuse in the future. I'll also explore the budding industry of trackers which use PII-derived identifiers to track users across devices, and even into the offline world. I’ll demonstrate a novel bait technique, which allows us to spoof the presence of PII on a large number of sites. I’ll show how trackers exfiltrate the spoofed PII from websites and emails through the abuse of browser features. Finally, I'll take a critical look at the efforts by browser vendors to protect user privacy and will end with a proposal for a path forward, grounding tracking protection in measurement.

1 0

Victoria Yao will present her Pre FPO "Integrative network-based approaches to analyze genomics data" on Tuesday, December 19, 2017 at 11am in CS 402.
by Nicki Gotsis 15 Dec '17

15 Dec '17

Victoria Yao will present her Pre FPO on Tuesday, December 19, 2017 at 11am in CS 402. The members of her committee are as follows: Adviser: Olga Troyanskaya Readers: Ryan Adams and Mona Singh Non-readers: Barbara Engelhardt and Coleen Murphy (MOL BIO/LSI) The title and abstract for her talk follows below. Title: Integrative network-based approaches to analyze genomics data Abstract: The increasingly commonplace generation of genome-scale data provides us with a wealth of biological knowledge that captures global molecular-level changes in diverse model organisms and humans. However, these large data are often noisy, highly heterogenous, and lack the resolution required to study key aspects of metazoan complexity, such as tissue and cell-type specificity. Furthermore, in order to efficiently apply the results of such studies towards understanding the etiology of complex human diseases, we must consider integrative approaches that fuse computational predictions and model organism experimentation in the context of human data. Here, we apply this core idea to the development of three different network-based approaches, with example applications to the study of neurodegenerative diseases. First, we will discuss diseaseQUEST , an integrative computational-experimental framework that combines human quantitative genetics with in silico functional network representations of model organism biology to systematically identify disease gene candidates. This framework leverages a novel semi-supervised Bayesian network integration approach to predict tissue- and cell-type specific functional relationships between genes in model organisms. We use diseaseQUEST to predict candidate genes for 25 different human diseases and traits using C. elegans as a model system, with a particular focus on Parkinson’s disease. Second, we will describe an approach that takes advantage of high-quality neuron-specific molecular profiles of cells that vary in vulnerability to Alzheimer’s disease obtained in mouse to generate neuron-specific functional networks in human. We then combine these network models with human quantitative genetics data to prioritize likely Alzheimer’s disease candidates. Finally, we will present a network-based approach that systematically identifies differential isoform interactions. We apply this approach to the study of tissue and environment dynamics in Alzheimer’s disease. BQ_BEGIN BQ_END

1 0

4:30pm Thu Dec 14 talk on "Advancing eLearning with data" in E-Quad B205
by Jennifer Rexford 14 Dec '17

