CSE Colloquia Series: Dorian Arnoldhttps://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-Dorian-Arnold.aspx800CSE Colloquia Series: Dorian Arnold2017-02-20T06:00:00Z10:30 a.m.Jolley Hall, Room 309<p style="text-align: center;"><strong>The SMURFS Project: Simulation and Modeling for Understanding Resilience and Faults at Scale</strong></p><p style="text-align: center;"><strong>Dorian Arnold </strong></p><p style="text-align: center;">Associate Professor</p><p style="text-align: center;">Department of Computer Science</p><p style="text-align: center;">University of New Mexico</p><p><strong>Abstract</strong></p><p>Current HPC research explorations target computer systems with exaflop (10^18 or a quintillion floating point operations per second) capabilities. Such computational power will enable new, important discoveries across all basic science domains. Application resilience is a major challenge to the realization of extreme scale computing systems. The SMURFS Project addresses this challenge by developing methods to improve our predictive understanding of the complex interactions amongst a given application, a given real or hypothetical hardware and software system environment and a given fault-tolerance strategy at extreme scale. Specifically, SMURFS explores: (1) Advanced simulation and modeling capabilities for studying application resilience at scale; (2) Comprehensive, comparative studies of existing and new fault-tolerance strategies; (3) Detailed understandings of how application features interplay with different fault-tolerance strategies and hardware technologies; and (4) Effective prescriptions to guide application developers, hardware architects and system designers to realize efficient, resilient extreme scale capabilities.</p><p>(This project is a collaboration amongst the University of New Mexico, the University of Tennessee and the Sandia National Labs. It is funded in part by the National Science Foundation.)</p><p> </p><p><strong>Biography</strong></p><p>Dorian Arnold is an associate professor in the Department of Computer Science at the University of New Mexico.  His broad research interests include operating and distributed systems, system software, middleware and run-time systems, online (streaming) data analysis, fault-tolerance and high-performance tools.</p><p>Particularly, he is interested in the performance, scalability and reliability issues that abound in extreme scale computing environments that comprise of hundreds of thousands or even millions of components.  Professor Arnold's research group maintains strong collaborations with the Los Alamos, Livermore, and Sandia National Laboratories and Cray Inc. These collaborations lend the privilege of working world-class scientists and engineers on world-class computing systems. In part due to such collaborations, Professor Arnold's research projects were selected as Top 100 R&D technologies in 1999 and 2011.</p><p>Arnold is very active in the HPC community and has held many leadership roles in major HPC conferences and is currently on the SC steering committee.  He is also very dedicated to diversity and inclusion in computer science and serves as the General Chair for the 2017 Tapia Conference. He is an Associate Editor of the IEEE Transactions on Parallel and Distributed Systems and was recently appointed as an ACM Distinguished Speaker.</p><p>Arnold holds a Ph.D. in Computer Science from the University of Wisconsin, an M.S. in Computer Science from the University of Tennessee and a B.S. in Mathematics and Computer Science from Regis University (Denver, CO).</p>
CSE Colloquia Series: Sofya Raskhodnikovahttps://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-Sofya-Raskhodnikova.aspx760CSE Colloquia Series: Sofya Raskhodnikova2017-02-24T06:00:00Z11 a.m.Lopata Hall, Room 101<p>​</p><p style="text-align: center;"><strong>Sublinear-Time Algorithms</strong> </p><p style="text-align: center;"><strong>Sofya Raskhodnikova</strong></p><p style="text-align: center;">Associate Professor</p><p style="text-align: center;">Department of Computer Science and Engineering</p><p style="text-align: center;">Penn State</p><p><strong>Abstract</strong></p><p>Massive datasets are becoming increasingly common. What useful computations can be performed on a dataset when reading all of it is prohibitively expensive? This question, fundamental to several fields, is at the heart of the research area, called sublinear-time algorithms, that has provided important insights into fast approximate computation.</p><p>In this talk, we will consider types of computational tasks central to sublinear-time algorithms:  testing, learning, and approximation. We will see examples of sublinear-time algorithms in several domains. The algorithms themselves are typically simple and efficient, but their analysis requires insights into basic combinatorial, algebraic, and geometric questions. We will also discuss new directions in sublinear-time algorithms, including new computational tasks, new measures for accuracy guarantees, and new models for data access. These directions enable applications of sublinear-time algorithms in privacy, analysis of real-valued data, and situations where the data is noisy or incomplete.</p><p><strong>Biography</strong></p><p>Sofya Raskhodnikova is an associate professor of Computer Science and Engineering at Penn State. She received her Ph.D. from MIT. Prior to joining Penn State in 2007, she was a postdoctoral fellow at the Hebrew University of Jerusalem and the Weizmann Institute of Science. She has held visiting positions at the Institute for Pure and Applied Mathematics at UCLA, Boston University, and Harvard University. She is a recipient of the NSF CAREER award.</p><p>Dr. Raskhodnikova works in the areas of randomized and approximation algorithms. Her main interest is the design and analysis of sublinear-time algorithms for combinatorial problems. She has also made important contributions to data privacy.<br/></p>
