Select a calendar:
Filter April Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for April
-
CS Colloquium: Ian Miers (Cornell Tech) - Cryptography in context: Bitcoin, breaches, and security in the real world
Mon, Apr 01, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ian Miers, Cornell Tech
Talk Title: Cryptography in context: Bitcoin, breaches, and security in the real world
Series: CS Colloquium
Abstract: This talk will cover the design, implementation, and deployment of new cryptography to solve security issues that arise in real-world applications. Providing security for practically-deployed systems requires a new approach to cryptography, one that begins with the context in which cryptographic protocols will be used and reasons backwards in order to obtain the necessary security properties. This talk will cover two examples of this approach. First, I will take a detailed look at confidentiality for payments and how to solve the privacy failures of blockchain protocols such as Bitcoin. I will detail the design, implementation, and commercial deployment of Zcash, the first system to offer confidentiality while preserving public verifiability for cryptocurrencies. Next, I will explore cryptography in the context of securing data against breaches, considering the reality that attackers will gain access to cryptographic key material --- thus rendering traditional encryption ineffective. I will show how to use new applications of puncturable encryption to address these vulnerabilities for messaging and device encryption.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ian Miers is a postdoctoral researcher at Cornell Tech working on computer security and applied cryptography. His research focuses on making systems secure by exploring cryptography in the context of real world problems. This includes Zerocoin and Zerocash, the first systems to provide strongly confidential payments on top of public blockchains and work improving secure messaging including attacks on Apple's iMessage protocol and new techniques for puncturable forward secure encryption. His work has been featured in The Washington Post, The New York Times, The Economist, and denounced in at least two editorials. He is one of the founders of Zcash, a privacy preserving cryptocurrency based on Zerocash.
Host: Muhammad Naveed
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Colloquium: Xinyu Wang (UT Austin) - A unified program synthesis framework for automating end-user programming tasks
Tue, Apr 02, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Xinyu Wang, UT Austing
Talk Title: A unified program synthesis framework for automating end-user programming tasks
Series: CS Colloquium
Abstract: Programming has started to become an essential skill for an increasing number of people, including novices without formal programming background. As a result, there is an increasing need for technology that can provide basic programming support to such non-expert computer end-users. Program synthesis, as a technique for automatically generating programs from high-level specifications, has been used to automate real-world programming tasks in a number of application domains (such as spreadsheet programming and data science) that non-expert users struggle with. However, developing specialized synthesizers for these domains is notoriously hard.
In this talk, I will describe a unified program synthesis framework that can be applied broadly to automating tasks across different application domains. This framework is also efficient and achieves orders of magnitude improvement in terms of synthesis speed compared to existing techniques. In particular, I have used this framework to build synthesizers for three different application domains and achieved up to 450x speed-up compared to state-of-the-art synthesis techniques.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Xinyu Wang is a PhD candidate at UT Austin advised by Isil Dillig. He works at the intersection of programming languages, software engineering and formal methods. He is interested in developing foundational program synthesis techniques that are applicable to automating real-world programming tasks.
