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

  • Breaking the Bluetooth Pairing the Fixed Coordinate Invalid Curve Attack

    Speaker:
    Lior Neumann, M.Sc. Thesis Seminar
    Date:
    Wednesday, 4.3.2020, 11:00
    Place:
    Room 601 Taub Bld.
    Advisor:
    Prof. Eli Biham

    Bluetooth is a widely deployed standard for wireless communications between mobile devices. It uses authenticated Elliptic Curve Diffie-Hellman for its key exchange. In this paper we show that the authentication provided by the Bluetooth pairing protocols is insufficient and does not provide the promised MitM protection. We present a new attack that modifies the y-coordinates of the public keys (while preserving the x-coordinates). The attack compromises the encryption keys of all of the current Bluetooth authenticated pairing protocols, provided both paired devices are vulnerable. Specifically, it successfully compromises the encryption keys of 50% of the Bluetooth pairing attempts, while in the other 50% the pairing of the victims is terminated. The affected vendors have been informed and patched their products accordingly, and the Bluetooth specification had been modified to address the new attack. We named our new attack the ``Fixed Coordinate Invalid Curve Attack''. Unlike the well known ``Invalid Curve Attack'' of Biehl et. al. which recovers the private key by sending multiple specially crafted points to the victim, our attack is a MitM attack which modifies the public keys in a way that lets the attacker deduce the shared secret.

  • ceClub: Service Rates in Distributed Systems with Redundancy

    Speaker:
    Prof. Emina Soljanin (Rutgers University)
    Date:
    Wednesday, 4.3.2020, 11:30
    Place:
    Taub 201 Taub Bld.

    Applications such as distributed learning and edge computing strive to maximize the number of service requests (e.g., for data access) that can be concurrently executed by the system. Redundancy, in the form of simple replication and erasure coding, has emerged as an efficient and robust way to enable simultaneous access of different data objects by many users competing for the system’s resources. Here, replication and coding of data affect the rates at which users can be simultaneously served. In this talk, we will first introduce the notion of the service rate region of a redundancy scheme, and present some examples where this region is known. We will then explain the recently recognized connections with batch and switch codes and combinatorial optimization on graphs. We will discuss some systems issues as well.

    Emina Soljanin is a professor of Electrical and Computer Engineering at Rutgers. Before moving to Rutgers in January 2016, she was a (Distinguished) Member of Technical Staff for 21 years in various incarnations of the Mathematical Sciences Research Center of Bell Labs. Her interests and expertise are wide, currently ranging from distributed computing to quantum information science. She is an IEEE Fellow, an outstanding alumnus of the Texas A&M School of Engineering, the 2011 Padovani Lecturer, a 2016/17 Distinguished Lecturer, and 2019 President for the IEEE Information Theory Society.

  • Leveraging Machine Learning Algorithms in Online Portfolio Selection

    Speaker:
    Guy Uziel, Ph.D. Thesis Seminar
    Date:
    Wednesday, 4.3.2020, 13:30
    Place:
    Room 601 Taub Bld.
    Advisor:
    Prof. R. El-Yaniv

    Online Portfolio Selection, aiming to optimize the allocation of wealth across a set of assets, is a fundamental research problem in computational finance and machine learning. Despite the theoretical challenges, the implementation of a real-world trading system is extremely challenging. This issue has been extensively studied across several research communities, including finance, statistics, coding and information theory and machine learning. This long-standing problem, however, still poses many challenges. We begin with a discussion of the problem of online portfolio selection in the presence of transaction costs, and we present two novel algorithms, enabling a trader to enhance any existing commission-oblivious algorithms. Our experiments which were conducted on common benchmarks show that the two new algorithms achieve state-of-the-art results. We then present an approach to handle the risk incurred while trading. First, we review multi-objective online learning, where we propose a novel framework and algorithm to address this problem in case the underlying process is stationary and ergodic. We prove that under mild conditions our algorithm is universal and thus asymptotically achieves the best possible outcome in hindsight. Later on, we show how this method can be utilized to incorporate the well-known risk proxy, conditional value at risk (CVaR) in online portfolio selection. Finally, we deal with the pattern matching algorithms and propose a novel approach to incorporate the learning of a suitable kernel using a deep neural network, in an online manner. The talk summarizes work presented in 5 papers, 4 of which were published/accepted to NIPS, AISTATS.

  • CGGC Seminar: Multidimensional Multimodal Content-Oriented Presentations

    Speaker:
    Daniil Rodin (CS, Technion)
    Date:
    Sunday, 8.3.2020, 13:30
    Place:
    Taub 301 Taub Bld.

