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Conferences, Lectures, & Seminars
Events for November
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CS Seminar: Michael J. Carey (UCI) - AsterixDB: A Counter but Intuitive Approach to Big Data Management
Mon, Nov 02, 2015 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Michael J. Carey, UC Irvine
Talk Title: AsterixDB: A Counter but Intuitive Approach to Big Data Management
Series: CS Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
We are living in the Big Data era, and we are witnessing a shift in the role of data management system: Rather than "just" being the systems of record at the heart of traditional enterprises, modern Big Data management systems must model, capture, track, and react to the current state of the world. Doing so requires the ingestion of event data, arriving from a variety of devices, as well as enabling query access to the history of captured data over time. These requirements span a variety of scientific disciplines, including the handling of data produced by a variety sensors in health care, environmental monitoring applications, traffic monitoring, dynamic social network data, and many other domains.
AsterixDB is an open source Big Data Management System (BDMS) with a feature set that's very different than those of other platforms in today's Big Data ecosystem. The system was initially co-developed by UC Irvine and UC Riverside, starting in 2009 and leading eventually to its first beta release in mid-2013. It has recently moved to Apache, where AsterixDB is now an active incubating project. Many of the system's key design decisions relate to the aforementioned shift. This talk will first briefly review AsterixDB's data model, query language, and scale-out architecture. It will then examine a number of counter-cultural aspects of the AsterixDB system, including where its data lives, its runtime architecture, its approach to streaming data, its view of transactions, and its features for handling time-based data.
Biography: Michael J. Carey is a Bren Professor of Information and Computer Sciences at UC Irvine. Before joining UCI in 2008, Carey worked at BEA Systems for seven years and led the development of BEA's AquaLogic Data Services Platform product for virtual data integration. He also spent a dozen years teaching at the University of Wisconsin-Madison, five years at the IBM Almaden Research Center working on object-relational databases, and a year and a half at e-commerce platform startup Propel Software during the infamous 2000-2001 Internet bubble. Carey is an ACM Fellow, a member of the National Academy of Engineering, and a recipient of the ACM SIGMOD E.F. Codd Innovations Award. His current interests all center around data-intensive computing and scalable data management (a.k.a. Big Data). He was also an adjunct faculty member at USC for a few years during his BEA days, but that never earned him the USC season football tickets he'd been hoping for.
Host: Shahram Ghandeharizadeh
Location: Mark Taper Hall Of Humanities (THH) - 202
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Geoffrey Zweig (Microsoft Research) - High Performance Image Captioning
Tue, Nov 03, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Geoffrey Zweig, Microsoft Research
Talk Title: High Performance Image Captioning
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
The problem of generating text conditioned on some sort of side information arises in many areas including dialog systems, machine translation, speech recognition, and image captioning. In this talk, we present a highly effective method for generating text conditioned on a set of words that should be mentioned. We apply this to the problem of image captioning by linking the generation module to a convolutional neural network that predicts a set of words that are descriptive of an image. The system placed first in the 2015 MSCoco competition on the Turing Test measure, and tied for first place overall.
This event will be available to stream HERE.
Biography: Geoffrey Zweig is a Principal Researcher, and Manager of the Speech and Dialog Group at Microsoft Research. His work centers on developing improved algorithms for speech and language processing. Recent work has focused on applications of side-conditioned recurrent neural network language models, such as image captioning and grapheme to phoneme conversion. Prior to Microsoft, Dr. Zweig managed the Advanced Large Vocabulary Continuous Speech Recognition Group at IBM Research, with a focus on the DARPA EARS and GALE programs. In the course of his career, Dr. Zweig has written several speech recognition trainers and decoders, as well as toolkits for doing speech recognition with segmental conditional random fields, and for maximum entropy language modeling. Dr. Zweig received his PhD from the University of California at Berkeley. He is the author of over 80 papers, numerous patents, is an Associate Editor of Computers Speech and Language, and is a Fellow of the IEEE.
Host: Yan Liu
Webcast: https://bluejeans.com/996018929Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/996018929
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Student Colloquium: Chen Sun (USC) - Towards Large-scale Video Understanding
Tue, Nov 10, 2015 @ 04:00 PM - 05:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Chen Sun, USC
Talk Title: Towards Large-scale Video Understanding
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
The ever-increasing popularity of video capturing devices and video sharing websites creates great opportunities for researchers to utilize the rich information encoded by consumer videos. Yet understanding videos on a large scale remains challenging: the video qualities usually vary in resolution, lighting condition and camera movement; spatiotemporal annotation of the videos could be expensive and time-consuming. As videos can be naturally represented by a hierarchy of events, activities and objects, it is essential to build a pool of mid-level concepts for semantic video interpretation. In light of these challenges, my research towards video understanding focuses on utilizing weak video-level annotations effectively, and building a stronger connection between language and vision. In this talk, I will show how Internet images can be used to localize fine-grained actions in videos without using temporal video annotations. I will then describe my approach to connect language and vision via visual concepts, and demonstrate how to automatically discover the visual concepts from parallel text and visual corpora.