Host: Chao Wang
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
PhD Defense - Shahrzad Gholami
Wed, Apr 03, 2019 @ 10:30 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Shahrzad Gholami
Wed, April 3, 2019
10:30 AM - 12:00 Noon
Location: EEB 132
Title:
Predicting and Planning against Real-world Adversaries: An End-to-end Pipeline to Combat Illegal Wildlife Poachers on a Global Scale
PhD Candidate: Shahrzad Gholami
Date, Time, and Location: Wednesday, April 3, 2019 at 10:30 am in EEB 132
Committee: Prof. Milind Tambe (chair), Prof. Aram Galstyan, and Prof. Emilio Ferrara, Prof. Richard John, Prof. Sze-Chuan Suen
Abstract:
Security is a global concern and a unifying theme in various security projects is strategic reasoning where the mathematical framework of machine learning and game theory can be integrated and applied. For example, in the environmental sustainability domain, the problem of protecting endangered wildlife from attacks (i.e., poachers' strikes) can be abstracted as a game between defender(s) and attacker(s). Applying previous research on security games to sustainability domains (denoted as Green Security Games) introduce several novel challenges that I address in my thesis to create computationally feasible and accurate algorithms in order to model complex adversarial behavior based on the real-world data and to generate optimal defender strategy. My thesis provides four main contributions to the emerging body of research in using machine learning and game theory framework for the fundamental challenges existing in the environmental sustainability domain, namely (i) novel spatio-temporal and uncertainty-aware machine learning models for complex adversarial behavior based on the imperfect real-world data, (ii) the first large-scale field test evaluation of the machine learning models in the adversarial settings concerning the environmental sustainability, (iii) a novel multi-expert online learning model for constrained patrol planning, and (iv) the first game theoretical model to generate optimal defender strategy against collusive adversaries. In regard to the first contribution, I developed bounded rationality models for adversaries based on the real-world data that account for the naturally occurring uncertainty in past attack evidence collected by defenders. To that end, I proposed two novel predictive behavioral models, which I improved progressively. The second major contribution of my thesis is a large-scale field test evaluation of the proposed adversarial behavior model beyond the laboratory. Particularly, my thesis is motivated by the challenges in wildlife poaching, where I directed the defenders (i.e., rangers) to the hotspots of adversaries that they would have missed. During these experiments across multiple vast national parks, several snares and snared animals were detected, and poachers were arrested, potentially more wildlife saved. The algorithm I proposed, that combines machine learning and game-theoretic patrol planning is planned to be deployed at 600 national parks around the world in the near future to combat poaching. The third contribution in my thesis introduces a novel multi-expert online learning model for constrained and randomized patrol planning, which benefits from several expert planners where insufficient or imperfect historical records of past attacks are available to learn adversarial behavior. The final contribution of my thesis is developing an optimal solution against collusive adversaries in security games assuming both rational and boundedly rational adversaries. I conducted human subject experiments on Amazon Mechanical Turk involving 700 human subjects using a web-based game that simulates collusive security games.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
-
CS Colloquium: Mukund Raghothaman (University of Pennsylvania) - Precise Program Reasoning using Probabilistic Methods
Wed, Apr 03, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Mukund Raghothaman, University of Pennsylvania
Talk Title: Precise Program Reasoning using Probabilistic Methods
Series: CS Colloquium
Abstract: The enormous rise in the scale, scope, and complexity of software projects has created a thriving marketplace for program analysis and verification tools. Despite routine adoption by industry, developing such tools remains challenging, and their designers must carefully balance tradeoffs between false alarms, missed bugs, and scalability to large codebases. Furthermore, when tools fail to verify some program property, they only provide coarse estimates of alarm relevance, potential severity, and of the likelihood of being a real bug, thereby limiting their usefulness in software projects with large teams.
I will present a framework that extends contemporary program reasoning systems with rich probabilistic models. These models emerge naturally from the program structure, and probabilistic inference refines the deductive process of the underlying system. In experiments with large programs, such probabilistic graphical representations of program structure enable an order-of-magnitude reduction in false alarm rates and invocations of expensive reasoning engines such as SMT solvers.
To the analysis user, these techniques offer a lens by which to focus their attention on the most important alarms and a uniform method for the tool to interactively generalize from human feedback. To the analysis designer, they offer novel opportunities to leverage data-driven approaches in analysis design. And to researchers, they offer new challenges while performing inference in models of unprecedented size. I will conclude by describing how these ideas promise to underpin the next generation of intelligent programming systems, with applications in diverse areas such as program synthesis, differentiable programming, and fault localization in complex systems.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Mukund Raghothaman is a postdoctoral researcher at the University of Pennsylvania. His research spans the areas of programming languages, software verification, and program synthesis, with the ultimate goal to help programmers create better software with less effort. He previously obtained a Ph.D. in 2017, also from the University of Pennsylvania, where he developed programming abstractions for data stream processing systems.
