February 2018 - talks - lists.cs.princeton.edu

Jieming Mao will present a pre-FPO talk on Tuesday, March 6, 2018 at 2:00 pm in CS 402
by Barbara A. Mooring 28 Feb '18

28 Feb '18

Jieming Mao will present a pre-FPO talk on Tuesday, March 6, 2018 at 2:00 pm in CS 402. Members of his Committee are as follows: Mark Braverman (Adviser) - Princeton University Matt Weinberg (Reader) - Princeton University Bernard Chazelle (Reader) - Princeton University Ran Raz (Examiner) - Princeton University Elad Hazan (Examiner) - Princeton University All are welcome to attend. ================ Title: Algorithms in Strategic or Noisy Environments Abstract: Algorithms are often used in strategic or noisy environments. These factors can significantly affect the performance of algorithms. My research aims to design algorithms that are robust against such factors.In the first half of the talk, I will talk about no-regret learning algorithms in strategic environments. We study a specific setting in which a fully strategic seller repeatedly sells goods to a buyer who no-regret learns over time. We give a full characterization of whether the seller can extract additional revenue from the buyer. In the second half of the talk, I will talk about parallel algorithms for finding top-k items with noisy pairwise comparisons. The problem has applications in crowdsourcing, peer grading and recommender systems. We show tight trade-offs between sample complexity and round complexity.

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Colloquium Speaker-Brian Smith, Monday, Mar 5
by Emily C. Lawrence 27 Feb '18

27 Feb '18

CS Department Colloquium Speaker Brian Smith, Columbia University Monday, March 5, 2018 - 12:30pm Computer Science - Room 105 Host: Prof. Arvind Narayanan "Analyzing Human Behavior to Make Computing More Useful" Abstract: Computing devices play a more significant role in our lives than ever before, so it is more important than ever for them to understand who we are as people on a deep level. But many of today’s user interfaces do not have such an understanding: rather, their designs are based on developers’ intuitions alone. This often leads to mismatches between how useful computing systems promise to be and how useful they are in practice. In this talk, I will show how analyzing and even modeling human behavior can unlock insights that help resolve such mismatches, resulting in systems that are significantly more useful than what they would otherwise be. I will discuss four results that I have worked on: (1) a system for interacting with objects by looking at them, (2) a system for typing on smartphones more quickly and with much fewer errors, (3) a system that can recognize players and recommend video game levels from controller inputs alone, and (4) a system that makes it possible for people who are blind to play the same types of racing games that sighted players can play with the same speed and sense of control that sighted players have. Bio: Brian A. Smith is a Ph.D. candidate in Computer Science at Columbia University, where he is a member of the Computer Vision Laboratory and the Computer Graphics and User Interfaces Laboratory. His research is in human–computer interaction, accessibility, and game design, and focuses on analyzing human behavior to make computing more useful. He has been awarded the NDSEG Fellowship, an NSF IGERT data science traineeship, and Columbia Engineering’s Extraordinary Teaching Assistant Award. He received his MS and BS in Computer Science from Columbia University.

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Naman Agawal will present a pre-FPO talk on March 5th at 2:00 pm in CS 401
by Barbara A. Mooring 27 Feb '18

27 Feb '18

Naman Agawal will present a pre-FPO talk on March 5th at 2:00 pm in CS 401 Members of his committee are as follows: 1. Elad Hazan (adviser and examiner) - Princeton University 2. Sanjeev Arora (examiner) - Princeton University 3. Mark Bravermann (examiner) - Princeton University 4. Yoram Singer (reader) - Princeton University 5. Yuxin Chen (reader) - Princeton University All are invited to attend. ============= Title: Efficient Second-Order Methods for Machine Learning Abstract: With the advent of massive datasets and increasingly complex tasks, modern machine learning systems pose several new efficiency and scalability challenges for optimization methods. Stochastic gradient-based methods form the state-of-the-art in large-scale machine learning optimization due to their extremely efficient per-iteration computational cost. Second-order methods, i.e. methods that use the second derivative of the optimization objective, are known to provably enable faster convergence as well as guaranteeing better solutions. The latter however have been less explored due to the high cost of computing the second-order information. In our research, we study second-order stochastic methods for optimization problems arising in machine learning that match the per-iteration cost of gradient based methods, yet enjoy the faster convergence properties of second-order optimization overall leading to faster algorithms than the best known gradient based methods. In the first part of the talk I will describe our algorithm LISSA (Linear Time Second-Order Stochastic Algorithm) for convex machine learning tasks such as ridge and logistic regression. LISSA incorporates second order information from such objectives, running in time proportional to the sparsity of the underlying input. Further we extend LISSA with careful matrix sampling techniques based on leverage scores to provide the best known theoretical running times for optimization in these settings. In the second part of the talk we will consider non-convex empirical risk minimization objectives including deep neural networks. I will describe our algorithm FastCubic, an efficient second order algorithm which provably converges to better local minima faster than known first-order methods.

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Colloquium Speaker-Misha Sra, TODAY at 12:30
by Emily C. Lawrence 27 Feb '18

27 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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Reminder: Colloquium Speaker-Misha Sra, tomorrow at 12:30
by Emily C. Lawrence 26 Feb '18

26 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, 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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