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

  • CSpecial Talk: How to Manage Negotiation on Employment Contract

    CSpecial Talk: How to Manage Negotiation on Employment Contract

    Speaker:
    Sarah Karu (Talent management specialist)
    Date:
    Monday, 28.5.2018, 17:00
    Place:
    Room 337 Taub Bld.

    We are happy to invite your to the sixth of series of meetings on career and job seeking which will be held at CS, on Monay, May 28, at 17:00, in room 337, CS Taub Building.

    Sarah Karu (Talent management specialist) will give a talk on "How to manage negotiation on employment contract".

    Please pre-register.

    See you there!

  • Pixel Club: The Perception-Distortion Tradeoff

    Speaker:
    Yochai Blau (EE, Technion)
    Date:
    Tuesday, 29.5.2018, 11:30
    Place:
    EE Meyer Building 1061

    Image restoration algorithms are typically evaluated by some distortion measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify perceived perceptual quality. In this work, we prove mathematically that distortion and perceptual quality are at odds with each other. Specifically, we study the optimal probability for correctly discriminating the outputs of an image restoration algorithm from real images. We show that as the mean distortion decreases, this probability must increase (indicating worse perceptual quality). As opposed to the common belief, this result holds true for any distortion measure, and is not only a problem of the PSNR or SSIM criteria. However, as we show experimentally, for some measures it is less severe (e.g. distance between VGG features). We also show that generative-adversarial-nets (GANs) provide a principled way to approach the perception-distortion bound. This constitutes theoretical support to their observed success in low-level vision tasks. Based on our analysis, we propose a new methodology for evaluating image restoration methods, and use it to perform an extensive comparison between recent super-resolution algorithms. Our study reveals which methods are currently closest to the theoretical perception-distortion bound. * Part of PhD research under the supervision of Prof. Tomer Michaeli.

  • Accelerating Innovation Through Analogy Mining

    Speaker:
    Dafna Shahaf - COLLOQUIUM LECTURE
    Date:
    Tuesday, 29.5.2018, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    Computer Science, Hebrew University
    Host:
    Yuval Filmus

    The availability of large idea repositories (e.g., the U.S. patent database) could significantly accelerate innovation and discovery by providing people with inspiration from solutions to analogous problems. However, finding useful analogies in these large, messy, real-world repositories remains a persistent challenge for either human or automated methods. In this work we explore the viability and value of learning simpler structural representations which specify the purpose of a product and the mechanisms by which it achieves that purpose. Our approach combines crowdsourcing and recurrent neural networks to extract purpose and mechanism vector representations from product descriptions. We demonstrate that these learned vectors allow us to find analogies with higher precision and recall than traditional information-retrieval methods. In an ideation experiment, analogies retrieved by our models significantly increased people's likelihood of generating creative ideas. Bio: Dafna Shahaf is an Assistant Professor in the School of Computer Science and Engineering at the Hebrew University of Jerusalem. Her research is about making sense of massive amounts of data. She designs algorithms that help people connect the dots between pieces of information and turn data into insight. She is especially interested in unlocking the potential of the many digital traces left by human activity to understand and emulate human characteristics (e.g., creativity). Her work has received multiple awards, including Best Research Paper at KDD'17 and KDD’10 and the IJCAI Early Career Award. She received her Ph.D. in Computer Science from Carnegie Mellon University. Prior to joining the Hebrew University, she was a postdoctoral fellow at Microsoft Research and Stanford University.

  • Qubit 2018 - Quantum Communication: Celebrating Bennett & Brassard's Wolf Prize for Physics

    Qubit 2018 - Quantum Communication: Celebrating Bennett & Brassard's Wolf Prize for Physics

    Date:
    Sunday, 3.6.2018, 09:30
    Place:
    CS Taub Building

    The Technion Hiroshi Fujiwara Cyber Security Research Center is happy to invite you to the Qubit 2018 - Quantum Communication: Celebrating Bennett & Brassard's Wolf Prize for Physics conference to be held on Sunday, June 3rd, 2018 at the Taub CS Building, Technion.

    Chairs:
    Eli Biham, Technion
    Tal Mor, Technion

    Speakers will be:
    Keynote: Charles Bennett, IBM Research Center:
    "Why DIY Randomness is Better Than DI Randomness"

    Keynote: Gilles Brassard, Université de Montréal:
    "Cryptography In A Quantum World"

     Lev Vaidman, Tel-Aviv University:
    "Counterfactual Communication"

    Tal Mor, Technion:
    "Quantum Computers - Is The Future Here?"

