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Conferences, Lectures, & Seminars
Events for April
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CS Colloquium: Austin Benson (Stanford) -Tools for higher-order network analysis
Mon, Apr 03, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Austin Benson , Stanford University
Talk Title: Tools for higher-order network analysis
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Networks are a fundamental model of complex systems in biology, neuroscience, engineering, and social science. Networks are typically described by lower-order connectivity patterns that are captured at the level of individual nodes and edges. However, higher-order connectivity patterns captured by small subgraphs, or network motifs, describe the fundamental structures that control and mediate the behavior of many complex systems. In this talk, I will discuss several higher-order analyses based on higher-order connectivity patterns that I have developed to gain new insights into network data. Specifically, I will introduce a motif-based clustering methodology, a generalization of the classical network clustering coefficient, and a formalism for temporal motifs to study temporal networks. I will also show applications of higher-order analysis in several domains including ecology, biology, transportation, neuroscience, social networks, and human communication.
Biography: Austin Benson is a PhD candidate at Stanford University in the Institute for Computational and Mathematical Engineering where he is advised by Professor Jure Leskovec of the Computer Science Department. His research focuses on developing data-driven methods for understanding complex systems and behavior. Broadly, his research spans the areas of network science, applied machine learning, tensor and matrix computations, and computational social science. Before Stanford, he completed undergraduate degrees in Computer Science and Applied Mathematics at the University of California, Berkeley. Outside of the university, he has spent summers interning at Google (four times), Sandia National Laboratories, and HP Labs.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Stephan Mandt (Disney Research) - Next generation variational inference: algorithms, models, and applications
Mon, Apr 03, 2017 @ 01:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Stephan Mandt, Disney Research
Talk Title: Next generation variational inference: algorithms, models, and applications
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Probabilistic modeling is a powerful paradigm in machine learning. In this field, we assume a generative process in order to explain our observations, and then use a Bayesian inference algorithm to reason about its parameters. Probabilistic modeling has become scalable due to stochastic variational inference which reduces Bayesian inference to non-convex stochastic optimization. This talk focuses on two new inference algorithms: variational tempering-an algorithm that operates on several artificial temperatures simultaneously to find better local optima, and constant SGD-a scalable inference algorithm with applications to hyperparameter optimization. I will then present several new models that have become tractable due to modern variational inference with applications in text modeling, recommendations, and computer vision. I will show how a probabilistic view on Google's word2vec algorithm allows for extensions to other types of high dimensional data and show new applications: analyzing supermarket shopping data, movie ratings, and tracking semantic changes of individual words over centuries of digitized books. Finally, I will show how factorized variational autoencoders allow us to analyze audience reactions to movies.
Biography: Stephan Mandt is a research scientist at Disney Research Pittsburgh, where he leads the statistical machine learning group. From 2014 to 2016 he was a postdoctoral researcher with David Blei at Columbia University, and from 2012 to 2014 a PCCM postdoctoral fellow at Princeton University. Stephan did his Ph.D. with Achim Rosch at the Institute for Theoretical Physics at the University of Cologne, supported by a fellowship of the German National Merit Foundation. His research interests include scalable approximate Bayesian inference and machine learning for media analytics.
Host: Fei Sha
Location: Kaprielian Hall (KAP) - 140
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Jelena (Marasevic) Diakonikolas (Boston University ) -From Networked Systems to Theory and Back: Full-Duplex Wireless and Beyond
Tue, Apr 04, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jelena (Marasevic) Diakonikolas, Boston University
Talk Title: From Networked Systems to Theory and Back: Full-Duplex Wireless and Beyond
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
As our ambitions to build larger and more complex networked systems are ever increasing, the following three general trends can be observed: (i) wireless data traffic is growing, (ii) the number of devices connecting to the networked systems is surging, and (iii) networks are increasingly used not only for communication, but also for computation. I will present results that are motivated by these trends and that span different aspects of networked systems: from modeling of the system components, over rigorous algorithm design and analysis, to testbed development and performance evaluation.
The unprecedented growth of the wireless traffic over scarce spectrum resources prompts the development of more spectrum-efficient techniques. On the roadmap to 5G wireless standards, full-duplex has been recognized as one of the key technologies for improving the spectrum efficiency. I will present the results on principled design of full-duplex systems that were obtained as part of a cross-disciplinary project "Full-duplex wireless: From integrated circuits to networks" (FlexICoN), which I co-initiated at Columbia. In particular, I will describe a mathematical model of an integrated full-duplex receiver developed within FlexICoN and present resource allocation algorithms tailored to the realistic receiver models. Then, I will highlight the experimental results obtained in a custom-designed full-duplex wireless testbed, developed for the evaluation of our full-duplex hardware and resource allocation and scheduling algorithms.
Further, I will highlight how the growing scale of networked systems raises the need for fast fair resource allocation algorithms and describe our novel algorithmic results for addressing these issues. Finally, I will describe some of the challenges in networks involving communication and computation, my ongoing work in this area, and future directions.
