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Conferences, Lectures, & Seminars
Events for January
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CS Colloquium: David Rosenblum (National University of Singapore) - Uncertainty in Computer Systems: Problems and Results
Fri, Jan 13, 2017 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: David Rosenblum, National University of Singapore
Talk Title: Uncertainty in Computer Systems: Problems and Results
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
For the past several years, my research has spanned problems in diverse areas including probabilistic verification, software testing, ubiquitous computing and the Internet of Things. A common theme of this research has been the need to deal with various forms of uncertainty, which is an increasingly important concern in the design of modern computer systems. In this talk I will describe recent research on perturbation analysis for probabilistic model checking, which provides systematic ways of computing the effect that uncertainty in the probability parameters of stochastic models has on verification results. Time permitting, I will also discuss recent research on the use of contextual bandit algorithms to deal with uncertainty in service composition for the Internet of Things, and research on how uncertainty in software testing can mask the existence of software faults.
Biography: David S. Rosenblum is Provost's Chair Professor of Computer Science at the National University of Singapore (NUS). He holds a Ph.D. from Stanford University and joined NUS in April 2011 after holding positions as Member of the Technical Staff at AT&T Bell Laboratories (Murray Hill); Associate Professor at the University of California, Irvine; Principal Architect and Chief Technology Officer of PreCache (a technology startup funded by Sony Music); and Professor of Software Systems at University College London. David is a Fellow of the ACM and IEEE, and he serves as Editor-in-Chief of the ACM Transactions on Software Engineering and Methodology (ACM TOSEM). He was previously Chair of the ACM Special Interest Group in Software Engineering (ACM SIGSOFT). He has received two "test-of-time" awards for his research papers, including the ICSE 2002 Most Influential Paper Award for his ICSE 1992 paper on assertion checking, and the first ACM SIGSOFT Impact Paper Award in 2008 for his ESEC/FSE 1997 on Internet-scale event observation and notification (co-authored with Alexander L. Wolf).
Host: CS Department
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 116
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Sungjin Ahn (University of Montreal) -Recent Advances and the Future of Recurrent Neural Networks
Tue, Jan 17, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Sungjin Ahn, University of Montreal
Talk Title: Recent Advances and the Future of Recurrent Neural Networks
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Although the recent resurgence of Recurrent Neural Networks (RNN) has achieved remarkable advances in sequence modeling, we are still missing many abilities of RNN necessary to model more challenging yet important natural phenomena. In this talk, I introduce some recent advances in this direction, focusing on two new RNN architectures: the Hierarchical Multiscale Recurrent Neural Networks (HM-RNN) and the Neural Knowledge Language Model (NKLM). In the HM-RNN, each layer in a multi-layered RNN learns different time-scales, adaptively to the inputs from the lower layer. The NKLM deals with the problem of incorporating factual knowledge provided by knowledge graph into RNNs. I argue the advantages of these models and then conclude the talk with a discussion on the key challenges that lie ahead.
Biography: Sungjin Ahn is currently a postdoctoral researcher at the University of Montreal, working with Prof. Yoshua Bengio on deep learning and its applications. He received his Ph.D. in Computer Science at the University of California, Irvine, under the supervision of Prof. Max Welling. During his Ph.D. program, He co-developed the Stochastic Gradient MCMC algorithms and awarded two best paper awards from the International Conference on Machine Learning in 2012 and the ParLearning 2016, respectively. His research interests include deep learning (on recurrent neural networks, deep generative models), approximate Bayesian inference, and reinforcement learning.
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Distinguished Lecture: Vitaly Shmatikov (Cornell) - Machine Learning and Privacy: Friends or Foes?
Thu, Jan 19, 2017 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Vitaly Shmatikov, Cornell University
Talk Title: Machine Learning and Privacy: Friends or Foes?
Series: CS Distinguished Lectures
Abstract: Recent advances in machine learning provide powerful new tools and juicy new targets for data privacy research. I will first show how to use machine learning against systems that partially encrypt data in storage while computing over it. Then, I will turn machine learning against itself, to extract sensitive training data from machine-learning models --- including black-box models constructed using Google's and Amazon's "learning-as-a-service" platforms. I will conclude with open research questions at the junction of machine learning and privacy.