Host: Jyotirmoy Deshmukh
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Colloquium: Yixin Sun (Princeton University) - Providing secure Internet services with insecure infrastructure
Thu, Apr 04, 2019 @ 09:30 AM - 10:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yixin Sun, Princeton University
Talk Title: Providing secure Internet services with insecure infrastructure
Series: CS Colloquium
Abstract: The insecurity of Internet services can lead to disastrous consequences -“ confidential communications can be monitored, financial information can be stolen, and our critical Internet infrastructure can be crippled. However, many prior works on Internet services only focus on the security of an individual network layer in isolation, whereas the adversaries do quite the opposite -“ they look for opportunities to exploit the interactions across heterogeneous components and layers to compromise the system security. This gap leaves the privacy and security of billions of users as well as our critical infrastructure at risk.
I aim to bridge this gap to build privacy-preserving and secure Internet services. In this talk, I will focus on two Internet services, the Tor network and the Public Key Infrastructure. I have uncovered new vulnerabilities in these services by taking a cross-layer approach to exploit the interdependencies across different network layers. I have demonstrated attacks in the wild (ethically) to evaluate the real effects of vulnerabilities. Consequently, I have built practical defenses that have received real-world deployment by the Tor Project which serves millions of users, and Let's Encrypt which is the world's largest Certificate Authority that has issued hundreds of millions of digital certificates.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Yixin Sun is a PhD candidate in Computer Science at Princeton University. Her research focuses on building privacy-preserving and secure networked systems. She received the Information Controls Fellowship from the Open Technology Fund, the SEAS Award for Excellence from Princeton, and the EECS rising star from MIT. Throughout her career, Yixin has collaborated with many industrial labs and non-profit organizations, such as the Tor Project, Let's Encrypt, Verisign Labs, NEC Labs and International Computer Science Institute (ICSI). Previously, Yixin received her Bachelor's degree in Computer Science and Mathematics from the University of Virginia.
Host: Muhammad Naveed
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Colloquium: Amy Babay (Johns Hopkins University) - Dependable Systems and Networks for a Complex World
Thu, Apr 04, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Amy Babay, Johns Hopkins University
Talk Title: Dependable Systems and Networks for a Complex World
Series: CS Colloquium
Abstract: As our world grows more complex, the expectations we place on the networked systems running our society's infrastructure grow more demanding. In this talk, I will discuss two types of emerging demands and present infrastructure systems we have developed to meet those demands. The first part of the talk will focus on the demanding performance requirements brought by emerging highly interactive applications such as remote robotic manipulation, remote surgery, and collaborative virtual reality. These applications require communication that is both timely and highly reliable, but the Internet natively supports only communication that is either completely reliable with no timeliness guarantees (e.g. TCP) or timely with only best-effort reliability (e.g. UDP). We present an overlay transport service that can provide highly reliable communication while meeting the stringent timeliness requirements of these applications. The second part of the talk will address the demanding security and resilience needs of critical infrastructure services, in particular SCADA systems for the power grid, that are increasingly becoming exposed to malicious attacks. I will present our work building Spire, the first intrusion-tolerant SCADA system for the power grid that is resilient to both system-level compromises and sophisticated network-level attacks.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Amy Babay recently completed her PhD in Computer Science at Johns Hopkins University, where she was a member of the Distributed Systems and Networks Lab. Her research focuses on enabling new Internet services with demanding performance requirements and on building dependable critical infrastructure systems. Prior to starting her PhD, she gained experience with global overlay networks in the commercial world, working at LTN Global Communications. She is currently working to advance some of her research toward commercialization at Spread Concepts LLC.
Host: Ramesh Govindan
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Second International Symposium on Foundations and Applications of Blockchain 2019, FAB'19
Fri, Apr 05, 2019
Thomas Lord Department of Computer Science
Receptions & Special Events
FAB 2019 is a symposium in the emerging area of blockchain technology and its applications. It brings together blockchain researchers and practitioners from academia and industry to share results and exchange experiences. This one-day event is held at the beautiful campus of the University of Southern California.
The symposium features an exciting program of four peer-reviewed papers from premier institutions around the world, keynotes from academia and industry, and a timely panel on the future of blockchain. See https://scfab.github.io/2019/schedule.html for the detailed program.
The registration site for FAB 2019 is now open, visit https://scfab.github.io/2019/registration.html to register.