    Participating is free but requires pre-registration.

    More details and full program.

  • Matching Visual Data

    Speaker:
    Shai Avidan - COLLOQUIUM LECTURE
    Date:
    Tuesday, 5.6.2018, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    Faculty of Engineering, Tel-Aviv University
    Host:
    Yuval Filmus

    Matching pixels is used in various computer vision applications such as template matching, tracking and image editing. I will give an overview of my work in this field with an emphasize on two components. The first is how to represent the data, and the second is what similarity measure to use. I will demonstrate the results on several applications including object tracking and image editing. Bio: Shai Avidan is an Associate Professor at the School of Electrical Engineering at Tel-Aviv University, Israel. He earned his PhD at the Hebrew University, Jerusalem, Israel, in 1999. Later, he was a Postdoctoral Researcher at Microsoft Research, a Project Leader at MobilEye, a Research Scientist at Mitsubishi Electric Research Labs (MERL), and a Senior Researcher at Adobe. He publishes extensively in the fields of object tracking in video and 3-D object modeling from images. Dr. Avidan was an Associate Editor of PAMI and on the program committee of multiple conferences and workshops in the fields of Computer Vision and Computer Graphics.

  • Privacy, and Why You Should Care

    Speaker:
    Katrina Ligett - COLLOQUIUM LECTURE
    Date:
    Tuesday, 12.6.2018, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    Hebrew University
    Host:
    Yuval Filmus

    Over the past decade, the computer science research community has converged around a formal notion of data privacy, known as differential privacy, and has made substantial progress in establishing the theoretical foundations of this notion. In this talk, I will give a brief overview of differential privacy and the relevant mathematical toolkit, and then we will discuss the implications and frontiers of this research space. Can differential privacy ever be practical? How might it be useful, even in settings where we don't care about privacy? What's next for privacy? Brief bio: Katrina Ligett is an Associate Professor in the School of Computer Science and Engineering at Hebrew University. Before joining Hebrew University, she was faculty in computer science and economics at Caltech. Katrina’s primary research interests are in data privacy and algorithmic game theory. She received her PhD in Computer Science from Carnegie Mellon University in 2009 and did her postdoc at Cornell University. She is a recipient of the NSF CAREER award and a Microsoft Faculty Fellowship.

  • The 8th Annual International TCE Conference on Deep Learning: Theory & Practice

    The 8th Annual International TCE Conference on Deep Learning: Theory & Practice

    Date:
    Thursday, 14.6.2018, 08:30
    Place:
    EE, Meyer 280

    The 8th annual international TCE conference on Deep Learning: Theory & Practice will take place on Thursday, June 14, 2018 at the Technion Electrical Engneering Department, Meyer 280, and will focus on why is it working so well, and how can we improve it in various domains, such as vision, language, and audio.

    Conference Chairs: Daniel Soudry (EE Technion) and Ran El-Yaniv (CS Technion)

    Confirmed Speakers list includes:

    · Lior Wolf, Tel Aviv University, Israel
    · Michal Irani, Weizmann Institute of Science, Israel
    · Nathan Srebro, Taub Distinguished Visitor, Toyota Technological Institute at Chicago, USA
    · Uri Shalit, Technion, Israel
    · Yoav Goldberg, Bar Ilan University, Israel
    · Zachary Chase Lipton, Taub Distinguished Visitor, Carnegie Mellon University, USA

    More details, program, registration, and information on TCE.

  • CS RESEARCH DAY 2018

    CS RESEARCH DAY 2018

    Date:
    Monday, 18.6.2018, 15:00
    Place:
    CS Taub Lobby

    The 8th CS Research Day for graduate studies will be held on Monday, June 18, 2018 between 15:00-17:00, at the lobby of the CS Taub Building.

    Research Day events are opportunity for our graduate students to expose their researches using posters and presentations to CS faculty and all degrees students, Technion distinguished representatives and to high-ranking delegates from the hi-tech leading industry companies in Israel and abroad.

    The participating researches will be on various topics: Cryptology and Cyber, Data Centers and Clouds, Graphics, Intelligent Systems and Scientific Computation, Machine Learning and Information Retrieval, Systems and Applications, Testing and Verification, Theory of Computer Science.

    Participating is free but requires preregistration.

    More details and registration

    Students wishing to present their research are kindly requested to register here.

