- talks - lists.cs.princeton.edu

Colloquium Speaker-Elena Glassman, TODAY at 12:30
by Emily C. Lawrence 26 Feb '18

26 Feb '18

CS Department Colloquium Speaker Elena Glassman, University of California, Berkeley Monday, February 26, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. Adam Finkelstein "Interactive systems for code and data demography " Programming-the means by which we tell computers what to do-has changed a lot over time. Programming today means programming alongside hundreds of fellow students, thousands of fellow professional software engineers at a particular company, or millions of fellow developers in the open-source community sharing their code online. In this talk, I will describe several interactive systems I have built that exploit the structure within large volumes of peer-produced code to help individual programmers learn how to write more correct, readable code. These systems are made possible by code demography, which I define as statistics, algorithms, and visualizations that help people comprehend and interact with population-level structure and trends in large code corpora. The key to my approach is designing or inferring abstractions that capture critical features and abstract away variation that is irrelevant to the user. Code demography can reveal strategically diverse sets of aligned code examples which, according to theories of human concept learning, help people learn, i.e., construct mental abstractions that generalize well. I will focus this talk on two families of systems that use program analysis, program synthesis, and visualization to either power active data-driven teaching in large programming classrooms or passive knowledge sharing within developer communities. Some of these systems have been integrated into UC Berkeley's largest introductory programming class, which regularly enrolls over 1500 students. I will conclude with my vision for how the techniques of code demography can be generalized to more types of messy, structured, complex data corpora in order to help data scientists and enable new data-driven programming paradigms. Bio: Elena Glassman is an EECS postdoctoral researcher at UC Berkeley, in the Berkeley Institute of Design, funded by the NSF ExCAPE Expeditions in Computer Augmented Program Engineering grant and the Moore/Sloan Data Science Fellowship from the UC Berkeley Institute for Data Science (BIDS). She earned her PhD in EECS at the MIT CS & AI Lab in August 2016, where she created scalable systems that analyze, visualize, and provide insight into the code of thousands of programming students. She has been a summer research intern at both Google and Microsoft Research, working on systems that help people teach and learn. She recently joined the program committees of ACM CHI, ACM Learning at Scale, and two SPLASH workshops on programming usability. She was awarded the 2003 Intel Foundation Young Scientist Award, both the NSF and NDSEG graduate fellowships, the MIT EECS Oral Master's Thesis Presentation Award, a Best of CHI Honorable Mention, and the MIT Amar Bose Teaching Fellowship for innovation in teaching methods. Prior to entering the field of human-computer interaction (HCI), she earned her MEng in the MIT CSAIL Robot Locomotion Group and was a visiting researcher at Stanford in the Stanford Biomimetics and Dextrous Manipulation Lab.

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Reminder: Colloquium Speaker-Elena Glassman, Tomorrow at 12:30
by Emily C. Lawrence 25 Feb '18