Please note that in order to attend, you must be registered.Location: Hughes Aircraft Electrical Engineering Center (EEB) -
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Colloquium: Yuke Zhu (Stanford University) - Closing the Perception-Action Loop
Mon, Apr 08, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yuke Zhu, Stanford University
Talk Title: Closing the Perception-Action Loop
Series: CS Colloquium
Abstract: Robots and autonomous systems have been playing a significant role in the modern economy. Custom-built robots have remarkably improved productivity, operational safety, and product quality. However, these robots are usually programmed for specific tasks in well-controlled environments, unable to perform diverse tasks in the real world. In this talk, I will present my work on building more effective and generalizable robot intelligence by closing the perception-action loop. I will discuss my research that establishes a tighter coupling between perception and action at three levels of abstraction: 1) learning primitive motor skills from raw sensory data, 2) sharing knowledge between sequential tasks in visual environments, and 3) learning hierarchical task structures from video demonstrations.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Yuke Zhu is a final year Ph.D. candidate in the Department of Computer Science at Stanford University, advised by Prof. Fei-Fei Li and Prof. Silvio Savarese. His research interests lie at the intersection of robotics, computer vision, and machine learning. His work builds machine learning and perception algorithms for general-purpose robots. He received a Master's degree from Stanford University and dual Bachelor's degrees from Zhejiang University and Simon Fraser University. He also collaborated with research labs including Snap Research, Allen Institute for Artificial Intelligence, and DeepMind.
Host: Joseph Lim
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
Computer Science General Faculty Meeting
Wed, Apr 10, 2019 @ 12:00 AM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
-
CAIS Seminar: Dan Berry (University of Minnesota) - It's Complex: Embracing Dynamic Complexity in Children's Self-Regulation Development
Wed, Apr 10, 2019 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Dan Berry, University of Minnesota
Talk Title: It's Complex: Embracing Dynamic Complexity in Children's Self-Regulation Development
Series: USC Center for Artificial Intelligence in Society (CAIS) Seminar Series
Abstract: Developmental psychologists often invoke the idea that human development reflects "dynamic systems"-”complex, non-linear processes (e.g., physiological, neural, psychological, behavioral) that organize the way we adapt to changing contextual demands. In practice, however, these complexities often serve more as theoretical touchstones than purposeful targets of investigation. In this presentation, Dr. Berry introduces some of the ways that we've begun to leverage the time-series dynamics of visual gaze, behavior, and autonomic physiology as a means of better understanding these complexities in children's self-regulation development.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Daniel Berry is an Assistant Professor at the Institute of Child Development, University of Minnesota. His research concerns the "real-time" and long-term role of context in children's self-regulation development.
Host: Milind Tambe
Location: James H. Zumberge Hall Of Science (ZHS) - 252
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
CS Colloquium: Tyler Sorensen (Princeton University) - Reasoning About Heterogenous Computing
Thu, Apr 11, 2019 @ 09:30 AM - 10:30 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Tyler Sorensen, Princeton University
Talk Title: Reasoning About Heterogenous Computing
Series: CS Colloquium
Abstract: Heterogeneous system designs have allowed computing efficiency to scale past fundamental constraints of transistors. Such systems are now the computation workhorses behind everyday technology, from speech recognition trained on clusters of GPUs, to efficient SoC designs in mobile phones. However, programming for these systems presents many challenges, specifically in orchestrating synchronization. Examining general purpose GPU (GPGPU) programming is a pragmatic start towards general heterogeneous reasoning, as GPGPU programming models expose hardware specialization and heterogeneous-aware constructs. In this talk, I discuss my work in this area, which has identified important areas of under-specification in GPGPU programming and laid the foundations for specification repairs.