Biography: Jelena (Marasevic) Diakonikolas is a Postdoctoral Associate at Boston University and a Visiting Scholar at Massachusetts Institute of Technology. Her research focuses on principled design of networked systems. Her research on full-duplex wireless systems was awarded a Qualcomm 2015 Innovation Fellowship, was featured in IEEE Spectrum, and resulted in several invited papers. She was selected as an MIT EECS Rising Star in 2015, and named one of the "10 Women in Networking/Communications That You Should Watch" in 2016. She designed the first cellular networking hands-on lab, winning GENI GREE 2013 Best Educational Paper Award. Jelena completed her Ph.D. and M.S. degrees at Columbia University, with an M.S. Award of Excellence and a Jacob Millman Prize for Excellence in Teaching Assistance. She obtained her Bachelor's degree from University of Belgrade, where she held the two most prestigious government-awarded fellowships.
Host: CS Department
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Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Big Data & Human Behavior Seminar Series: Justin Grimmer (Stanford University) - Exploratory and Confirmatory Causal Inference for High Dimensional Interventions
Wed, Apr 12, 2017 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Justin Grimmer, Associate Professor of Political Science and Computer Science, Stanford University
Talk Title: Exploratory and Confirmatory Causal Inference for High Dimensional Interventions
Series: Big Data & Human Behavior Seminar Series
Abstract: An extensive literature in computational social science examines how features of messages, advertisements, and other corpora affect individuals' decisions, but these analyses must specify the relevant features of the text before the experiment. Automated text analysis methods are able to discover features of text, but these methods cannot be used to obtain the estimates of causal effects-”the quantity of interest for applied researchers. We introduce a new experimental design and statistical model to simultaneously discover treatments in a corpora and estimate causal effects for these discovered treatments. We prove the conditions to identify the treatment effects of texts and introduce the supervised Indian Buffet process to discover those treatments. Our method enables us to discover treatments in a training set using a collection of texts and individuals' responses to those texts, and then estimate the effects of these interventions in a test set of new texts and survey respondents. We apply the model to an experiment about candidate biographies, recovering intuitive features of voters' decisions and revealing a penalty for lawyers and a bonus for military service.
Biography: Justin Grimmer's research examines how representation occurs in American politics using new statistical methods. His first book Representational Style in Congress: What Legislators Say and Why It Matters (Cambridge University Press, 2013) shows how senators define the type of representation they provide constituents and how this affects constituents' evaluations and won the Fenno Prize from the legislative studies section. His second book The Impression of Influence: How Legislator Communication and Government Spending Cultivate a Personal Vote (Princeton University Press, with Sean J. Westwood and Solomon Messing) demonstrates how legislators ensure they receive credit for government actions. His work has appeared in the American Political Science Review, American Journal of Political Science, Journal of Politics, Political Analysis, Proceedings of the National Academy of Sciences, Regulation and Governance, and other journals.
Host: Morteza Dehghani
Location: BCI
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Satish Chandra (Facebook) - Formula-Based Software Debugging
Thu, Apr 20, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Satish Chandra , Facebook
Talk Title: Formula-Based Software Debugging
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Software often ships with known defects because fixing bugs requires expensive developer time. With the availability of virtually unlimited compute power, an interesting question is whether the burden of fixing bugs can be shifted, at least in part, from the human to the machine. This question has, of late, attracted significant activity in the software engineering and programming language communities. In this talk, I will discuss recent techniques that have been proposed towards this goal. My main focus will be on techniques that draw on the power of SMT (satisfiability modulo theories) solvers, i.e. engines that crunch first-order logic formulae.
Time permitting, I will also talk about my experiences with tech transfer at industrial research labs.
Biography: Satish Chandra obtained a PhD from the University of Wisconsin-Madison in 1997, and a B.Tech from the Indian Institute of Technology-Kanpur in 1991, both in computer science. From 1997 to 2002, he was a member of technical staff at Bell Laboratories, where his research focused on program analysis, domain-specific languages, and data-communication protocols. From 2002 to 2013, he was a research staff member at IBM Research, where his research focused on bug finding and verification, software synthesis, and test automation.
From 2013 to 2016, he worked at Samsung Research America, where he led the advanced programming tools research team. In 2016, he started working at Facebook. He is an ACM Distinguished Scientist.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium Event: Facebook Tech Talk - Query Understanding and Semantic Search
Mon, Apr 24, 2017 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Haixun Wang, Facebook
Talk Title: Facebook Tech Talk - Query Understanding and Semantic Search
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Understanding short texts is crucial to many applications, but challenges abound. First, queries do not always observe the syntax of a written language. As a result, traditional natural language processing methods cannot be easily applied. Second, queries usually do not contact in sufficient statistical signals to support many state-of-the-art approaches for text processing such as topic modeling. Third, queries are usually more ambiguous. We argue that knowledge is needed in order to better understand short texts. In this talk, I describe how to use lexical semantic knowledge provided by a well-known semantic network for short text understanding. Our knowledge-intensive approach disrupts traditional methods for tasks such as text segmentation, part-of-speech tagging, and concept labeling, in the sense that we focus on semantics in all the set tasks. We conduct a comprehensive performance evaluation on real-life data. The results show that knowledge is indispensable for short text understanding, and our knowledge-intensive approaches are effective in harvesting semantics of short texts.