25 Feb '18

CS Department Colloquium Speaker Elena Glassman, University of California, Berkeley Monday, February 26, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. Adam Finkelstein "Interactive systems for code and data demography " Programming-the means by which we tell computers what to do-has changed a lot over time. Programming today means programming alongside hundreds of fellow students, thousands of fellow professional software engineers at a particular company, or millions of fellow developers in the open-source community sharing their code online. In this talk, I will describe several interactive systems I have built that exploit the structure within large volumes of peer-produced code to help individual programmers learn how to write more correct, readable code. These systems are made possible by code demography, which I define as statistics, algorithms, and visualizations that help people comprehend and interact with population-level structure and trends in large code corpora. The key to my approach is designing or inferring abstractions that capture critical features and abstract away variation that is irrelevant to the user. Code demography can reveal strategically diverse sets of aligned code examples which, according to theories of human concept learning, help people learn, i.e., construct mental abstractions that generalize well. I will focus this talk on two families of systems that use program analysis, program synthesis, and visualization to either power active data-driven teaching in large programming classrooms or passive knowledge sharing within developer communities. Some of these systems have been integrated into UC Berkeley's largest introductory programming class, which regularly enrolls over 1500 students. I will conclude with my vision for how the techniques of code demography can be generalized to more types of messy, structured, complex data corpora in order to help data scientists and enable new data-driven programming paradigms. Bio: Elena Glassman is an EECS postdoctoral researcher at UC Berkeley, in the Berkeley Institute of Design, funded by the NSF ExCAPE Expeditions in Computer Augmented Program Engineering grant and the Moore/Sloan Data Science Fellowship from the UC Berkeley Institute for Data Science (BIDS). She earned her PhD in EECS at the MIT CS & AI Lab in August 2016, where she created scalable systems that analyze, visualize, and provide insight into the code of thousands of programming students. She has been a summer research intern at both Google and Microsoft Research, working on systems that help people teach and learn. She recently joined the program committees of ACM CHI, ACM Learning at Scale, and two SPLASH workshops on programming usability. She was awarded the 2003 Intel Foundation Young Scientist Award, both the NSF and NDSEG graduate fellowships, the MIT EECS Oral Master's Thesis Presentation Award, a Best of CHI Honorable Mention, and the MIT Amar Bose Teaching Fellowship for innovation in teaching methods. Prior to entering the field of human-computer interaction (HCI), she earned her MEng in the MIT CSAIL Robot Locomotion Group and was a visiting researcher at Stanford in the Stanford Biomimetics and Dextrous Manipulation Lab.

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Sivakanth Gopi will present pre-FPO talk on March 1st, 2018 at 2pm in CS 302.
by GradInfo 23 Feb '18

23 Feb '18

Sivakanth Gopi will present pre-FPO talk on March 1st, 2018 at 2pm in CS 302. The memebers of his committee are as follows: Zeev Dvir (advisor and examiner), Princeton University Ran Raz (examiner), Princeton University Avi Wigderson (examiner), IAS Mark Zhandry (reader), Princeton University Swastik Kopparty (reader), Rutgers University All are welcome to attend. =============== Title: Locality in coding theory Abstract: Error correcting codes have been extremely successful in practice to build storage/communication systems which are resilient to noise and corruptions. They also found several theoretical applications in complexity theory, pseudorandomness, hardness of approximation (PCPs) and cryptography. Each application requires codes with specific properties. One such property desirable in many applications is ‘locality’. Locality refers to the ability to perform operations like decoding/correction/testing in sublinear or sometimes constant time. For example, a constant query locally decodable code (LDC) allows decoding of any message bit in constant time given a corrupted encoding of the message. The goal of our research is to understand the power and limitations of codes with ‘locality’. We show that one can get non-trivial locality and still match the best known rate-distance tradeoffs of traditional error correcting codes (Gilbert-Varshamov bound). We prove some conditional lower bounds on codes with locality and give new directions for constructing such codes by showing an analytic characterization of LDCs. We also explore applications of such codes to privacy and data storage. We show how to use ideas from existing constructions of LDCs to design 2-server private information retrieval schemes where a user can efficiently and privately query a database replicated among two (non-communicating) servers without revealing any information about their query to either server. We also show limits and improved constructions of maximally recoverable local reconstruction codes (MR LRCs) which are local codes designed specifically for distributed data storage applications; these codes are already being deployed in practice, for example in Microsoft Azure storage. ================

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Fwd: TODAY 2/22 at 4:30pm in B205 E-Quad: EE Seminar Series - Stefanos Nikolaidis (University of Washington)
by Jennifer Rexford 22 Feb '18