First, I will present work on testing memory consistency models, i.e. the rules governing fine-grained communication, for GPGPUs. This work exposed wide-spread confusion in the GPGPU community, including identifying programming errors in two Nvidia-endorsed textbooks. Second, I will present work on GPGPU forward progress models, which defines a progress abstraction that allows cross-vendor GPGPU global barrier synchronization. This can then be used in an optimization for GPGPU graph traversal applications, achieving over a 10x speedup on Intel and AMD GPUs. The talk concludes by showing that GPGPU reasoning is a natural foundation for future work targeting general heterogeneous programming.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Tyler Sorensen is a PostDoc at Princeton University in Professor Margaret Martonosi's architecture group working on designing new heterogeneous systems. He received his PhD from Imperial College London under the supervision of Dr. Alastair Donaldson. His thesis work involved rigorous reasoning about GPGPU programming, with an emphasis on fine-grained synchronization idioms. This work has been published widely (including two distinguished paper awards at PLDI'18 and
FSE'17) and presented to major GPU vendors, including Nvidia, AMD and ARM. Tyler received his MS/BS from University of Utah, where he received the 2014 Outstanding Senior Award. He has done internships at both Microsoft Research and Nvidia.
Host: Jyotirmoy Deshmukh
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Colloquium: Motahhare Eslami (University of Illinois at Urbana-Champaign) - Participating and Designing around Algorithmic Sociotechnical Systems
Thu, Apr 11, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Motahhare Eslami, University of Illinois at Urbana-Champaign
Talk Title: Participating and Designing around Algorithmic Sociotechnical Systems
Series: CS Colloquium
Abstract: Algorithms play a vital role in curating online information in socio-technical systems, however, they are usually housed in black-boxes that limit users' understanding of how an algorithmic decision is made. While this opacity partly stems from protecting intellectual property and preventing malicious users from gaming the system, it is also designed to provide users with seamless, effortless system interactions. However, this opacity can result in misinformed behavior among users, particularly when there is no clear feedback mechanism for users to understand the effects of their own actions on an algorithmic system. The increasing prevalence and power of these opaque algorithms coupled with their sometimes biased and discriminatory decisions raise questions about how knowledgeable users are and should be about the existence, operation and possible impacts of these algorithms. In this talk, I will address these questions by exploring ways to investigate users' behavior around opaque algorithmic systems. I will then present new design techniques that communicate opaque algorithmic processes to users and provide them with a more informed, satisfying, and engaging interaction. In doing so, I will add new angles to the old idea of understanding the interaction between users and automation by designing around algorithm sensemaking and transparency.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Motahhare Eslami is a Ph.D. Candidate in Computer Science at the University of Illinois at Urbana-Champaign, where she is advised by Karrie Karahalios. Motahhare's research develops new communication techniques between users and opaque algorithmic socio-technical systems to provide users a more informed, satisfying, and engaging interaction. Her work has been recognized with a Google PhD Fellowship, Best Paper Award at ACM CHI, and has been covered in mainstream media such as Time, The Washington Post, Huffington Post, the BBC, Fortune, and Quartz. Motahhare is also a Facebook and Adobe PhD fellowship finalist, and a recipient of C.W. Gear Outstanding Graduate Student Award, Saburo Muroga Endowed Fellowship, Feng Chen Memorial Award, Young Researcher in Heidelberg Laureate Forum and Rising Stars in EECS.
Host: Heather Culbertson
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Colloquium: Jeff Clune (University of Wyoming) - Understanding and Improving Deep Neural Networks
Mon, Apr 15, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jeff Clune, University of Wyoming
Talk Title: Understanding and Improving Deep Neural Networks
Series: CS Colloquium
Abstract: Through deep learning, deep neural networks have produced state-of-the-art results in a number of different areas of machine learning, including computer vision, natural language processing, robotics and reinforcement learning. I will summarize three projects on better understanding deep neural networks and improving their performance. First I will describe our sustained effort to study how much deep neural networks know about the images they classify. Our team initially showed that deep neural networks are "easily fooled," meaning they will declare with near certainty that completely unrecognizable images are everyday objects. These results suggested that deep neural networks do not truly understand the objects they classify. However, our subsequent results reveal that, when augmented with powerful priors, deep neural networks actually have a surprisingly deep understanding of objects, which also enables them to be incredibly effective generative models that can produce a wide diversity of photo-realistic images. Second, I will summarize our Nature paper on learning algorithms that enable robots, after being damaged, to adapt in 1-2 minutes in order to continue performing their mission. This work combines a novel stochastic optimization algorithm with Bayesian optimization to produce state-of-the-art robot damage recovery. Third, I will describe our recent Go-Explore algorithm, which dramatically improves the ability of deep reinforcement learning algorithms to solve previously unsolvable problems wherein reward signals are sparse, meaning that intelligent exploration is required. Go-Explore solves Montezuma's Revenge, considered by many to be a grand challenge of AI research. I will also very briefly summarize a few other machine learning projects from my career, including our PNAS paper on automatically identifying, counting, and describing wild animals in images taken remotely by motion-sensor cameras.