14 Dec '17

SEMINAR TODAY: Department of Electrical Engineering Seminar Speaker: Christopher Brinton Zoomi Inc Title: Advancing eLearning with Data Date: Thursday, December 14, 2017 Time: 4:30pm Room: E-Quad, Room B205 Hosts: Profs. Prateek Mittal & H. Vincent Poor Abstract: Electronic learning (eLearning) has witnessed explosive growth over the past decade, with learning scenarios from corporate training to higher education now supporting full course delivery online. Despite the conveniences offered to both learners and instructors, eLearning has been widely criticized for poorer learning outcomes compared with those obtained by traditional classrooms, in terms of metrics such as engagement and knowledge transfer. Today’s learning technology systems, however, can be equipped with infrastructure to capture a plethora of data about learners as they take courses online, such as their multimedia clickstream measurements, assessment responses, and social interactions with one another. The availability of this data presents the opportunity to develop fine-granular models of learners, which can in turn drive methodologies at the intersection of machine learning, data science, and the science of learning aimed at improving learning outcomes. In this talk, I will describe the research I have undertaken to improve the quality of eLearning, while at my company Zoomi Inc., during my graduate studies, and as an instructor. Broadly speaking, my team’s investigations have followed three, complementary thrusts: Predictive Learning Analytics (PLA), Social Learning Networks (SLN), and Deep Learning Personalization (DLP). PLA involves developing algorithms for early detection of learning outcomes that model from behavioral data and can provide actionable analytics to instructors. SLN involves quantifying how learners seek and disseminate knowledge socially, and designing algorithms to predict and optimize these processes. DLP involves systematically dividing course content into micro-segments, and combining topic, learner, and reinforcement learning models to adapt the material presented to individual learners in real time. In describing these thrusts, I will point out results Zoomi has witnessed in deployments to different learning scenarios, and will also discuss open research questions. Bio: Dr. Christopher G. Brinton is the Head of Advanced Research at Zoomi Inc, an AI-driven learning technology company he co-founded in 2013, and a Lecturer in Electrical Engineering at Princeton University. His research focus is developing systems and methods to improve the quality of student learning, through predictive learning analytics, social learning networks, and individualization. Chris co-authored the book The Power of Networks: Six Principles that Connect our Lives, and has reached over 250,000 students through MOOCs based on his book. A recipient of the 2016 Bede Liu Best Dissertation Award in Electrical Engineering, Chris received his PhD from Princeton in 2016, his Master’s from Princeton in 2013, and his BSEE from The College of New Jersey (valedictorian and summa cum laude) in 2011, all in Electrical Engineering.

1 0

Haoyu Zhang will present his Pre FPO talk "Resource Management for Advanced Data Analytics at Large Scale" on Tuesday December 19th, 2017 at 10:00am in CS 302.
by Nicki Gotsis 12 Dec '17

12 Dec '17

Haoyu Zhang will present his Pre FPO talk on Tuesday December 19th, 2017 at 10:00am in CS 302. The members of his committee are: Michael Freedman (advisor), Kyle Jamieson, Wyatt Lloyd (readers), Kai Li, Jennifer Rexford (examiners). All are welcome to attend. Title: Resource Management for Advanced Data Analytics at Large Scale The rapidly growing size of data and the complexity of analytics present new challenges for large-scale data processing systems. Modern distributed computing frameworks need to support not only embarrassingly parallelizable batch jobs, but also advanced applications analyzing text and multimedia data using multi-stage DAG queries and machine learning (ML) models. Given the high costs of advanced data analytics, resource management is crucial. New applications and workloads expose vastly different characteristics which makes traditional scheduling systems inadequate, and at the same time offer many opportunities that lead to new system designs for better performance. In this talk, I will present resource management system designs that can efficiently utilize cluster resources by leveraging the insights from advanced data analytics applications. We identify and study the following three key scenarios: (i) VideoStorm: a video analytics system that processes thousands of video analytics queries on live video streams over large clusters. VideoStorm's offline scheduler generates resource-quality profiles for vision processing queries, and its online scheduler allocates resources to maximize performance in terms of quality and lag, in contrast to the commonly used fair sharing of resources. (ii) SLAQ: a cluster scheduling system for approximate ML training jobs that aims to maximize the overall job quality. ML training typically involves many iterations that continually improve the quality of the model. Yet in exploratory settings, better models can be obtained faster by directing resources to jobs with the most potential for improvement. SLAQ collects quality and resource usage information from concurrent jobs, and allocates resources to maximize quality improvement based on highly-tailored model quality predictions. (iii) Riffle: an optimized shuffle service for big-data analytics frameworks that significantly improves I/O efficiency and scales to process petabytes of data. The all-to-all data transfer (i.e., shuffle) in modern big-data systems (such as Spark and Hadoop) becomes the scaling bottleneck for multi-stage analytics jobs, due to the superlinear increase in disk I/O operations as data volume increases. Riffle significantly improves system performance by merging fragmented intermediate files and efficiently scheduling the merge operations. We have built and deployed the systems at large clusters and performed extensive evaluation with real production workloads. Our results show significant improvement in resource efficiency, job completion time, and system throughput.

1 0