    Contemporary presentation software provides sufficient tools for simple presentation scenarios. However, such tools have not changed for about two decades and are unsuitable for many complex modern needs. Content is typically limited to text and images only and cannot be expressed well in terms of 2D slides and bullets quantization. Such limitations are ill-suited for many modern needs where linear sequences of fixed-sized 2D slides consisting of text and images is not nearly sufficient.

    In this work, we propose a presentation system aimed at overcoming the limitations of the contemporary presentation software. To achieve this goal, we abandon the usual concept of a presentation as a 2D slide sequence, and instead treat them as continuous, automatically created, 3D scenes of non-linear hierarchical structure with multi-modal content, all without requiring any (3D) professional skills from the end user.

    Moving from the concept of discrete sequences of 2D slides towards smooth 3D multi-modal hierarchical presentations poses many difficulties, one of which is how to arrange content in a 3D space. This task becomes further complicated when the story-graph of the presentation is evolving and is more complex than a single linear story-path. In this work, we describe in detail a framework for automatically solving the task of 3D content placement, which is based on views --- 3D replacement for slides. We examplify our proposed approach with two spatial layouts for 3D non-linear presentations: ``nested spheres'' and a `building', as well as algorithms that automatically create these layouts from an abstract hierarchical story-graph.

    The techniques developed during this research for 3D presentations are also applicable in other fields. One such example is education. Native support for 3D content and interactivity opens an opportunity to create educational materials that are significantly more demonstrative and thus efficient, especially in the fields that include inherently 3D phenomena, such as geometry and physics. As another example, the hierarchical structure of a 3D scene combined with non-linear connections between the views is also present in visualization of various kinds of traffic. We explore the possibilities opened by this similarity by creating an interactive visualization system for monitoring and analyzing traffic data on a 3D globe. Our system is general and can be transparently used in different domains, which we examplify by two simulated demonstrations of use cases: Logistic Service and Data Communication. Using these examples, we show that our approach is more general than the current state of the art, and that there are significant similarities between several domains in need of interactive visualization, which are mostly treated as completely separate.

  • Hypernetworks and a New Feedback Model

    Speaker:
    Lior Wolf - COLLOQUIUM LECTURE
    Date:
    Tuesday, 31.3.2020, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    School of Computer Science, Tel-Aviv University
    Host:
    Yuval Filmus

    Hypernetworks, also known as dynamic networks, are neural networks in which the weights of at least some of the layers vary dynamically based on the input. Such networks have composite architectures in which one network predicts the weights of another network. I will briefly describe the early days of dynamic layers and present recent results from diverse domains: 3D reconstruction from a single image, image retouching, electrical circuit design, decoding block codes, graph hypernetworks for bioinformatics, and action recognition in video. Finally, I will present a new hypernetwork-based model for the role of feedback in neural computations. Short Bio: ========== Lior Wolf is a faculty member at the School of Computer Science at Tel Aviv University and a research scientist at Facebook AI Research. Before, he was a postdoc working with Prof. Poggio at CBCL, MIT. He received his PhD working with Prof. Shashua at the Hebrew U, Jerusalem. ====================================== Refreshments will be served from 14:15 Lecture starts at 14:30

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    Place:
  • Second-order Optimization for Machine Learning, Made Practical

    Speaker:
    Tomer Koren - COLLOQUIUM LECTURE
    Date:
    Tuesday, 5.5.2020, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    School of Computer Science at Tel Aviv University
    Host:
    Yuval Filmus

    Optimization in machine learning, both theoretical and applied, is presently dominated by first-order gradient methods such as stochastic gradient descent. Second-order optimization methods---that involve second-order derivatives and/or second-order statistics of the data---have become far less prevalent despite strong theoretical properties, due to their impractical computation, memory and communication costs. I will present some recent theoretical, algorithmic and infrastructural advances that allow for overcoming these challenges in using second-order methods and obtaining significant performance gains in practice, at very large scale, and on highly-parallel computing architectures. Short Bio: =========== Tomer Koren is an Assistant Professor in the School of Computer Science at Tel Aviv University since Fall 2019. Previously, he was a Senior Research Scientist at Google Brain, Mountain View. He received his PhD in December 2016 from the Technion - Israel Institute of Technology, where his advisor was Prof. Elad Hazan. His research interests are in machine learning and optimization. ===================================== Rereshments will be served from 14:15 Lecture starts at 14:30