Biography: Vitaly Shmatikov is a professor at Cornell Tech, where he works on computer security and privacy. He most recently served as the program chair of the IEEE Symposium on Security and Privacy ("Oakland").
Host: Aleksandra Korolova
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium and CAIS Seminar: Eva K Lee (GATECH) - System interoperability & Machine Learning: Multi-site Evidence-based Best Practice Discovery
Fri, Jan 20, 2017 @ 11:00 AM - 11:50 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Eva K Lee, Georgia Institute of Technology
Talk Title: System interoperability & Machine Learning: Multi-site Evidence-based Best Practice Discovery
Series: Center for AI in Society (CAIS) Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
This study establishes interoperability among electronic medical records from 737 healthcare sites and performs machine learning for best practice discovery. A mapping algorithm is designed to disambiguate free text entries and to provide a unique and unified way to link content to structured medical concepts despite the extreme variations that can occur during clinical diagnosis documentation. Redundancy is reduced through concept mapping. A SNOMED-CT graph database is created to allow for rapid data access and queries. These integrated data can be accessed through a secured web-based portal. A classification model ((DAMIP) is then designed to uncover discriminatory characteristics that can predict the quality of treatment outcome. We demonstrate system usability by analyzing Type II diabetic patients. DAMIP establishes a classification rule on a training set which results in greater than 80% blind predictive accuracy on an independent set of patients. By including features obtained from structured concept mapping, the predictive accuracy is improved to over 88%. The results facilitate evidence-based treatment and optimization of site performance through best practice dissemination and knowledge transfer. This project receives the 2016 NSF Health Organization Transformation award.
Biography: Dr. Lee is a Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology, and Director of the Center for Operations Research in Medicine and HealthCare, a center established through funds from the National Science Foundation and the Whitaker Foundation. The center focuses on biomedicine, public health, and defense, advancing domains from basic science to translational medical research; intelligent, quality, and cost-effective delivery; and medical preparedness and protection of critical infrastructures. She is a Distinguished Scholar in Health Systems, Health System Institute at Georgia Tech and Emory University. She is also the Co-Director of the Center for Health Organization Transformation, an NSF Industry/University Cooperative Research Center. Lee partners with hospital leaders to develop novel transformational strategies in delivery, quality, safety, operations efficiency, information management, change management and organizational learning. Lee's research focuses on mathematical programming, information technology, and computational algorithms for risk assessment, decision making, predictive analytics and knowledge discovery, and systems optimization. She has made major contributions in advances to medical care and procedures, emergency response and medical preparedness, healthcare operations, and business operations transformation.
Dr. Lee serves on the National Preparedness and Response Science Board. She is the principle investigator of an online interoperable information exchange and decision support system for mass dispensing, emergency response, and casualty mitigation. The system integrates disease spread modeling with response processes and human behavior; and offers efficiency and quality assurance in operations and logistics performance. It currently has over 9500+ public health site users. Lee has also performed field work within the U.S. on mass dispensing design and evaluation, and has worked with local emergency responders and affected populations after Hurricane Katrina, the Haiti earthquake, the Fukushima Japan radiological disaster, and Hurricane Sandy. Lee has received multiple analytics and practice excellence awards including INFORMS Franz Edelman award, Daniel H Wagner prize for novel cancer therapeutics, bioterrorism emergency response dispensing for mass casualty mitigation, optimizing and transforming clinical workflow and patient care, vaccine immunity prediction, and reducing hospital acquired conditions. Dr. Lee is an INFORMS Fellow. She has received seven patents on innovative medical systems and devices. A brief glimpse of Dr. Lee's healthcare work can be found in the following link: http://www2.isye.gatech.edu/~evakylee/Eva_Lee_Intl_Innovation_139_Research_Media_HR.pdf
Host: Milind Tambe
Location: Seeley G. Mudd Building (SGM) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Reza Shokri (Cornell) - Data Privacy: How to Survive the Inference Avalanche
Mon, Jan 23, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Reza Shokri , Cornell University
Talk Title: Data Privacy: How to Survive the Inference Avalanche
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Underestimating the power of inference attacks is the major reason why data privacy mechanisms fail. In this talk, I will describe my general approach to quantifying privacy and illustrate its applications by showing how to rigorously measure privacy risks of location data and machine-learning models. I will then discuss my current research at the junction of privacy and data science in two important practical scenarios: generating privacy-preserving synthetic data and building accurate deep-learning models that respect privacy of the training data.