Biography: Jeff Clune is the Loy and Edith Harris Associate Professor in Computer Science at the University of Wyoming and a Senior Research Manager and founding member of Uber AI Labs, which was formed after Uber acquired a startup he helped lead. Jeff focuses on robotics and training deep neural networks via deep learning, including deep reinforcement learning. Since 2015, a robotics paper he co-authored was on the cover of Nature, a deep learning paper from his lab was on the cover of the Proceedings of the National Academy of Sciences, he won an NSF CAREER award, his deep learning papers were awarded honors (best paper awards and/or oral presentations) at the top machine learning conferences (NeurIPS, CVPR, ICLR, and ICML), he was an invited speaker at five ICML and two NeurIPS workshops (including the NeurIPS Deep Reinforcement Learning Workshop), and he was invited to give a forthcoming ICML tutorial. His research is regularly covered in the press, including the New York Times, NPR, NBC, Wired, the BBC, the Economist, Science, Nature, National Geographic, the Atlantic, and the New Scientist. Prior to becoming a professor, he was a Research Scientist at Cornell University and received degrees from Michigan State University (PhD, master's) and the University of Michigan (bachelor's).
Host: Yan Liu
Location: Ronald Tutor Hall of Engineering (RTH) - 115
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
CS Colloquium: Owolabi Legunsen (University of Illinois at Urbana-Champaign) - Evolution-Aware Runtime Verification
Tue, Apr 16, 2019 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Owolabi Legunses, University of Illinois at Urbana-Champaign
Talk Title: Evolution-Aware Runtime Verification
Series: CS Colloquium
Abstract: The risk posed by software bugs has increased significantly as software is now essential to many areas of our daily lives. Runtime verification can help find bugs by monitoring program executions against formally specified properties. Over the last two decades, tremendous research progress has improved the performance of runtime verification. However, there has been very little focus on the benefits and challenges of using runtime verification during software testing. Yet, testing generates many executions on which properties can be monitored.
In this talk, I will describe my work on studying and improving runtime verification during testing. My large-scale study was the first to show that runtime verification during testing is beneficial for finding many important bugs from tests that developers already have. However, my study also showed that runtime verification still incurs high overhead, both in machine time to monitor properties and in developer time to inspect violations of the properties. Moreover, all prior runtime verification techniques consider only one program version and would wastefully re-monitor unaffected properties and code as software evolves. To reduce the overhead across multiple program versions, I proposed the first evolution-aware runtime verification techniques. My techniques exploit the key insight that software evolves in small increments and reduce the accumulated runtime verification overhead by up to 10x, without missing new violations.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Owolabi Legunsen is a PhD candidate in Computer Science at the University of Illinois at Urbana-Champaign, where he works with Darko Marinov and Grigore Rosu. Owolabi's interests are in Software Engineering and Applied Formal Methods, with a focus on Software Testing and Runtime Verification. His research goal is to improve software reliability by helping developers find more bugs, find bugs faster, and find bugs more reliably. So far, his techniques and tools helped find more than 450 bugs in over 90 open-source projects. His research on runtime verification during software testing received an ACM SIGSOFT Distinguished Paper Award at ASE 2016. More information is available on his web page: http://mir.cs.illinois.edu/legunsen
Host: Nenad Medvidovic
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
-
MASCLE Machine Learning Seminar: Peter L. Bartlett (University of California, Berkeley) – Optimizing Probability Distributions for Learning: Sampling Meets Optimization
Tue, Apr 16, 2019 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Peter L. Bartlett, University of California, Berkeley
Talk Title: Optimizing Probability Distributions for Learning: Sampling Meets Optimization
Series: Machine Learning Seminar Series
Abstract: Optimization and sampling are both of central importance in large-scale machine learning problems, but they are typically viewed as very different problems. This talk presents recent results that exploit the interplay between them. Viewing Markov chain Monte Carlo sampling algorithms as performing an optimization over the space of probability distributions, we demonstrate analogs of Nesterov's acceleration approach in the sampling domain, in the form of a discretization of an underdamped Langevin diffusion. In the other direction, we view stochastic gradient optimization methods, such as those that are common in deep learning, as sampling algorithms, and study the finite-time convergence of their iterates to an invariant distribution.
