Colloquia and Seminars

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Computer Science events calendar in HTTP ICS format for of Google calendars, and for Outlook.

Academic Calendar at Technion site.

Upcoming Colloquia & Seminars

  • Theory Seminar: Exploring Crypto Dark Matter: New Simple PRF Candidates and Their Applications

    Speaker:
    David Wu (Stanford University)
    Date:
    Wednesday, 24.10.2018, 12:30
    Place:
    Taub 201

    Pseudorandom functions (PRFs) are one of the fundamental building blocks in cryptography. Traditionally, there have been two main approaches for PRF design: the "practitioner's approach" of building concretely-efficient constructions based on known heuristics and prior experience, and the "theoretician's approach" of proposing constructions and reducing their security to a previously-studied hardness assumption. While both approaches have their merits, the resulting PRF candidates vary greatly in terms of concrete efficiency and design complexity. In this work, we depart from these traditional approaches by exploring a new space of plausible PRF candidates. Our guiding principle is to maximize simplicity while optimizing complexity measures that are relevant to cryptographic applications. Our primary focus is on weak PRFs computable by very simple circuits--specifically, depth-2 ACC^0 circuits. Concretely, our main weak PRF candidate is a "piecewise-linear" function that first applies a secret mod-2 linear mapping to the input, and then a public mod-3 linear mapping to the result. We also put forward a similar depth-3 strong PRF candidate. The advantage of our approach is twofold. On the theoretical side, the simplicity of our candidates enables us to draw many natural connections between their hardness and questions in complexity theory or learning theory. On the applied side, the piecewise-linear structure of our candidates lends itself nicely to applications in secure multiparty computation (MPC). In this talk, I will introduce our new PRF candidates and highlight some of the connections between our candidates and questions in complexity theory, learning theory, and MPC.

    Joint work with Dan Boneh, Yuval Ishai, Alain Passelègue, and Amit Sahai.

  • Exploring the signal manifold of super-imposed pulses

    Speaker:
    Charles Sutton, M.Sc. Thesis Seminar
    Date:
    Thursday, 25.10.2018, 14:00
    Place:
    Taub 401
    Advisor:
    Prof. A. Bruckstein

    Large points cloud X in $R^{n\times D}$ are often assumed to be sampled from a k-dimensional manifold where $k 1$). However, there is no evidence that this technique extends to other manifolds. This work aims to verify how the multi-scale singular value analysis of a manifold can extend to any manifold. In this work, we focus our effort on signal manifolds of super-imposed pulses (SIPS), due to their generic nature and widespread use in signal processing applications. First, we examine why the current state of the art cannot be extended to SIPS manifolds. We prove that the current approaches rely upon averaging methods that are too sensitive to the manifold’s shape. Then, we propose a method that is agnostic to the shape of manifold by utilizing the k-medoids clustering algorithm. We then present a method to tackle the problem of the estimation of the intrinsic dimensionality, including manifolds constructed out of rather noisy signals. Our method improves upon the state of the art in estimating the intrinsic dimensionality and shows promising results for an extension to any manifold.

  • Coding Theory: Codes, Computation, and Privacy

    Speaker:
    Netanel Raviv (California Institute of Technology)
    Date:
    Sunday, 28.10.2018, 14:30
    Place:
    Taub 601

    Data intensive tasks have been ubiquitous ever since the data science revolution. The immensity of contemporary datasets no longer allows computations to be done on a single machine, and distributed computations are inevitable. Since most users cannot afford to maintain a network of commodity servers, burdensome computations are often outsourced to third party cloud services. However, this approach opens a Pandora's box of potential woes, such as malicious intervention in computations, privacy infringement, and workload imbalance.