  • Novel Image and Video Super-Resolution Relying on Denoising Algorithms

    Speaker:
    Alon Brifman, M.Sc. Thesis Seminar
    Date:
    Tuesday, 19.6.2018, 11:00
    Place:
    Taub 601
    Advisor:
    Prof. M. Elad

    Single Image Super-Resolution (SISR) aims to recover a high-resolution image from a given low resolution version of it (the given image is assumed to be a blurred, down- sampled and noisy version of the original image). Video Super Resolution (VSR) targets series of given images, aiming to fuse them to create a higher resolution outcome. Although SISR and VSR seem to have a lot in common, as only the input domain changes between the two, most SISR algorithms do not have a simple extension to VSR, apart for the trivial option of applying the SISR for each frame separately. The VSR task is considered to be a more challenging inverse problem, mainly due to its reliance on a sub-pixel accurate motion estimation, which has no parallel in SISR. Another complication is the dynamics of the video, often addressed by simply generating a single frame instead of a complete output sequence. We suggest an appealing alternative to the above that leads to a simple and robust Super-Resolution framework that can be applied to SISR and then easily extended to VSR. Our work relies on the observation that image and video denoising are well-managed and very effectively treated by a variety of methods, many of which not yet effectively adapted to the super-resolution task. We exploit the Plug-and-Play framework and the recently introduced Regularization-by-Denoising (RED) approach that extends it, and show how to use these denoisers in order to handle the SISR and the VSR problems. This way, we benefit from the effectiveness and efficiency of existing image/video denoising algorithms, while solving much more challenging problems. We test our SISR framework against the NCSR algorithm that solves for denoising and super-resolution separately, and show how its denoiser can be used in order to perform highly effective super-resolution. Then we turn to video, harnessing the VBM3D video denoiser, we compare our results to the ones obtained by the DeepSR and 3DSKR algorithms, showing a tendency to a higher-quality output and a much faster processing.

  • Metabolic Modeling for Bioengineering

    Speaker:
    Edward Vitkin, Ph.D. Thesis Seminar
    Date:
    Sunday, 24.6.2018, 14:30
    Place:
    Taub 601
    Advisor:
    Dr. Zohar Yakhini

    Efficient and sustainable conversion of biomass into valuable products is a major challenge for bioengineering. The composition of the feedstock biomass and the ability of microorganisms to efficiently ferment it are two most critical factors influencing the process efficiency. Intelligent design that addresses both these factors can greatly benefit from organism metabolic models and from using them in simulations and in computer-assisted optimization of the fermentation processes. In this talk we will cover several aspects of such simulations. We will discuss the construction and improvement of single organism metabolic models as well as present high-scale simulations to optimize multi-organism fermentation processes. Even in the two-organism fermentation system many tested scenarios, such as reaction knockout analysis, may require solution for millions of optimization tasks. We will present BioLEGO, a framework to support these heavy calculations, which is deployed as a Microsoft Azure Cloud service, leveraging the associated parallel computing capacities.

  • Core-sets for Nano-Drones, Provable Big/Deep Data Learning and Rami Levy

    Speaker:
    Dan Feldman - COLLOQUIUM LECTURE
    Date:
    Tuesday, 26.6.2018, 14:30
    Place:
    Room 337 Taub Bld.
    Affiliation:
    Computer Science Dept., Haifa University
    Host:
    Yuval Filmus

    T B A

  • Metabolic Modeling for Bioengineering

    Speaker:
    Edward Vitkin, Ph.D. Thesis Seminar
    Date:
    Sunday, 1.7.2018, 14:30
    Place:
    Taub 601
    Advisor:
    Dr. Zohar Yakhini

    Efficient and sustainable conversion of biomass into valuable products is a major challenge for bioengineering. The composition of the feedstock biomass and the ability of microorganisms to efficiently ferment it are two most critical factors influencing the process efficiency. Intelligent design that addresses both these factors can greatly benefit from organism metabolic models and from using them in simulations and in computer-assisted optimization of the fermentation processes. In this talk we will cover several aspects of such simulations. We will discuss the construction and improvement of single organism metabolic models as well as present high-scale simulations to optimize multi-organism fermentation processes. Even in the two-organism fermentation system, many tested scenarios, such as reaction knockout analysis, may require solutions of millions of optimization tasks. We will present BioLEGO, a framework to support these heavy calculations, which is deployed as a Microsoft Azure Cloud service, leveraging the associated parallel computing capacities.