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20 November 2017

Maria KlaweSpeaker: Maria Klawe, President, Harvey Mudd College
Title: Broadening Participation in Computing
Host School: NCSU
Location: Hunt Library Auditorium
Host: Blair Sullivan (blair_sullivan at ncsu.edu)

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Abstract

Computing is one of the least diverse disciplines in science and engineering in terms of participation by women, African-Americans and Hispanics, and the only discipline where participation by women has significantly decreased over the last three decades. While our discipline does well in encouraging members of underrepresented groups to go on to graduate programs, we have been less successful in attracting members of these groups into undergraduate programs. This talk discusses successful strategies for significantly increasing the number of women and students of color majoring in computer science.

Biography

Maria Klawe is the president of Harvey Mudd College. Prior to joining HMC, she served as dean of engineering and professor of computer science at Princeton University. She also held positions at the University of British Columbia, IBM Research in California, and the University of Toronto, subsequent to earning her PhD in mathematics from the University of Alberta.

Klawe has made significant research contributions in several areas of mathematics and computer science, including functional analysis, discrete mathematics, theoretical computer science, human-computer interaction, gender issues in information technology and interactive-multimedia for mathematics education. Her current research focuses on discrete mathematics.

Klawe is also a renowned lecturer and has given talks at international conferences, national symposia, and colleges across the U.S. and Canada about diversity in science, technology, engineering, and mathematics disciplines and industries, gender and gaming, and lessons from her own career in STEM industry and education. She has devoted particular attention in recent years to improving K-12 science and mathematics education. Dr. Klawe serves as a board member of the nonprofit Math for America, chair of the board of the nonprofit EdReports.org, a fellow of the American Academy of Arts & Sciences, a trustee for the Mathematical Sciences Research Institute in Berkeley and a member of the Canada Excellence Research Chairs Selection Board.

Klawe is the recipient of numerous awards and honors, including the 2014 Women of Vision ABIE Award for Leadership and being ranked 17th on Fortune’s 2014 list of the World’s 50 Greatest Leaders. In 2015 she was honored with the Lifetime Achievement Award from the Canadian Association of Computer Science and the Achievement Award from the American Association of University Women, and she was inducted into the US News STEM Solutions Leadership Hall of Fame. She was honored by the Computing Research Association with the 2016 Distinguished Service Award.

22 January 2018

Moshe VardiSpeaker: Moshe Vardi, Karen Ostrum George Distinguished Service Professor in Computational Engineering and Director of the Ken Kennedy Institute for Information Technology, Rice University
Title: Humans, Machines, and Work: The Future is Now
Host School: Duke
Location: LSRC D106
Host: Sudeepa Roy (sudeepa at cs.duke.edu)

Abstract

Automation, driven by technological progress, has been increasing inexorably for the past several decades. Two schools of economic thinking have for many years been engaged in a debate about the potential effects of automation on jobs: will new technology spawn mass unemployment, as the robots take jobs away from humans? Or will the jobs robots take over create demand for new human jobs?

I will present data that demonstrate that the concerns about automation are valid. In fact, technology has been hurting working Americans for the past 40 years. The discussion about humans, machines and work tends to be a discussion about some undetermined point in the far future. But it is time to face reality. The future is now.

Biography

Moshe Y. Vardi is the George Distinguished Service Professor in Computational Engineering and Director of the Ken Kennedy Institute for Information Technology at Rice University. He is the recipient of three IBM Outstanding Innovation Awards, the ACM SIGACT Goedel Prize, the ACM Kanellakis Award, the ACM SIGMOD Codd Award, the Blaise Pascal Medal, the IEEE Computer Society Goode Award, the EATCS Distinguished Achievements Award, and the Southeastern Universities Research Association’s Distinguished Scientist Award. He is the author and co-author of over 500 papers, as well as two books: “Reasoning about Knowledge” and “Finite Model Theory and Its Applications”. He is a Fellow of the Association for Computing Machinery, the American Association for Artificial Intelligence, the American Association for the Advancement of Science, the European Association for Theoretical Computer Science, the Institute for Electrical and Electronic Engineers, and the Society for Industrial and Applied Mathematics. He is a member of the US National Academy of Engineering and National Academy of Science, the American Academy of Arts and Science, the European Academy of Science, and Academia Europaea. He holds honorary doctorates from the Saarland University in Germany, Orleans University in France, UFRGS in Brazil, and the University of Liege in Belgium. He is currently a Senior Editor of of the Communications of the ACM, after having served for a decade as Editor-in-Chief.