    Error correcting codes are mathematical devices that were originally developed to obtain noise resilience in digital communication. Recently, these devices have found surprising applications in solving various problems in distributed computing. This newly emerging topic, which addresses resiliency, security, and privacy issues in distributed environments through a coding-theoretic lens, is often called coded computing. In this talk I will survey some of my work on the topic, which includes coding for distributed gradient descent, an exciting new framework called Lagrange Coded Computing, and finally, an important extension of Private Information Retrieval called Private Computation.

  • CGGC Seminar: Tangent Estimation of 3D Digital Curves

    Speaker:
    Kacper Pluta (CS, Technion)
    Date:
    Sunday, 4.11.2018, 13:30
    Place:
    Room 337 Taub Bld.

    In this talk I will discuss a new tangent estimator for 3D digital curves. The proposed estimator is based on 3D digital line recognition, and it is an extension of a similar 2D tangent estimator proposed for tangent estimating along digital contours.

    The main advantages of this new tangent estimator are its speed and its asymptotic convergence – the estimated tangents converge to the ground truth as the resolution increases.

  • COLLOQUIUM LECTURE - Learning-Driven Network Protocols

    Speaker:
    Michael Schapira
    Date:
    Tuesday, 6.11.2018, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    School of Computer Science and Engineering, Hebrew University
    Host:
    Roy Schwartz

    Machine learning (ML) has deeply impacted many areas of computer science, including computer vision, natural language processing, computational biology, and beyond. Yet, computer networking has largely withstood the ML tide until recently. Recent advances suggest that this might be changing. We ask whether/when traditional network protocol design, which traditionally relies on the application of algorithmic insights by human experts, can be replaced by a data-driven, ML-guided approach. We will investigate this question in the context of the fundamental challenge of congestion control on the Internet. Short bio: ========== Michael Schapirs is an associate professor at the School of Computer Science and Engineering, the Hebrew University of Jerusalem. He is also the scientific co-leader of the Fraunhofer Cybersecurity Center at Hebrew University, and a member of the Center for the Study of Rationality and of the Israeli Center of Research Excellence in Algorithms. Prior to joining the Hebrew University he was a visiting scientist at Google NYC (2011/12), where he worked with the Infrastructure Networking group. He was also a postdoctoral researcher at UC Berkeley and Yale University (jointly), with Prof. Joan Feigenbaum and Prof. Scott Shenker (2008-2010), and at Princeton University, with Prof. Jennifer Rexford (2010/11). Prof. Schapira is a recipient of the Allon Fellowship (2011), the Microsoft Research Faculty Fellowship (2013), the Hebrew University President's Prize (2014), the Wolf Foundation's Krill Prize (2015), an ERC Starting Grant (2015), 2 IETF/IRTF Applied Networking Research Prizes (2014+2017), and a Google Faculty Research Award (2017). Schapira holds a B.Sc. in Mathematics and Computer Science, a B.A. in Humanities, and a Ph.D. in Computer Science, all from the Hebrew University (received in 2004, 2004, and 2008, respectively). His Ph.D. dissertation, titled ''The Economics of Internet Protocols'', was written under the supervision of Prof. Noam Nisan. During his graduate studies, he spent time at UC Berkeley and Yale University as a visiting student, interned at Microsoft Research Silicon Valley, and worked at BrightSource Industries Israel (BSII). ========================= Refreshments will be served from 14:15 Lecture starts at 14:30

  • COLLOQUIUM LECTURE - Facing Old New Frontiers in Visual Object Recognition Using Deep Learning

    Speaker:
    Daphna Weinshall
    Date:
    Tuesday, 13.11.2018, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    School of Computer Science and Engineering, Hebrew University
    Host:
    Roy Schwartz