Biography: Reza Shokri is a postdoctoral researcher at Cornell Tech. His research focuses on quantitative analysis of privacy, as well as design and implementation of privacy technologies for practical applications. His work on quantifying location privacy was recognized as a runner-up for the Award for Outstanding Research in Privacy Enhancing Technologies (PET Award). Recently, he has focused on privacy-preserving generative models and privacy in machine learning. He received his PhD from EPFL.
Host: Aleksandra Korolova
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Ruslan Salakhutdinov (Carnegie Mellon) - Learning Deep Unsupervised and Multimodal Models
Tue, Jan 24, 2017 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ruslan Salakhutdinov, Carnegie Mellon
Talk Title: Learning Deep Unsupervised and Multimodal Models
Series: NVIDIA Distinguished Lecture Series in Machine Learning
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
In this talk I will first introduce a broad class of unsupervised deep learning models and show that they can learn useful hierarchical representations from large volumes of high-dimensional data with applications in information retrieval, object recognition, and speech perception. I will next introduce deep models that are capable of extracting a unified representation that fuses together multiple data modalities and present the Reverse Annealed Importance Sampling Estimator (RAISE) for evaluating these deep generative models. Finally, I will discuss models that can generate natural language descriptions (captions) of images and generate images from captions using attention, as well as introduce multiplicative and fine-grained gating mechanisms with application to reading comprehension.
Part of NVIDIA Distinguished Lecture Series in Machine Learning.
Biography: Ruslan Salakhutdinov received his PhD in computer science from the University of Toronto in 2009. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an Assistant Professor in the Departments of Statistics and Computer Science. In 2016 he joined the Machine Learning Department at Carnegie Mellon University as an Associate Professor. Ruslan's primary interests lie in deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research and served on the senior programme committee of several learning conferences including NIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, a recipient of the Early Researcher Award, Google Faculty Award, Nvidia's Pioneers of AI award, and is a Senior Fellow of the Canadian Institute for Advanced Research.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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George A. Bekey Distinguished Lecture with Professor Moshe Y. Vardi (Rice University)
Thu, Jan 26, 2017 @ 04:00 PM - 05:30 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Moshe Y. Vardi, Rice University
Talk Title: Humans, Machines, and Work: The Future is Now
Series: CS Keynote Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
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, and UFRGS in Brazil. He is the Editor-in-Chief of the Communications of the ACM.
Host: CS Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Distinguished Lecture with Professor Moshe Y. Vardi (Rice University) - The Automated-Reasoning Revolution: From Theory to Practice and Back
Fri, Jan 27, 2017 @ 11:00 AM - 11:50 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Moshe Y. Vardi, Rice University
Talk Title: The Automated-Reasoning Revolution: From Theory to Practice and Back
Series: CS Keynote Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
For the past 40 years computer scientists generally believed that NP-complete problems are intractable. In particular, Boolean satisfiability (SAT), as a paradigmatic automated-reasoning problem, has been considered to be intractable. Over the past 20 years, however, there has been a quiet, but dramatic, revolution, and very large SAT instances are now being solved routinely as part of software and hardware design.
In this talk I will review this amazing development and show how automated reasoning is now an industrial reality.