22 Feb '18

> From: Lidia Stokman <lstokman(a)Princeton.EDU> > Date: February 22, 2018 at 8:27:47 AM EST > To: ee-seminar(a)Princeton.EDU > Subject: TODAY 2/22 at 4:30pm in B205 E-Quad: EE Seminar Series - Stefanos Nikolaidis (University of Washington) > Reply-To: Lidia Stokman <lstokman(a)Princeton.EDU> > > > EE SEMINAR SERIES > > > Speaker: Stefanos Nikolaidis, University of Washington > Title: Mathematical Models of Adaptation in Human-Robot Collaboration > Day: Thursday, February 22, 2018 > Time: 4:30 pm > Room: B205 Engineering Quadrangle > Host: Prof. Niraj Jha > > > Abstract: > The goal of my research is to improve human-robot collaboration by integrating mathematical models of human behavior into robot decision making. I develop game-theoretic algorithms and probabilistic planning techniques that reason over the uncertainty in the human internal state and its dynamics, enabling autonomous systems to act optimally in a variety of real-world collaborative settings. > While much work in human-robot interaction has focused on leader-assistant teamwork models, the recent advancement of robotic systems that have access to vast amounts of information suggests the need for robots that take into account the quality of the human decision making and actively guide people towards better ways of doing their task. In this talk, I propose an equal partners model, where human and robot engage in a dance of inference and action, and I focus on one particular instance of this dance: the robot adapts its own actions via estimating the probability of the human adapting to the robot. I start with a bounded memory model of human adaptation parameterized by the human adaptability - the probability of the human switching towards a strategy newly demonstrated by the robot. I then propose data-driven models that capture subtler forms of adaptation, where the human teammate updates their expectations of the robot’s capabilities through interaction. Integrating these models into robot decision making allows for human-robot mutual adaptation, where coordination strategies, informative actions and trustworthy behavior are not explicitly modeled, but naturally emerge out of optimization processes. Human subjects experiments in a variety of collaboration and shared autonomy settings show that mutual adaptation significantly improves human-robot team performance, compared to one-way robot adaptation to the human. > > Bio: > Stefanos Nikolaidis completed his PhD at Carnegie Mellon's Robotics Institute in December 2017 and he is currently a research associate at the University of Washington, Computer Science & Engineering. His research lies at the intersection of human-robot interaction, algorithmic game-theory and planning under uncertainty. Stefanos develops decision making algorithms that leverage mathematical models of human behavior to support deployed robotic systems in real-world collaborative settings. He has a MS from MIT, a MEng from the University of Tokyo and a BS from the National Technical University of Athens. He has additionally worked as a research specialist at MIT and as a researcher at Square Enix in Tokyo. He has received a Best Enabling Technologies Paper Award from the IEEE/ACM International Conference on Human-Robot Interaction, has an upcoming best paper nomination from the same conference and was a best paper award finalist in the International Symposium on Robotics.

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Colloquium Speaker-Misha Sra, Tuesday, Feb 27
by Emily Lawrence 21 Feb '18

21 Feb '18

CS Department Colloquium Speaker Misha Sra, Massachusetts Institute of Technology Tuesday, February 27, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. Szymon Rusinkiewicz " A Boundless, Fluid and Programmable Reality" Abstract: Virtual reality, in the form we recognize it today, dates all the way back to 1968 when Ivan Sutherland created the first head-mounted display. Half a century later, we've only just begun to explore what VR might ultimately become. Imagine standing at the edge of a virtual cliff -- your heart racing and knees shaking. Cognitively, you know the edge is not there, and yet your unconscious responses say otherwise. This paradox is the root of the concept of presence. Extend this to walking, using virtual objects, and interacting with anyone, anywhere, and the power of VR starts to emerge. VR has already shown great potential across an extensive array of application domains like therapy, education, journalism, architecture, data visualization, remote collaboration, and entertainment. My doctoral research explores novel ways of enhancing presence by incorporating spatial and sensory affordances from the physical world for single and multiuser applications. The challenge of integrating the real world with the virtual one while simultaneously trying to prevent the couch or the wall from disrupting presence can be surprisingly complex. But doing so successfully can provide a magical experience where the real and virtual worlds blend seamlessly to create something new. In this talk, I will show different methods of integrating key elements from the user's physical environment into the virtual experience that lead to an enhanced sense of presence. The goal is to invent a new language of "interactive experience design" based on managing perception and attention, a language as rich and varied as that of cinema. The methods aim to maintain an unbroken sense of presence despite the constraints of the physical world and the disruptions imposed by interacting with the system. I will conclude with a few research challenges and opportunities in virtual and mixed reality that I am excited about and hope to work on in the future. Bio: Misha Sra is a PhD student working with Prof. Pattie Maes in the Fluid Interfaces Group at the MIT Media Lab. Her research involves development of novel virtual and mixed reality experiences that integrate elements from the user's real world environment with the goal of enhancing the user's sense of presence. She is interested in applications of virtual and mixed reality in learning, healthcare, collaboration, and entertainment and is more broadly interested in data-driven design of immersive environments. Misha has published at the most selective HCI and VR venues such as CHI, UIST, IEEE VR, and VRST where she received a best paper award. From 2014-2015, she was a Robert Wood Johnson Foundation wellbeing research fellow at the MIT Media Lab. In spring 2016, she received the Silver Award in the annual Edison Awards global competition that honors excellence in human-centered design and innovation. To learn more about Misha's work visit: https://web.media.mit.edu/~sra