    The emergence of very effective deep learning techniques in recent years has affected almost all areas of research remotely related to AI, and computer vision in particular has been changed irreversibly. In this talk I will focus on visual object recognitions. The incredible recent progress in this area, and the availability of very effective public domain tools for object recognition in images, allows us to reopen old questions and approach them from new directions with new tools. I will talk about two such questions. Specifically, in the first part of the lecture I will talk about curriculum learning, where a learner is exposed to examples whose difficulty level is gradually increased. This heuristic has been empirically shown to improve the outcome of learning in various models. Our main contribution is a theoretical result, showing that learning with a curriculum speeds up the rate of learning in the context of the regression and the hinge loss. Interestingly, we also show how curriculum learning and hard-sample mining, although conflicting at first sight, can coexist harmoniously within the same theoretical model. In the second part of the lecture I will talk about a new generative deep learning model, which we call GM-GAN. I will show how this model can be used for novelty detection, and also augment data in a semi-supervised setting when the labeled sample is small. I will conclude by showing how GM-GAN can be used for unsupervised clustering. Short Bio:
    Daphna Weinshall received the BSc degree in mathematics and computer science from Tel-Aviv University, Tel-Aviv Israel, in 1982. She received the MSc and PhD degrees in mathematics and statistics from Tel-Aviv University in 1985 and 1986, respectively, working on models of evolution and population genetics. Between 1987 and 1992, she visited the center for biological information processing at MIT and the IBM T.J. Watson Research Center. In 1993, she joined the Institute of Computer Science at the Hebrew University of Jerusalem, where she is now a full professor. Her research interests include computer and biological vision, as well as machine and human learning. Her recent interests include the learning of distance functions, object class recognition, cognitive passwords, and Virtual Reality in schizophrenia research.

  • COLLOQUIUM LECTURE - Parallelizing Inherently Sequential Computations by Breaking Dependences Precisely

    Speaker:
    Madan Musuvathi
    Date:
    Tuesday, 18.12.2018, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    https://www.microsoft.com/en-us/research/people/madanm/
    Host:
    Roy Schwartz
  • COLLOQUIUM LECTURE - Toward human-centered programming language design

    Speaker:
    Joshua Sunshine
    Date:
    Tuesday, 1.1.2019, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    Institute for Software Research at Carnegie Mellon University
    Host:
    Roy Schwartz

    Programming languages are a tool for human thought, expression, and work yet they are principally designed using mathematical and engineering techniques. In this talk, I will describe how our group has applied human-centered design techniques --- interviews, participatory design exercises, and qualitative analysis of developer forums --- in the design of three research programming systems (Plaid, Glacier, and Obsidian). I will speak frankly about the strengths and weaknesses of these approaches and discuss speculative new techniques. Short Bio: ========== Joshua Sunshine is a Systems Scientist in the Institute for Software Research at Carnegie Mellon University. He has broad research interests at the intersection of programming languages and software engineering. He is particularly interested in better understanding of the factors that influence the usability of reusable software components. He completed his Ph.D. in Software Engineering from Carnegie Mellon in December 2013. His dissertation focused on the usability of software libraries with ordering constraints (API protocols). He was advised by Jonathan Aldrich. He graduated from Brandeis University in 2004 and worked for almost four years as a software engineer before starting graduate school. ============================ Refreshments will be served from 14:15 Lecture starts at 14:30

  • COLLOQUIUM LECTURE - Consolidating and Exploring Open Textual Knowledge

    Speaker:
    Ido Dagan
    Date:
    Tuesday, 15.1.2019, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    Department of Computer Science, Bar Ilan University
    Host:
    Roy Schwartz

    Abstract: T B A

    Short Bio:
    Ido Dagan holds B.Sc. (Summa Cum Laude) and Ph.D. degrees in Computer Science from the Technion, Israel. He conducted his Ph.D. research in collaboration with the IBM Haifa Scientific Center, where he was a research fellow in 1991. During 1992-1994 he was a Member of Technical Staff at AT&T Bell Laboratories. During 1994-1998 he has been at the Department of Computer Science of Bar Ilan University, to which he returned in 2003. During 1998-2003 he was co-founder and CTO of a text categorization startup company, FocusEngine, and VP of Technology at LingoMotors, a Cambridge Massachusetts company which acquired FocusEngine.