Joint work with Xiang Cheng, Niladri S. Chatterji, and Michael Jordan.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Peter Bartlett is a professor in the Computer Science Division and Department of Statistics and Associate Director of the Simons Institute for the Theory of Computing at the University of California at Berkeley. His research interests include machine learning and statistical learning theory. He is the co-author, with Martin Anthony, of the book Neural Network Learning: Theoretical Foundations. He has served as an associate editor of the journals Bernoulli, Mathematics of Operations Research, the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and the IEEE Transactions on Information Theory, and as program committee co-chair for COLT and NIPS. He was awarded the Malcolm McIntosh Prize for Physical Scientist of the Year in Australia in 2001, and was chosen as an Institute of Mathematical Statistics Medallion Lecturer in 2008, an IMS Fellow and Australian Laureate Fellow in 2011, and a Fellow of the ACM in 2018. He was elected to the Australian Academy of Science in 2015.
Host: Yan Liu, USC Machine Learning Center
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
-
Computer Science General Faculty Meeting
Wed, Apr 17, 2019 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
Receptions & Special Events
Bi-Weekly regular faculty meeting for invited full-time Computer Science faculty only. Event details emailed directly to attendees.
Location: Michelson 101
Audiences: Invited Faculty Only
Contact: Assistant to CS chair
-
PhD Defense - Jens Windau
Thu, Apr 18, 2019 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Jens Windau
Thu, April 18, 2019
3:00 PM - 4:30 PM
Location: MCB 102
Title:
Smart Monitoring and Autonomous Situation Classification of Humans and Machines
PhD Candidate: Jens Windau
Date, Time, and Location: Thursday, April 18, 2019 at 3:00 pm in MCB 102
Committee: Prof. Laurent Itti (chair), Prof. Bartlett Mel, and Prof. Hao Li
Abstract:
Emerging wearable and cloud-connected sensor technologies offer new sensor placement options on the human body and machines. This opens new opportunities to explore cyber robotics algorithms (sensors and human motor plant) and smart manufacturing algorithms (sensors and manufacturing equipment). These algorithms process motion sensor data and provide situation awareness for a wide range of applications. Smart management and training systems assist humans in day-to-day living routines, healthcare and sports. Machines benefit from smart monitoring in manufacturing, retail machinery, transportation, and construction safety. During my PhD Research, I have developed several approaches for motion analysis and classification. (1) A situation awareness system (SAS) for head-mounted smartphones to respond to user activities (e.g., disable incoming phone calls in elevators, activate video recording while car driving), (2) a filter for head-mounted sensors (HOS) to allow full-body motion capturing by removing interfering head-motions, (3) an Inertial Machine Monitoring System (IMMS) to detect equipment failure or degraded states of a 3D-Printer, and (4) a "Smart Teaching System" (STS) for targeted motion feedback to refine physical tasks. To capture real-world sensor data, we designed hardware prototypes or used state-of-the-art wearable technology. We developed novel sensor fusion algorithms, implemented feature extraction methods based on gist, statistics, physics, frequency diagrams and validated classifiers: SAS achieved high accuracy (81.5%) when distinguishing between 20 real-world activities. HOS reduced the positional error of a traveled distance below 2.5 % with head-mounted sensors for pedestrian dead reckoning applications. IMMS yielded 11-way classification accuracy over 99% when distinguishing between normal operation vs. 10 types of real-world abnormal equipment behavior. STS demonstrated that combining motion sensors and provide targeted feedback yield significantly improved golf swing training (3.7x increased performance score).