29 January 2018

Avrim BlumSpeaker: Avrim Blum, Professor of Computer Science and Chief Academic Officer, Toyota Technical Institute at Chicago
Title: Learning about Agents and Mechanisms from Opaque Transactions
Host School: Duke
Location: LSRC D106
Host: Kamesh Munagala (kamesh at cs.duke.edu)

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Abstract

In this talk I will discuss the problem of trying to learn the requirements and preferences of economic agents by observing the outcomes of an allocation mechanism whose rules you also don’t initially know.   As an example, consider observing web pages where the agents are advertisers and the winners are those whose ads show up on the given page.  We know these ads are placed based on bids and other constraints given to some auction mechanism, but we do not get to see these bids and constraints. What we would like to do is from repeated observations of this type to learn what the requirements and preferences of the agents are.  Or consider observing the input-output behavior of some scheduling service, where the input consists of a set of agents requesting service, and the output tells us which actually received service and which did not.  In this case, we assume the agents who did not receive service were not served due to overlap of their resource needs with higher-priority requests. From such input-output behavior, we would like to learn the underlying structure.  Our goal will be from observing a series of such interactions to try to learn both the needs and preferences of the agents and perhaps also the rules of the allocation mechanism.

This talk is based on work joint with Yishay Mansour and Jamie Morgenstern, as well as work joint with Michael Liang.

Biography

Avrim Blum received his B.S., M.S., and Ph.D. from MIT in 1987, 1989, and 1991 respectively. He then served on the faculty in the Computer Science Department at Carnegie Mellon University from 1992 to 2017. In 2017 he joined the Toyota Technological Institute at Chicago as Chief Academic Officer.

Prof. Blum’s main research interests are in Theoretical Computer Science and Machine Learning, including Machine Learning Theory, Approximation Algorithms, Algorithmic Game Theory, and Database Privacy, as well as connections among them. Some current specific interests include multi-agent learning, multi-task learning, semi-supervised learning, and the design of incentive systems. He is also known for his past work in AI Planning. Prof. Blum has served as Program Chair for the IEEE Symposium on Foundations of Computer Science (FOCS) and the Conference on Learning Theory (COLT). He has served as Chair of the ACM SIGACT Committee for the Advancement of Theoretical Computer Science and on the SIGACT Executive Committee. Prof. Blum is recipient of the AI Journal Classic Paper Award, the ICML/COLT 10-Year Best Paper Award, the Sloan Fellowship, the NSF National Young Investigator Award, and the Herbert Simon Teaching Award, and he is a Fellow of the ACM.

26 March 2018

Richard SzeliskiSpeaker: Richard Szeliski, Research Scientist and Director of the Computational Photography Group, Facebook Research
Title: Visual Reconstruction and Image-Based Rendering
Host School: Duke
Location: LSRC D106
Host: Xiaobai Sun (xiaobai at cs.duke.edu)

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Abstract

The reconstruction of 3D scenes and their appearance from imagery is one of the longest-standing problems in computer vision. Originally developed to support robotics and artificial intelligence applications, it has found some of its most widespread use in support of interactive 3D scene visualization.

One of the keys to this success has been the melding of 3D geometric and photometric reconstruction with a heavy re-use of the original imagery, which produces more realistic rendering than a pure 3D model-driven approach. In this talk, I give a retrospective of two decades of research in this area, touching on topics such as sparse and dense 3D reconstruction, the fundamental concepts in image-based rendering and computational photography, applications to virtual reality, as well as ongoing research in the areas of layered decompositions and 3D-enabled video stabilization.