CSE Colloquia Series: Brian Kocoloskihttps://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-Brian-Kocoloski.aspx799CSE Colloquia Series: Brian Kocoloski2017-02-27T06:00:00Z10:30 a.m.Jolley Hall, Room 309<p>​</p><p style="text-align: center;"><strong>Scalability in the Presence of Variability</strong></p><p style="text-align: center;"><strong>Brian Kocoloski </strong></p><p style="text-align: center;">Ph.D. Candidate</p><p style="text-align: center;">Department of Electrical Engineering</p><p style="text-align: center;">University of Southern California</p><p><strong>Abstract</strong></p><p>High performance computing (HPC) systems, which will soon consist of over one billion aggregate processing elements (e.g., cores), are poised to meet the demands of an ever growing set of domains, including scientific computing, graph processing, and machine learning. To cull the benefits of large parallel systems, applications in these domains must often globally synchronize across many or all computational elements. This talk will focus on performance variability, where non-uniform parallel progress generates "stragglers" that delay synchronization and thus reduce the scalability of applications.</p><p>I will first focus on the operating systems (OS) in large scale HPC systems. I will discuss how conventional general purpose OSes based on Linux, which are ubiquitous in large scale systems, often limit scalability with operations that generate variability. I will present research in "multi-stack" OSes that allow for dynamic runtime reconfiguration of the OS to eliminate sources of OS variability, thereby improving the scalability of tightly synchronized applications.</p><p>I will then discuss additional challenges that variability poses for emerging HPC environments, and will motivate my vision for "variability tolerant" parallelism. I will identify key questions and opportunities related to the design of variability tolerance. Finally, I will motivate the use of scalable optimization techniques, based on distributed modeling and prediction, to design adaptive, variability tolerant software.</p><p><strong>Biography</strong></p><p>Brian Kocoloski is a Ph.D. candidate in the Department of Computer Science at the University of Pittsburgh. He received his B.S in Computer Science at the University of Dayton in 2011. He spent the summer of 2013 as an intern in the Scalable System Software group at Sandia National Laboratories, and the summer of 2015 as an intern at AMD Research.</p><p>The theme of his research is to make it easier to efficiently utilize large parallel computers. He has designed operating systems and virtualization techniques to provide specialized, low-overhead environments for tightly synchronized parallel applications. His work is currently being leveraged in Hobbes, a US Department of Energy operating system for future exascale computers. He is also interested in distributed optimization techniques, particularly as they pertain to parallel runtimes in large scale systems.</p>
CSE Colloquia Series-Steven Wuhttps://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-Steven-Wu.aspx825CSE Colloquia Series-Steven Wu2017-03-03T06:00:00Z11:00 AMLopata Hall, Room 101<p style="text-align: center;">​<strong style="text-align: center;">Protecting People from Algorithms (and Vice Versa)</strong></p><p style="text-align: center;"><strong>Steven Wu</strong></p><p style="text-align: center;">Ph.D. Candidate</p><p style="text-align: center;">Department of Computer Science</p><p style="text-align: center;">University of Pennsylvania</p><p><strong>Abstract</strong></p><p>Computing technologies today have made it much easier to gather personal data. Algorithms are constantly analyzing such personal information and making consequential decisions on people. The extensive use of algorithms can impose the risks of algorithms mistreating people such as privacy violation or unfair discrimination. There is also a risk of people mistreating algorithms. For example, in a strategic environment people may have incentives to misreport their data to game the algorithms for their own benefits.</p><p>In this talk, I will first present an overarching theme in my research---protecting people and algorithms from each other. In particular, my work seeks to (1) protect people from algorithms in developing algorithms with privacy and fairness guarantees and (2) aims to protect algorithms from people in providing algorithms that incentivize people's truthful behavior.  </p><p>I will then present two technical results in my work on differential privacy, a rigorous algorithmic notion for data privacy.  The first result focuses on a fundamental problem in differential privacy---private query release.  