Location: 102
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
-
PhD defense - Yaguang Li
Tue, Apr 23, 2019 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Defense - Yaguang Li
Tue, April 23rd, 2019
1:00 pm - 3:00 pm
Location: PHE 325
Title:
Spatiotemporal Prediction with Deep Learning on Graphs
PhD Candidate: Yaguang Li
Date, Time, and Location: Tuesday, April 23rd, 2019 at 1pm in PHE 325
Committee: Prof. Cyrus Shahabi, Prof. Yan Liu, and Prof. Antonio Ortega
Abstract:
Spatiotemporal data is ubiquitous in our daily life, ranging from climate science, via transportation, social media, to various dynamical systems. The data is usually collected from a set of correlated objects over time, where objects can be sensors, locations, regions, particles, users, etc. For instance, in the transportation network, road sensors constantly record the traffic data at various correlated locations; in social networks, we observe activity data of correlated users, as indicated by friendships, evolving over time, and in dynamical systems, e.g., physics, climate, we observe the movement of particles interacting with each other. Spatiotemporal prediction aims to model the evolution of a set of correlated objects. It has various applications, ranging from classical subjects such as intelligent transportation system, climate science, social media, physics simulation to emerging fields of sustainability, Internet of Things (IoT) and health-care.
Spatiotemporal prediction is challenging mainly due to the complicated spatial dependencies and temporal dynamics. In this thesis, we study the following important questions in spatiotemporal prediction: (1) How to model complex spatial dependency among objects that are usually non-Euclidean and multimodal? (2) How to model the non-linear and non-stationary temporal dynamics for accurate long-term prediction? (3) How to infer the correlations or interactions among objects when they are not provided nor can be constructed a prior?
To model the complex spatial dependency, we represent the non-Euclidean pair-wise correlations among objects using directed graphs and then propose the novel diffusion graph convolution which captures the spatial dependency with bidirectional random walks on the graph. To model the multimodal correlations among objects, we further propose the multi-graph convolution network. To model the non-linear and non-stationary temporal dynamics, we integrate the novel diffusion graph convolution into the recurrent neural network to jointly model the spatial and temporal dependencies. To capture the long-term temporal dependency, we propose to use the sequence to sequence architecture with scheduled sampling. To utilize the global contextual information in the temporal correlation modeling, we further propose the contextual gated recurrent neural network which augments the recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. To infer correlation among objects, we propose a structure-informed variational graph autoencoder based model, which infers the explicit interactions considering both observed movements and structural prior knowledge, e.g., node degree distribution, edge type distribution, and sparsity. The model represents the structural prior knowledge as differentiable constraints on the interaction graph and optimizes it using gradient-based methods.
We conduct extensive experiments on multiple real-world large-scale datasets for various spatiotemporal prediction tasks, including traffic forecasting, spatiotemporal demand forecasting, travel time estimation, relational inference and simulation. The results show the proposed models consistently achieve clear improvements over state-of-the-art methods. The proposed models and their variants have been deployed in real-world large-scale systems for applications including road traffic speed prediction, Internet traffic forecasting, air quality forecasting, travel time estimation, and spatiotemporal demand forecasting.
Location: Charles Lee Powell Hall (PHE) - 325
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
-
CS Distinguished Lecture: Dr. Dan Boneh (Stanford University) – Cryptography for Blockchains
Tue, Apr 23, 2019 @ 04:00 PM - 05:20 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Dan Boneh, Stanford University
Talk Title: Cryptography for Blockchains
Series: Computer Science Distinguished Lecture Series
Abstract: This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Boneh is a Professor of Computer Science at Stanford University where he heads the applied cryptography group and co-directs the computer security lab. Dr. Boneh's research focuses on applications of cryptography to computer security. His work includes cryptosystems with novel properties, web security, cryptography for blockchains, and cryptanalysis. He is the author of over a 150 publications in the field and is a recipient of the 2014 ACM prize, the 2013 Godel prize, the RSA award in mathematics, and six best paper awards. In 2016 Dr. Boneh was elected to the National Academy of Engineering.
Host: Computer Science Department
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Computer Science Department