Biography

Richard Szeliski is a Research Scientist in the Computational Photography group at Facebook, which he founded in 2015.  He is also an Affiliate Professor at the University of Washington, and is member of the NAE and a Fellow of the ACM and IEEE.  Dr. Szeliski has done pioneering research in the fields of Bayesian methods for computer vision, image-based modeling, image-based rendering, and computational photography, which lie at the intersection of computer vision and computer graphics.  His research on Photo Tourism, Photosynth, and Hyperlapse are exciting examples of the promise of large-scale image and video-based rendering.

Dr. Szeliski received his Ph.D. degree in Computer Science from Carnegie Mellon University, Pittsburgh, in 1988 and joined Facebook as founding Director of the Computational Photography group in 2015.  Prior to Facebook, he worked at Microsoft Research for twenty years, the Cambridge Research Lab of Digital Equipment Corporation for six years, and several other industrial research labs.  He has published over 150 research papers in computer vision, computer graphics, neural nets, and numerical analysis, as well as the books Computer Vision: Algorithms and Applications and Bayesian Modeling of Uncertainty in Low-Level Vision.  He was a Program Committee Chair for CVPR’2013 and ICCV’2003, served as an Associate Editor of the IEEE Transactions on Pattern Analysis and Machine Intelligence and on the Editorial Board of the International Journal of Computer Vision, and as Founding Editor of Foundations and Trends in Computer Graphics and Vision.

9 April 2018

Polina GollandSpeaker: Polina Golland, Director of the Computer Science and Artificial Intelligence Laboratory (CSAIL) and Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology
Title: Image Imputation
Host School: UNC
Location: SN011
Host: Marc Niethammer (mn at cs.unc.edu)

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Abstract

We present an algorithm for creating high resolution anatomically plausible images that are consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large databases of clinical images contain a wealth of information, medical acquisition constraints result in sparse scans that miss much of the anatomy. These characteristics often render computational analysis impractical as standard processing algorithms tend to fail when applied to such images. Our goal is to enable application of existing algorithms that were originally developed for high resolution research scans to severely undersampled images. We introduce a generative model that captures fine-scale anatomical similarity across subjects in clinical image collections and use it to fill in the missing data in scans with large slice spacing. Our experimental results demonstrate the promise of the resulting algorithm in a context of large studies of neurodegeneration and acute stroke.

Biography

Polina Golland is a professor of EECS at MIT CSAIL. She received her Ph.D. from MIT in and her Bachelor and Masters degree from Technion, Israel. Polina’s primary research interest is in developing novel techniques for medical image analysis and understanding. With her students, she has demonstrated novel approaches to image segmentation, shape analysis, functional image analysis and population studies. Polina has served as an associate editor of the IEEE Transactions on Medical Imaging and of the IEEE Transactions on Pattern Analysis and Machine Intelligence and is currently serving on the editorial board of Journal of Medical Image Analysis. She is a Fellow of the International Society for Medical Image Computing and Computer Assisted Interventions.

16 April 2018

Chris ManningSpeaker: Christopher Manning, Thomas M. Siebel Professor in Machine Learning and Professor of Linguistics and of Computer Science, Stanford University
Title: Building Neural Network Models That Can Reason
Host School: UNC
Location: SN011
Host: Mohit Bansal (mbansal at cs.unc.edu)

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Abstract

Deep learning has had enormous success on perceptual tasks but still struggles in providing a model for inference. To address this gap, we have been developing Memory-Attention-Composition networks (MACnets). The MACnet design provides a strong prior for explicitly iterative reasoning, enabling it to support explainable, structured learning, as well as good generalization from a modest amount of data. The model builds on the great success of existing recurrent cells such as LSTMs: A MacNet is a sequence of a single recurrent Memory, Attention, and Composition (MAC) cell. Its careful design imposes structural constraints on the operation of each cell and the interactions between them, incorporating explicit control and soft attention mechanisms into their interfaces. We demonstrate the model’s strength and robustness on the challenging CLEVR dataset for visual reasoning (Johnson et al. 2016), achieving a new state-of-the-art 98.9% accuracy, halving the error rate of the previous best model. More importantly, we show that the new model is more computationally efficient and data-efficient, requiring an order of magnitude less time and/or data to achieve good results. Joint work with Drew Hudson.