Biography: Chen Sun is a Ph.D. candidate in the Computer Vision group at University of Southern California, advised by Prof. Ram Nevatia. His research interest includes Computer Vision and Machine Learning, with a focus on large-scale video understanding. Chen got his bachelor degree in Computer Science at Tsinghua University, Beijing. He has collaborated with researchers at Google Research and Facebook AI Research over the summers.
Host: USC CS
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Barna Saha (UMass) - Randomization in Data and Design
Thu, Nov 12, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Barna Saha, University of Massachusetts Amherst
Talk Title: Randomization in Data and Design
Series: CS Colloquium
Abstract:
This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Use of randomness is ubiquitous in modern computing, and promises to play a major role in today's Big Data era. There are three diverging viewpoints of how randomization impacts our ability to effectively understand and analyze data, (1) the underlying data itself may be stochastic, e.g. for the uncertainty in data acquisition; (2) randomization may appear by design to develop algorithms that are scalable; and (3) randomization can help answer why some simple heuristics are surprisingly effective on real data. In this talk, I will explain these phenomena through some basic tasks such as ranking, clustering and estimating distance or deviation of data from a formal (probabilistic) model.
Biography: Barna Saha is an Assistant Professor in the College of Information and Computer Science at the University of Massachusetts Amherst. She received her Ph.D. from the University of Maryland College Park, and then spent a couple of years at the AT&T Shannon Labs as a senior researcher before joining UMass Amherst in 2014. Her research interests are in algorithm design and analysis, and large scale data analytics. She particularly likes to work on problems that are tied to core applications but have the potentials to lead to beautiful theory. She is the recipient of Yahoo ACE Award (2015), Simons-Berkeley Research Fellowship (2015), NSF CRII Award (2015), Dean's Dissertation Fellowship (2011), and the best paper award and finalists for best papers at VLDB 2009 and IEEE ICDE 2012 respectively.
Host: David Kempe
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Heng-Tze Cheng (Google Research) - Sibyl: Google-Scale Machine Learning
Thu, Nov 19, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Heng-Tze Cheng, Google Research
Talk Title: Sibyl: Google-Scale Machine Learning
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Sibyl is one of the most widely used machine learning and prediction systems at Google, actively used in production in nearly every product area. Designed for the largest datasets at Google, Sibyl scales up to hundreds of billions of training examples and billions of features. Sibyl is used for various prediction tasks ranging from classification, regression, ranking to recommendations. Beyond core learning algorithms and scalable distributed systems, Sibyl contains a suite of data processing, monitoring, analysis, and serving tools, making it a robust and easy-to-use production system.
This lecture will be available to stream HERE.
Biography: Heng-Tze Cheng is currently a senior software engineer on the Sibyl large-scale machine learning team at Google Research. He has developed new search, ranking, and recommendation systems that are widely used across Google products. Heng-Tze received his Ph.D. from Carnegie Mellon University in 2013 and B.S. from National Taiwan University in 2008. His research interests include machine learning, user behavior modeling, and human activity recognition, with over 20 publications and 3 U.S. patents in the related fields.
Host: Yan Liu
Webcast: https://bluejeans.com/467893187Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/467893187
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Seminar: Dr. Gita Sukthankar (University of Central Florida) - Data-driven Social Informatics
Mon, Nov 30, 2015 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Gita Sukthankar, University of Central Florida
Talk Title: Data-driven Social Informatics
Series: Teamcore Seminar
Abstract: Data-driven social informatics unites models derived from social science with data-driven approaches in order to model and predict population behavior patterns. It can be used to advance our understanding of human behavior, guide public policy decisions, and improve user experience with social media platforms. In this talk, I'll describe work done at UCF's Intelligent Agents Lab (http://ial.eecs.ucf.edu/) in which we use a combination of agent-based modeling, machine learning, and crowdsourcing to model human social systems. The benefits of this approach will be illustrated using three case studies: 1) predicting the influence of social norms on smoking cessation behavior, 2) tracking campus parking usage using crowdsourcing and transportation modeling, 3) learning collaboration patterns from co-authorship networks. We believe that the combination of techniques yields a more nuanced view that relying on data alone.
Biography: Dr. Gita Sukthankar is an Associate Professor and Charles N. Millican Faculty Fellow in the Department of Computer Science at the University of Central Florida, and an affiliate faculty member at UCF's Institute for Simulation and Training. She received her Ph.D. from the Robotics Institute at Carnegie Mellon and an A.B. in psychology from Princeton University. In 2009, Dr. Sukthankar was selected for the Air Force Young Investigator award, the DARPA Computer Science Study Panel, and an NSF CAREER award. Gita Sukthankar's research focuses on multi-agent systems and computational social models. She is the lead editor of the book: Plan, Activity, and Intent Recognition: Theory and Practice and currently serves on DARPA's Information Science and Technology advisory group.
Host: Teamcore Group
Location: 107
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