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Colloquium Speaker-Elena Glassman, Monday, Feb 26
by Emily Lawrence 20 Feb '18

20 Feb '18

CS Department Colloquium Speaker Elena Glassman, University of California, Berkeley Monday, February 26, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. Adam Finkelstein "Interactive systems for code and data demography " Programming-the means by which we tell computers what to do-has changed a lot over time. Programming today means programming alongside hundreds of fellow students, thousands of fellow professional software engineers at a particular company, or millions of fellow developers in the open-source community sharing their code online. In this talk, I will describe several interactive systems I have built that exploit the structure within large volumes of peer-produced code to help individual programmers learn how to write more correct, readable code. These systems are made possible by code demography, which I define as statistics, algorithms, and visualizations that help people comprehend and interact with population-level structure and trends in large code corpora. The key to my approach is designing or inferring abstractions that capture critical features and abstract away variation that is irrelevant to the user. Code demography can reveal strategically diverse sets of aligned code examples which, according to theories of human concept learning, help people learn, i.e., construct mental abstractions that generalize well. I will focus this talk on two families of systems that use program analysis, program synthesis, and visualization to either power active data-driven teaching in large programming classrooms or passive knowledge sharing within developer communities. Some of these systems have been integrated into UC Berkeley's largest introductory programming class, which regularly enrolls over 1500 students. I will conclude with my vision for how the techniques of code demography can be generalized to more types of messy, structured, complex data corpora in order to help data scientists and enable new data-driven programming paradigms. Bio: Elena Glassman is an EECS postdoctoral researcher at UC Berkeley, in the Berkeley Institute of Design, funded by the NSF ExCAPE Expeditions in Computer Augmented Program Engineering grant and the Moore/Sloan Data Science Fellowship from the UC Berkeley Institute for Data Science (BIDS). She earned her PhD in EECS at the MIT CS & AI Lab in August 2016, where she created scalable systems that analyze, visualize, and provide insight into the code of thousands of programming students. She has been a summer research intern at both Google and Microsoft Research, working on systems that help people teach and learn. She recently joined the program committees of ACM CHI, ACM Learning at Scale, and two SPLASH workshops on programming usability. She was awarded the 2003 Intel Foundation Young Scientist Award, both the NSF and NDSEG graduate fellowships, the MIT EECS Oral Master's Thesis Presentation Award, a Best of CHI Honorable Mention, and the MIT Amar Bose Teaching Fellowship for innovation in teaching methods. Prior to entering the field of human-computer interaction (HCI), she earned her MEng in the MIT CSAIL Robot Locomotion Group and was a visiting researcher at Stanford in the Stanford Biomimetics and Dextrous Manipulation Lab.

