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Events Calendar



Events for April

  • CS Colloquium: Guy van den Broeck (KU Leuven) - Scalable Inference and Learning for High-Level Probabilistic Models

    Thu, Apr 02, 2015 @ 09:45 AM - 10:50 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Guy van den Broeck, KU Leuven

    Talk Title: Scalable Inference and Learning for High-Level Probabilistic Models

    Series: CS Colloquium

    Abstract: Probabilistic graphical models are pervasive in AI and machine learning. A recent push, however, is towards more high-level representations of uncertainty, such as probabilistic programs, probabilistic databases, and statistical relational models. This move is akin to going from hardware circuits to a full-fledged programming language, and poses key challenges for inference and learning. For instance, we encounter a fundamental limitation of classical learning algorithms: they make strong independence assumptions about the entities in the data (e.g., images, web pages, patients, etc.). These assumptions fail to hold in a global view of the data, where all entities are related. We also encounter a limitation of existing reasoning algorithms, which fail to scale to large, densely connected graphical models, consisting of millions of interrelated entities.

    In this talk, I present my research on efficient algorithms for high-level probabilistic models, called lifted inference and learning algorithms. I begin by introducing the key principles behind exact lifted inference, namely to exploit symmetry and exchangeability in the model. Next, I discuss the strengths and limitations of lifting. Building on results from database theory and counting complexity, I identify classes of tractable models, and classes where high-level reasoning is fundamentally hard. I conclude by showing the practical embodiment of these ideas, in the form of approximate inference and learning algorithms that scale up to big data and big models.

    The lecture will be available to stream HERE

    Biography: Guy Van den Broeck graduated summa cum laude with a Ph.D. in Computer Science from KU Leuven, Belgium, in 2013. He was a postdoctoral researcher at UCLA and KU Leuven. His research interests are broadly in machine learning, artificial intelligence, knowledge representation and reasoning, and statistical relational learning. His work was awarded the ECCAI AI Dissertation Award 2014, Scientific Prize IBM Belgium for Informatics 2014, and Alcatel-Lucent Innovation Award 2009. He is the recipient of the best student paper award at ILP 2011 and a best paper honorable mention at AAAI 2014. For more information, see http://guyvandenbroeck.com
    Host: Computer Science Department

    Webcast: https://bluejeans.com/44222652

    Location: Olin Hall of Engineering (OHE) - 132

    WebCast Link: https://bluejeans.com/442226528

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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  • Big Data and Data Science: Some Hype but Real Opportunities

    Thu, Apr 02, 2015 @ 05:00 PM - 06:00 PM

    Computer Science

    University Calendar


    Big Data and Data Science: Some Hype but Real Opportunities

    IMSC Seminar – Host: Cyrus Shahabi
    April 2 - 5:00-6:00pm
    SAL-101

    Speaker: Michael Franklin, UC Berkeley Computer Science

    Abstract
    Data is all the rage across industry and across campuses. While it may be temping to dismiss the buzz as just another spin of the hype cycle, there are substantial shifts and realignments underway that are fundamentally changing how Computer Science, Statistics and virtually all subject areas will be taught, researched, and perceived as disciplines. In this talk I will give my personal perspectives on this new landscape based on experiences organizing a large, industry-engaged academic Computer Science research project (the AMPLab), in helping to establish a campus-wide Data Science research initiative (the Berkeley Institute for Data Science), and my participation on a campus task force charged with mapping out Data Science Education for all undergraduates at Berkeley. I will make the case that there are real opportunities across campus in both education and research, and that Data Science should be viewed as an emerging discipline in its own right.

    Bio
    Michael Franklin is the Thomas M. Siebel Professor of Computer Science and Chair of the Computer Science Division at the University of California, Berkeley. Prof. Franklin is also the Director of the Algorithms, Machines, and People Laboratory (AMPLab) at UC Berkeley. The AMPLab currently works with 27 industrial sponsors including founding sponsors Amazon Web Services, Google, and SAP. AMPLab is well-known for creating a number of popular systems in the Open Source Big Data ecosystem including Spark, Mesos, GraphX and MLlib, all parts of the Berkeley Data Analytics Stack (BDAS). Prof. Franklin is a co-PI and Executive Committee member for the Berkeley Institute for Data Science, part of a multi-campus initiative to advance Data Science Environments. He is an ACM Fellow, a two-time winner of the ACM SIGMOD "Test of Time" award, has several "Best Paper" awards and two CACM Research Highlights selections, and is recipient of the outstanding Advisor Award from the Computer Science Graduate Student Association at Berkeley.

    Location: Henry Salvatori Computer Science Center (SAL) - 101

    WebCast Link: https://bluejeans.com/952662854

    Audiences: Everyone Is Invited

    Posted By: Lizsl De Leon

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  • AI Seminar- Predicting human behaviors in techno-social systems: fighting abuse and illicit activities

    Fri, Apr 03, 2015 @ 11:00 AM - 12:00 PM

    Information Sciences Institute

    Conferences, Lectures, & Seminars


    Speaker: Emilio Ferrara , Indiana University

    Talk Title: Predicting human behaviors in techno-social systems: fighting abuse and illicit activities

    Series: Artificial Intelligence Seminar

    Abstract: The increasing availability of data across different socio-technical systems, such as online social networks, social media, and mobile phone networks, presents novel challenges and intriguing research opportunities. As more online services permeate through our everyday life and as data from various domains are connected and integrated with each other, the boundary between the real and the online worlds becomes blurry. Such data convey both online and offline activities of people, as well as multiple time scales and resolutions.