I will present a scalable algorithm that can accurately and privately answer a large collection of counting queries for high-dimensional data. In the second result, I will focus on a general framework for solving a family of economic optimization problems under a strong relaxation of differential privacy.  I will also demonstrate how differential privacy can be used as a novel tool to incentivize truth-telling when the algorithms need to elicit input data from self-interested participants.</p><p><strong>Biography</strong></p><p>Steven is currently a PhD candidate in computer science at the University of Pennsylvania, where he is co-advised by Michael Kearns and Aaron Roth. His primary research interests are in developing theory and algorithms for privacy-preserving data analysis. He has also been studying machine learning in economic environments, where he designs algorithms that learn from observed economic behavior and create desired incentives for the participants. His more recent research focuses on fairness in machine learning, where the goal is to provide fair decision-making algorithms that learn from personal data.</p><p>During the summer of 2015 and the spring of 2016, he was a research intern at Microsoft Research in New York City (MSR-NYC), and during the summer of 2016, he was also a research intern at Microsoft Research New England (MSR-NE).</p>
CSE Colloquia Series-William Yeohhttps://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-William-Yeoh.aspx817CSE Colloquia Series-William Yeoh2017-03-06T06:00:00Z10:30 AMJolley Hall, Room 309<p style="text-align: center;"><strong>Distributed Constraint Optimization: Model, Algorithms, and Applications</strong></p><p style="text-align: center;"><strong>William Yeoh </strong></p><p style="text-align: center;">Assistant Professor</p><p style="text-align: center;">Department of Computer Science</p><p style="text-align: center;">New Mexico State University</p><p><strong>Abstract</strong></p><p>A Distributed Constraint Optimization Problem (DCOP) is a problem where several agents coordinate with each other to take on values so as to minimize the sum of the resulting constraint costs, which are dependent on the values of the agents. DCOPs are rapidly becoming popular for formulating and solving multi-agent coordination problems such as the distributed coordination of sensors in a network and the distributed scheduling of meetings. </p><p>In this talk, I will first describe the formulation for a DCOP and the motivation for using DCOPs in multi-agent coordination problems. I will then provide a brief overview on the leading approaches to solve DCOPs as well as describe some of our recent extensions of the DCOP model and algorithms. Finally, I will wrap up the talk by briefly describing our recent work on applying DCOP algorithms to solve the problem of scheduling smart/IoT devices in smart homes.</p><p><strong>Biography</strong></p><p>William Yeoh is an assistant professor of computer science at New Mexico State University. He received his Ph.D. in computer science at the University of Southern California. His research interests include multi-agent systems, distributed constraint reasoning, heuristic search, and planning with uncertainty. He is an NSF CAREER awardee and was named in IEEE's AI's 10-to-Watch list in 2015. He currently serves on the editorial board of the Journal of Artificial Intelligence Research.</p>
CSE Colloquia Series-Kangjie Luhttps://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-Kangjie-Lu.aspx818CSE Colloquia Series-Kangjie Lu2017-03-10T06:00:00Z11:00 AMLopata Hall, Room 101<p style="text-align: center;"><strong>Defeating Advanced Memory-Error Exploits by Preventing Information Leaks</strong></p><p style="text-align: center;"><strong>Kangjie Lu</strong></p><p style="text-align: center;">Ph.D. Candidate</p><p style="text-align: center;">School of Computer Science</p><p style="text-align: center;">Georgia Institute of Technology</p><p><strong>Abstract</strong></p><p>Widely used systems such as operating systems (OS) are implemented in unsafe programming languages for efficiency. Hence, these foundational systems inherently suffer from a variety of memory errors, and the exploitation of memory errors has become a critical attack vector. The past several years have continuously witnessed critical attacks targeting systems belonging to individuals, enterprises, and government agencies. Two typical goals of these attacks are to leak sensitive data and to control victim systems.</p><p>In this talk, I will first present that since modern systems widely deploy memory-layout randomization techniques, leaking a randomized code pointer has become a prerequisite for advanced control attacks such as code-reuse attacks. Therefore, preventing information leaks can be a general defense that not only stops data leaks but also defeats control attacks. Then, I will present two systems I developed, UniSan and ASLR-Guard.  Specifically, UniSan completely eliminates information leaks caused by reading uninitialized variables (the most common cause) in OS kernels, which has triggered extensive discussions in the Linux and GCC development communities, and resulted in many updates in the Linux kernel, the Android kernel, and the GCC compiler. Similarly, to defeat code-reuse attacks, which always require leaking a code pointer in modern systems, ASLR-Guard either prevents code-pointer leaks or renders the leaks useless in deriving the value of code pointers. While automatically and reliably securing complex systems such as OS kernels and web servers, both UniSan and ASLR-Guard impose negligible performance overhead.