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Steven Goldfeder will present his Pre FPO on Monday, February 26, 2018 at 3PM in CS 302.
by Nicki Gotsis 20 Feb '18

20 Feb '18

Steven Goldfeder will present his Pre FPO on Monday, February 26, 2018 at 3PM in CS 302. The members of his committee are: Arvind Narayanan (adviser), Ed Felten, Mark Zhandry, and Matt Weinberg, Rosario Gennaro All are welcome to join. Abstract and title follow below. Increasing scalability and privacy for cryptocurrencies via off-chain protocols Abstract: The limits of Bitcoin’s scripting language motivate the need for off-blockchain protocols that extend the functionality of Bitcoin scripts. These protocols are run out-of-band by the transacting parties, but they are constructed in a manner that cryptographically binds them to on-chain scripts. Even with the advent of Ethereum, which provides a much richer scripting language, there are still privacy and scalability benefits to running off-chain protocols, even when on-chain analogs exist. I present off-chain protocols for a variety of applications as well as a general framework for off-chain smart contracts. An important tool for constructing off-chain protocols is threshold-signatures, a primitive that enables distributing the signing power of a given public key into n shares, such that at least k shares are required to produce a signature from that key. I show how to construct threshold signatures that are compatible with Bitcoin, and use this primitive to build off-chain protocols for privacy-preserving access control and escrow services. Turning to more advanced smart contracts, I show off-chain protocols for the fair exchange of digital goods and services for payment. Finally, we present a private and scalable smart contract system which enables arbitrary smart contracts that are enforced via off-chain protocols.

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Colloquium Speaker-Marshini Chetty, TODAY at 12:30
by Emily C. Lawrence 20 Feb '18

20 Feb '18

CS Department Colloquium Speaker Marshini Chetty, Princeton University Tuesday, February 20, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. David Dobkin "Parting the Cloud: Empowering End-Users through Internet Transparency" Why is my Internet slow? Why have I run out of data again? Is my thermostat talking to strangers? Internet users face seemingly simple questions like these every time they connect to the Internet with increasingly significant consequences for inaccurate information. Yet, as networks, applications, and devices proliferate and become more complex, questions like these are getting harder to answer. I study, design, build, and evaluate technologies to help users answer these questions, by improving the “transparency” of the networks, applications, and networked devices used to get online. My research goal is to empower Internet users by providing them with accurate and real-time information about, and control over, Internet performance, costs, privacy, and security. By doing so, my work gives Internet users the power to make well-informed decisions and agency for protecting themselves against and holding others accountable for online malfeasance. My research informs Internet policy by providing evidence of what kinds of transparency Internet users require and respond to. In this talk, I describe why Internet transparency is so important and how challenging it is to provide transparency to end-users. I present three projects that make Internet costs, online privacy and security, and Internet constraints more transparent for end-users. These projects include a system for giving adult users information and control over home broadband data usage, a prototype for helping elementary school age children learn about and manage their own Internet safety, and a mixed method approach for understanding how Internet constraints affect end-users in developing countries. I conclude the talk with open questions for making the Internet more transparent for end-users. Bio: Marshini Chetty is a research scholar in the Department of Computer Science at Princeton University where she directs the Princeton Human-Computer Interaction Laboratory. She specializes in human-computer interaction, usable security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from performance, costs to privacy and security. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from University of Cape Town, South Africa. Prior to joining Princeton, Marshini was an assistant professor in the College of Information Studies at the University of Maryland, College Park. Her work on creating consumer-facing tools for monitoring Internet speeds won a CHI best paper award; her research has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, and two Google Faculty Research Awards.

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Reminder: Colloquium Speaker-Marshini Chetty, Tomorrow at 12:30
by Emily C. Lawrence 19 Feb '18