    In this talk, I'll discuss my research efforts aimed at characterizing and predicting human behaviors and activities in techno-social worlds: starting by discussing network structure and information spreading on large social networks, I'll move toward characterizing entire online conversations, such as those around big real-world events, to capture the dynamics driving the emergence of collective attention and trending topics. I'll describe a machine learning framework leveraging these insights to detect promoted campaigns that mimic grassroots conversation. Aiming at learning the signature of abuse at the level of the single individuals, I'll illustrate the challenges posed by characterizing human activity as opposed to that of synthetic entities (social bots) that attempt emulate us, to persuade, smear, tamper or deceive. I'll draw a parallel with detecting illicit activities in the real world leveraging the traces left by criminals' interactions via mobile phones.

    I'll conclude envisioning the design of computational systems that will help us making effective, timely decisions (informed by social data), and create actionable policies to contribute create a better future society.


    Biography: Emilio Ferrara is research assistant professor at the School of Informatics and Computing of Indiana University, where he teaches I400/590 Mining the Social Web, and research scientist at the IU Network Science Institute. He holds a PhD in Mathematics & Computer Science with honors [University of Messina (IT), program ranked 2nd in Italy, top100 worldwide]. During his PhD years he was a visiting scholar at the Vienna University of Technology and at the Royal Holloway University of London. He was a postdoctoral fellow of the Center for Complex Networks and Systems Research at Indiana University, working with Alessandro Flammini and Fil Menczer for 2.5 years. He lead the DARPA/SMISC project on campaigns and social bots detection, and the DARPA Social Bot Detection Challenge for the IU team.

    Emilio’s research interests lie at the intersection between Network Science, Data Science, Machine Learning, and Computational Social Science. His work explores Social Networks and Social Media Analysis, Criminal Networks, and Knowledge Engineering. His research appears on top journals like Communications of the ACM and Physical Review Letters, and on several ACM and IEEE Transactions Journals and Conference Proceedings.

    He is Lead Guest Editor of the EPJ Data Science thematic series on Collective Behaviors and Networks, and serves in the Program Committees of several prestigious conferences like WWW, ICWSM, and SocInfo. Emilio is co-chair of various workshops recurring at ECCS, WWW, SocInfo, and WebScience; he was also the local & sponsor chair of ACM Web Science 2014 and publicity co-chair of SocInfo 2014.

    His work has been featured on tech and business magazines like MIT Technology Review, TIME, New Scientist, Fast Company, Engadget, Wired, and Mashable, and on the popular press including on the Guardian, the Washington Post, the Seattle Times, the Atlantic, and BBC!

    Emilio is a top 0.5% Kaggle competitor and enjoys participating to various data science competitions.

    Host: Aram Galstyan

    Webcast: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=0de28f610b2344099f9759c6f8e566f61

    Location: Information Science Institute (ISI) - 11th Flr. Conf Rm # 1135, Marina Del Rey

    WebCast Link: http://webcasterms1.isi.edu/mediasite/Viewer/?peid=0de28f610b2344099f9759c6f8e566f61d

    Audiences: Everyone Is Invited

    Posted By: Peter Zamar

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  • CS Colloquium: Steve Checkoway (Johns Hopkins) - Revealing Reality Through Reverse Engineering

    Mon, Apr 06, 2015 @ 09:45 AM - 10:50 AM

    Computer Science

    Conferences, Lectures, & Seminars


    Speaker: Steve Checkoway, Johns Hopkins

    Talk Title: Revealing Reality Through Reverse Engineering

    Series: CS Colloquium

    Abstract: Insecure computer systems in the wild can enable consequences ranging from crime to mass surveillance to (in the case of cyberphysical systems) physical destruction or even death. But how can anyone know if a particular computer system is insecure? One can rely on the representations of the system designers or manufacturers; however, the history of computers is replete with examples of claims that products are secure which are subsequently proven false. This is, in part, because computer systems tend to exhibit unanticipated, unintended, or poorly-understood behaviors that have complex interactions. As a result, the best way to learn about the security of a system is to take a detailed look at the hardware and software that comprise the system, and their interactions. In the common case where hardware designs and software source code are not available, reverse engineering the system is often the best way to derive ground-truth data on how the system functions.

    In this talk, I'll describe some of my recent research where reverse engineering played a key role, covering TLS implementations with backdoors as well as cyberphysical systems. I'll also describe the scientific nature of reverse engineering as well as the positive, real-world impact reverse engineering can have on security and safety.

    The lecture will be available to stream HERE.

    Biography: Stephen Checkoway is an Assistant Research Professor in the Department of Computer Science at Johns Hopkins University and a member of the Johns Hopkins University Information Security Institute. Checkoway's research focuses on the security of embedded and cyberphysical systems. He has demonstrated exploitable vulnerabilities in such embedded systems as electronic voting machines, laptop webcams, automobiles, and airport scanners. He received his Ph.D. in Computer Science from the University of California, San Diego in 2012.

    Host: CS Department

    Webcast: https://bluejeans.com/77493697

    Location: Olin Hall of Engineering (OHE) - 132

    WebCast Link: https://bluejeans.com/774936978

    Audiences: Everyone Is Invited

    Posted By: Assistant to CS chair

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