Biography: Haixun Wang is a Research Scientist at Facebook and he manages the Query and Document Understanding team. Before Facebook, he was with Google Research, working on natural language processing. From 2009 to 2013, he led research in semantic search, graph data processing systems, and distributed query processing at Microsoft Research Asia. He had been a research staff member at IBM T. J. Watson Research Center from 2000 -“ 2009. He was Technical Assistant to Stuart Feldman (Vice President of Computer Science of IBM Research) from 2006 to 2007, and Technical Assistant to Mark Wegman (Head of Computer Science of IBM Research) from 2007 to 2009. He received the Ph.D. degree in Computer Science from the University of California, Los Angeles in 2000. He has published more than 150 research papers in referred international journals and conference proceedings. He served PC Chair of conferences such as CIKM'12, and he is on the editorial board of journals such as IEEE Transactions of Knowledge and Data Engineering (TKDE) and Journal of Computer Science and Technology (JCST). He won the best paper award in ICDE 2015, 10 year best paper award in ICDM 2013, and best paper award of ER 2009.
Host: CS Department
Location: Mark Taper Hall Of Humanities (THH) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium and RASC seminar: Steven Waslander (University of Waterloo) - Gimballed multi-camera localization and mapping for aerial vehicles
Thu, Apr 27, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Steven Waslander, University of Waterloo
Talk Title: Gimballed multi-camera localization and mapping for aerial vehicles
Series: RASC Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Multi-camera clusters used for visual SLAM assume a fixed calibration between the cameras, which places many limitations on its performance, and directly excludes all configurations where a camera in the cluster is mounted to a moving component. We present a calibration method and SLAM solution for dynamic multi-camera clusters, where one or more of the cluster cameras is mounted to an actuated mechanism, such as a gimbal or robotic manipulator. Our approach parametrizes the actuated mechanism using the Denavit-Hartenberg convention, then determines the calibration parameters which allow for the estimation of the time varying extrinsic transformations between camera frames. We rely on joint encoder data or camera-attached IMU to identify the extrinsic transformations during operation, and are developing active calibration methods to automate the process in the field. We validate our calibration approach using a dynamic camera cluster consisting of a static camera and a camera mounted to a pan-tilt unit as well as on a four-camera system with a single three-axis gimballed unit on a hexacopter aerial vehicle, and demonstrate that dynamic camera clusters can be provide accurate pose tracking when used to perform SLAM.
Biography: Prof. Steven Waslander is an Associate Professor in the Department of Mechanical and Mechatronics Engineering at the University of Waterloo in Waterloo, Ontario, Canada and director of the Waterloo Autonomous Vehicles Laboratory (WAVELab, http://wavelab.uwaterloo.ca). He received his B.Sc.E.in 1998 from Queen's University, his M.S. in 2002 and his Ph.D. in 2007, both from Stanford University in Aeronautics and Astronautics. He is the Program Co-Chair for the CIPPRS Computer and Robot Vision Conference, the Competition Chair for the IEEE/RSJ International Conference on Intelligent Robots and Systems and the former General Chair of the International Autonomous Robot Racing competition. His research interests lie in the areas of autonomous aerial and ground vehicles, autonomous driving, simultaneous localization and mapping, quadrotor vehicles, and machine learning. Prof. Waslander currently collaborates with numerous industrial partners, including Aeryon Labs, Clearpath Robotics, Nuvation Engineering, Denso, Renesas Electronics Corp, Qnx, and Applanix, and is a member of the NSERC Canadian Field Robotics Network. He also acts as the academic advisor to the University of Waterloo Robotics Team, which compete in multiple competitions, including the NASA Sample Return Robot Challenge, the Intelligent Ground Vehicle Competition and the University Rover Challenge.
Host: Gaurav Sukhatme
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS & ML Colloquium: Matus Telgarsky (UIUC) - Representation power of neural networks
Thu, Apr 27, 2017 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Matus Telgarsky, UIUC
Talk Title: Representation power of neural networks
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
This talk will present a series of mathematical vignettes on the representation power of neural networks. Amongst old results, the classical universal approximation theorem will be presented, along with Kolmogorov's superposition theorem. Recent results will include depth hierarchies (for any choice of depth, there exists functions which can only be approximated by slightly less deep networks when they have exponential size), connections to polynomials (namely, rational functions and neural networks well-approximate each other), and the power of recurrent networks. Open problems will be sprinkled throughout.
Biography: Matus Telgarsky is an assistant professor at UIUC. He received his PhD in 2013 at UCSD under Sanjoy Dasgupta. He works in machine learning theory; his current interests are non-convex optimization and neural network representation.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