19 Feb '18

CS Department Colloquium Speaker Marshini Chetty, Princeton University Tuesday, February 20, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. David Dobkin "Parting the Cloud: Empowering End-Users through Internet Transparency" Why is my Internet slow? Why have I run out of data again? Is my thermostat talking to strangers? Internet users face seemingly simple questions like these every time they connect to the Internet with increasingly significant consequences for inaccurate information. Yet, as networks, applications, and devices proliferate and become more complex, questions like these are getting harder to answer. I study, design, build, and evaluate technologies to help users answer these questions, by improving the “transparency” of the networks, applications, and networked devices used to get online. My research goal is to empower Internet users by providing them with accurate and real-time information about, and control over, Internet performance, costs, privacy, and security. By doing so, my work gives Internet users the power to make well-informed decisions and agency for protecting themselves against and holding others accountable for online malfeasance. My research informs Internet policy by providing evidence of what kinds of transparency Internet users require and respond to. In this talk, I describe why Internet transparency is so important and how challenging it is to provide transparency to end-users. I present three projects that make Internet costs, online privacy and security, and Internet constraints more transparent for end-users. These projects include a system for giving adult users information and control over home broadband data usage, a prototype for helping elementary school age children learn about and manage their own Internet safety, and a mixed method approach for understanding how Internet constraints affect end-users in developing countries. I conclude the talk with open questions for making the Internet more transparent for end-users. Bio: Marshini Chetty is a research scholar in the Department of Computer Science at Princeton University where she directs the Princeton Human-Computer Interaction Laboratory. She specializes in human-computer interaction, usable security, and ubiquitous computing. Marshini designs, implements, and evaluates technologies to help users manage different aspects of Internet use from performance, costs to privacy and security. She often works in resource-constrained settings and uses her work to help inform Internet policy. She has a Ph.D. in Human-Centered Computing from Georgia Institute of Technology, USA and a Masters and Bachelors in Computer Science from University of Cape Town, South Africa. Prior to joining Princeton, Marshini was an assistant professor in the College of Information Studies at the University of Maryland, College Park. Her work on creating consumer-facing tools for monitoring Internet speeds won a CHI best paper award; her research has been funded by the National Science Foundation, the National Security Agency, Intel, Microsoft, and two Google Faculty Research Awards.

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Linpeng Tang will present his FPO, "Efficient Processing and Delivery of Multimedia Data" on Friday, 2/23/2018 at 1:30pm in CS 402
by Nicki Gotsis 15 Feb '18

15 Feb '18

Linpeng Tang will present his FPO, "Efficient Processing and Delivery of Multimedia Data" on Friday, 2/23/2018 at 1:30pm in CS 402. The members of this committee is as follows: Kai Li (Adviser); Examiners: Jennifer Rexford, Kyle Jamieson, and Kai Li; Readers: Wyatt Lloyd and Michael Freedman Everyone is invited to attend. His abstract follows below. The explosion of multimedia data on the Internet in recent years has greatly enriched people's online experience. However, it also poses a great challenge to analyze and process, and then deliver such content to the worldwide audience. This dissertation presents novel approaches to improve the overall efficiency of the stack by novel system designs. First, to improve the caching performance of the flash-memory caches for content delivery network, this thesis proposes RIPQ, a framework for efficient and advanced caching with flash memory. Traditional implementations of these algorithms generate random writes that are not appropriate for flash devices, decreasing the device's performance and lifespan. RIPQ overcomes this issue by aggregating small writes, colocating items with similar priorities, and perform lazy updates to achieve low overhead. Second, this thesis proposes Chess, which uses popularity prediction for higher quality video streaming. Although better encodings improve video streaming, they are also compute-intensive, and it's infeasible to encode all videos uploaded to Facebook with the highest quality codec. However, because the accesses to videos are highly skewed, we may obtain most of the benefit by only running the compute- intensive encoding on a small portion of popular videos. Chess meets this demand by designing an approximate but fast base predictor with the access history information, and using an online learning method to combine multiple such predictors as well as the social signals to boost accuracy. Lastly, this thesis investigates how to accelerate deep learning models on manycore CPUs. Deep learning is now widely used for analyzing multimedia data, but it's compute-intensive, which constitutes its major bottleneck. The manycore CPU, combining both high FLOPS and a flexible computing model, is a promising solution to this problem. However, existing frameworks are still mainly optimized for GPU, and don't run efficiently on this architecture. To overcome this issue, this thesis proposes Graphi, the first attempt to accelerate the execution of computation graphs for deep learning models on this architecture. Graphi determines the optimal parallel settings with a profiling step, runs concurrent operations with low contention, and further reduces execution makespan with critical-path first scheduling.

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