Biography

Christopher Manning is the Thomas M. Siebel Professor in Machine Learning, Linguistics and Computer Science at Stanford University. He works on software that can intelligently process, understand, and generate human language material.  He is a leader in applying Deep Learning to Natural Language Processing, including exploring Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, and deep language understanding. He also focuses on computational linguistic approaches to parsing, robust textual inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning is an ACM Fellow, a AAAI Fellow, an ACL Fellow, and a Past President of ACL. He has coauthored leading textbooks on statistical natural language processing and information retrieval. He is the founder of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP software.

23 April 2018

Jeannette WingSpeaker: Jeannette Wing, Avanessians Director of the Data Science Institute and Professor of Computer Science, Columbia University
Title: Data for Good: Data Science at Columbia University
Host School: UNC
Location: SN011
Host: Ketan Mayer-Patel (kmp at cs.unc.edu)

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Abstract

Every field has data. We use data to discover new knowledge, to interpret the world, to make decisions, and even to predict the future. The recent convergence of big data, cloud computing, and novel machine learning algorithms and statistical methods is causing an explosive interest in data science and its applicability to all fields. This convergence has already enabled the automation of some tasks that better human performance. The novel capabilities we derive from data science will drive our cars, treat disease, and keep us safe. At the same time, such capabilities risk leading to biased, inappropriate, or unintended action. The design of data science solutions requires both excellence in the fundamentals of the field and expertise to develop applications which meet human challenges without creating even greater risk.

The Data Science Institute at Columbia University promotes “Data for Good”: using data to address societal challenges and bringing humanistic perspectives as—not after—new science and technology is invented. Started in 2012, the Institute is now a university-level institute representing over 300 affiliated faculty from 12 different schools across campus. Data science literally touches every corner of the university.

In this talk, I will present the mission of the Institute, highlights of our educational and research activities, and plans for future initiatives.

Biography

Jeannette M. Wing is Avanessians Director of the Data Science Institute and Professor of Computer Science at Columbia University. From 2013 to 2017, she was a Corporate Vice President of Microsoft Research. She is Consulting Professor of Computer Science at Carnegie Mellon where she twice served as the Head of the Computer Science Department and had been on the faculty since 1985. From 2007-2010 she was the Assistant Director of the Computer and Information Science and Engineering Directorate at the National Science Foundation. She received her S.B., S.M., and Ph.D. degrees in Computer Science, all from the Massachusetts Institute of Technology.

Professor Wing’s general research interests are in the areas of trustworthy computing, specification and verification, concurrent and distributed systems, programming languages, and software engineering. Her current interests are in the foundations of security and privacy, with a new focus on trustworthy AI. She was or is on the editorial board of twelve journals, including the Journal of the ACM and Communications of the ACM.

She is currently a member of: the National Library of Medicine Blue Ribbon Panel, the Science, Engineering, and Technology Advisory Committee for the American Academy for Arts and Sciences; the Board of Trustees for the Institute of Pure and Applied Mathematics; the Advisory Board for the Association for Women in Mathematics; and the Alibaba DAMO Technical Advisory Board. She has been chair and/or a member of many other academic, government, and industry advisory boards. She received the CRA Distinguished Service Award in 2011 and the ACM Distinguished Service Award in 2014. She is a Fellow of the American Academy of Arts and Sciences, American Association for the Advancement of Science, the Association for Computing Machinery (ACM), and the Institute of Electrical and Electronic Engineers (IEEE).