I will then describe how we can leverage SAT solving to accomplish other automated-reasoning tasks. Counting the the number of satisfying truth assignments of a given Boolean formula or sampling such assignments uniformly at random are fundamental computational problems in computer science with applications in software testing, software synthesis, machine learning, personalized learning, and more. While the theory of these problems has been thoroughly investigated since the 1980s, approximation algorithms developed by theoreticians do not scale up to industrial-sized instances. Algorithms used by the industry offer better scalability, but give up certain correctness guarantees to achieve scalability. We describe a novel approach, based on universal hashing and Satisfiability Modulo Theory, that scales to formulas with hundreds of thousands of variables without giving up correctness guarantees.
The talk is accessible to a general CS audience.
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, and UFRGS in Brazil. He is the Editor-in-Chief of the Communications of the ACM.
Host: CS Department
Location: May Ormerod Harris Hall, Quinn Wing & Fisher Gallery (HAR) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Nadia Polikarpova (MIT CSAIL) - Type-Driven Program Synthesis
Mon, Jan 30, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nadia Polikarpova, MIT CSAIL
Talk Title: Type-Driven Program Synthesis
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
Modern programming languages safeguard developers from many classes of common errors, yet more subtle errors-”such as violations of security policies-”still plague software. Program synthesis has the potential to eliminate such errors, by generating executable code from concise and intuitive high-level specifications. The major obstacle to practical program synthesis is in navigating large search spaces to find programs that satisfy a given specification. My work addresses this problem through the design of synthesis-friendly type systems that are able to decompose a synthesis problem into smaller problems and efficiently navigate the search space.
Based on this principle I developed Synquid, a synthesizer that generates programs from type-based specifications. Synquid is the first synthesizer to automatically discover provably correct implementations of sorting algorithms, as well as balancing and insertion operations on Red-Black Trees and AVL Trees. For these programs, the required specifications are up to seven times more concise than the implementations, and the synthesis times range from fractions of a second (for insertion sort) to under a minute (for Red-Black Tree rotations). Going beyond textbook algorithms, I creates a language called Lifty, which uses type-driven synthesis to automatically rewrite programs that violate information flow policies.
In our case study, Lifty was able to enforce all required policies in a prototype conference management system.
Biography: Nadia Polikarpova is a postdoctoral researcher at the MIT Computer Science and Artificial Intelligence Lab, interested in helping programmers build secure and reliable software. She completed her PhD at ETH Zurich. For her dissertation she developed tools and techniques for automated formal verification of object-oriented libraries, and created the first fully verified general-purpose container library, receiving the Best Paper Award at the International Symposium on Formal Methods. During her doctoral studies, Nadia was an intern at MSR Redmond, where she worked on verifying real-world implementations of security protocols. At MIT, Nadia has been applying formal verification to automate various critical and error-prone programming tasks.
Host: Chao Wang
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Barath Raghavan (UC Berkeley) -Frontier Networks: Context, Challenges, and Connectivity
Tue, Jan 31, 2017 @ 11:00 AM - 12:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Barath Raghavan, UC Berkeley
Talk Title: Frontier Networks: Context, Challenges, and Connectivity
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
In this talk, I discuss Frontier Networks -- networks that expand Internet access to disconnected regions -- and their role in scaling Internet access to the half of the global population that remains offline. I describe how a deep understanding of the context and challenges reveals new approaches to building more reliable, cost-effective, and manageable Frontier Networks. I describe a project I led to build such a network in a previously-disconnected region of Mendocino County, CA, the lessons it taught us about network design and operation, and the systems we built to address the needs that were unmet. I then describe three subsequent challenges -- bootstrapping, planning, and routing -- and describe ongoing projects to address them.
Biography: Barath Raghavan is a senior researcher at the International Computer Science Institute in Berkeley, CA. His research interests include networked systems, security and applied cryptography, ICTD, and sustainable computing. His work spans a wide range of topics including congestion control, routing security, Internet architecture, software-defined networking, rural Internet access, network function virtualization, network troubleshooting and testing, anonymity systems, and computing for sustainable agriculture. He received his PhD from UC San Diego in 2009 and his BS from UC Berkeley in 2002. He has received a number of paper awards including from ACM SIGCOMM and ACM DEV.
Host: CS Department
Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