</p><p><strong>Biography</strong></p><p>Kangjie Lu is a Ph.D. candidate in Computer Science at the Georgia Institute of Technology. His research interests include security and privacy, programming languages, and operating systems. He is particularly interested in automatically uncovering and addressing fundamental security problems, and securing widely used systems while preserving their reliability and efficiency. In addition to papers published in top-tier security conferences (CCS, NDSS, and USENIX Security), his research has resulted in many important updates in the Linux kernel, the Android OS, and Apple's iOS. During his Ph.D. study, he worked as an intern at NEC Labs America and Samsung Research America, and as a visiting scholar at the Max Planck Institute for Software Systems (MPI-SWS).</p>
CSE Colloquia Series: Ang Chenhttps://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-Ang-Chen.aspx802CSE Colloquia Series: Ang Chen2017-03-13T05:00:00Z10:30 a.m.Jolley Hall, Room 309<p style="text-align: center;"><strong>Secure Diagnostics and Forensics with Network Provenance</strong></p><p style="text-align: center;"><strong>Ang Chen</strong></p><p style="text-align: center;">Ph.D. Candidate</p><p style="text-align: center;">Department of Computer and Information Science</p><p style="text-align: center;">University of Pennsylvania</p><p><strong>Abstract</strong></p><p>Distributed systems are behind many important services that we use every day, such as online banking, social media, and video conferencing. However, in a large-scale distributed system, many things can go wrong: routers can be misconfigured, programs can be buggy, and computers can be compromised by an attacker. To investigate these problems, system administrators need to play the role of 'part-time detectives'. Their tasks would be much easier if there were a way for them to ask the system to explain certain events, such as 'Why was this particular route chosen?'.</p><p>My work leverages data provenance - a concept from the database community - to enable distributed systems to offer such explanations. At a high level, provenance tracks causality between network states and events, and produces a detailed, structured explanation of any event of interest. Such information can be a helpful starting point when investigating a variety of problems, ranging from benign misconfigurations to malicious attacks.</p><p>In this talk, I will present one technique in detail that can accurately pinpoint the root causes of problems by comparing the provenance of 'correct' and 'incorrect' events. I will then give an overview of my other work on network provenance, including an extension of provenance to repair network programs, and an application of secure provenance to the Internet's data plane.</p><p><strong>Biography</strong></p><p>Ang Chen is a fifth-year Ph.D. student in the Department of Computer and Information Science at the University of Pennsylvania, advised by Professor Andreas Haeberlen. His research interests are distributed systems, networking, and security. Besides network provenance, he has also worked on systems and network security, including projects on detecting covert timing channels, mitigating attacks in cyber-physical systems, and defending against DDoS attacks.</p>
Groundbreaking Ceremony: East Danforth Campus Transformationhttps://engineering.wustl.edu/Events/Pages/Jubel-Hall-Groundbreaking.aspx524Groundbreaking Ceremony: East Danforth Campus Transformation2017-05-05T05:00:00Z4 p.m.<ul><li>Anabeth and John Weil Hall<br/></li><li>Henry A. and Elvira H. Jubel Hall (Engineering)<br/></li><li>Gary M. Sumers Welcome Center<br/></li><li>James M. McKelvey, Sr. Hall (Engineering)<br/></li><li>Mildred Lane Kemper Art Museum Expansion<br/></li><li>Ann and Andrew Tisch Park<br/></li></ul>
Commencement: Engineering Student Recognition Ceremonyhttps://engineering.wustl.edu/Events/Pages/Engineering-Student-Recognition-Ceremony.aspx567Commencement: Engineering Student Recognition Ceremony2017-05-18T05:00:00Z1:30 p.m.Field House, Athletic Complex<p>Students (undergraduate, graduate and PhD) should arrive for lineup before 12:45 p.m. in the lower level hallway. Upon arrival, students should obtain a name card and complete the information requested on the back. <span style="line-height: 25.6px;">Students should carry (not wear) hoods.</span><br/></p><p><strong>Reception locations following the ceremony:</strong></p><ul><li><strong>Biomedical Engineering: </strong>Whitaker Hall</li><li><strong>Computer Science & Engineering:</strong> Sever Plaza</li><li><strong>Electrical & Systems Engineering:</strong> Green Hall </li><li><strong>Energy, Environmental & Chemical Engineering:</strong> Brauer Hall </li><li><strong>Mechanical Engineering & Materials Science:</strong> Lopata Hall</li></ul>Kim Shilling, (314) 935-6100
Commencementhttps://engineering.wustl.edu/Events/Pages/All-University-Commencement-2017.aspx568Commencement2017-05-19T05:00:00Z8:30 a.m.Brookings Quadrangle<p>Engineering students lineup along Louderman Hall. <span style="line-height: inherit;">PhD stude</span><span style="line-height: inherit;">nts assemble with the Graduate School next to Wilson Hall.</span>​</p>