Cybersecurity Graduate Programs Information Session Graduate Programs Information Session2018-02-20T06:00:00Z4 p.m.6 p.m.Cortex Innovation District, 4240 Duncan, Suite 110<p>This open house style information session will be held at Cortex and is a great opportunity to:<br/></p><p></p><ul><li>Learn more about the <a href="/Programs/Pages/cybersecurity.aspx">cybersecurity programs</a><br/></li><li>Meet the program directors<br/></li><li>Learn how to apply <br/></li></ul><p></p><p>You may find more information on location and parking at Cortex through the <a href="" target="_blank" rel="noopener noreferrer">Location and Transportation website</a> for Cortex.<br/><br/><strong>Unable to attend or have questions? </strong>Simply email us at <a href="" target="_blank" rel="noopener noreferrer"></a>.</p><p></p><div class="twocols first"> <a href="" class="widget_button red">>> Register to attend.</a></div><div class="twocols"> <a href="" class="widget_button gray">>> Read our Reddit AMA on Cybersecurity</a></div><br/>
Atlanta Alumni Event: 'Using Data for Social Good' Alumni Event: 'Using Data for Social Good'2018-02-20T06:00:00Z6 p.m.8 p.m.Atlanta, Ga<p>Join the WashU Atlanta Network for a special presentation by Dr. Ellen Zegura, BS '83, MS '90, DSc '93, followed by a networking reception. At Georgia Tech, Dr. Zegura helped combine a culture of philanthropy and computing as an agent of change through the Computing for Good initiative a project-based teaching and research activity that focuses on using computing to solve pressing societal problems. The presentation will share how computing and related disciplines can play a critical role in addressing systemic societal problems, and that tackling such problems can push the intellectual boundaries of the disciplines.</p><p>6 p.m. - Registration, Heavy Appetizers, and Open Bar<br/>6:30 p.m. - Using Data for Social Good and Q&A<br/>7:15 p.m. - Reception Continues<br/></p><p><a href="">Ellen W. Zegura</a> earned bachelor’s degrees in computer science and electrical engineering, and a master’s and DSc in computer science, all from Washington University in St. Louis. Since 1993, she has been on the faculty of the College of Computing at Georgia Tech, where she conducts research and teaches in computer networking and computing for development.  She is a Fellow of the IEEE, a Fellow of the ACM, and an elected member of the Computing Research Association Board (CRA). Since Fall 2014 she has been on the Executive Board of the CRA. She served on the NSF CISE Advisory Committee from 2005-2009.<br/></p><p><a href="">>> Register online.</a><br/></p>
Internship Presentation: Halo Neuroscience Presentation: Halo Neuroscience2018-02-21T06:00:00Znoon1 p.m.Simon Hall, Room 105<p>Can neurotechnology impact athletic performance? Learn how <a href="" target="_blank" rel="noopener noreferrer" style="background-color: #ffffff;">Halo Neuroscience</a> is being used by professional athletes to enhance their training.</p><p>Dr. Daniel Chao, Co-Founder and CEO of Halo Neuroscience, will present.</p><p>Internship opportunities available that are ideal for Computer Science/Engineering/Medical students. </p><p>Go to the <a href="" target="_blank" rel="noopener noreferrer">CareerLink website and search for Daniel Chao</a> to reserve a spot to chat after the presentation.<br/></p>Career Center
Blockchain and Cryptocurrency Workshop and Cryptocurrency Workshop2018-02-21T06:00:00Z6 p.m.8 p.m.Gregg Technology Classroom<p>​"Come learn more about blockchain and cryptocurrency with Microsoft at WashU and Women in Computer Science. After our presentation, we'll be making our very own cryptocurrency using Microsoft Azure. All are welcome! Free food will be provided."<br/></p>
Women & Engineering Event: What's new in the Cortex District & Engineering Event: What's new in the Cortex District2018-02-22T06:00:00Z5:30 p.m.CORTEX Innovation Commmunity, 4240 Duncan Ave #110<p>Join us for a Women & Engineering event during National Engineers Week — Women & Engineering: What's new in the Cortex District<br/></p><p><strong>5:30 p.m. </strong>Reception<br/><strong>6 p.m.</strong> Presentation</p><p>Áine O'Conner, Special Initiatives Lead from the <a href="" target="_blank" rel="noopener noreferrer">CORTEX Innovation Community</a>, will discuss the evolution and growth of this exciting area. Enjoy light appetizers and refreshments while networking with fellow alumni and current students. Deepen the conversation by attending the <a href="" target="_blank" rel="noopener noreferrer">Venture Café</a> before or after the presentation.<br/></p>Julie Anderson,
CSE Doctoral Student Seminar: Shali Jiang and Cynthia Ma Doctoral Student Seminar: Shali Jiang and Cynthia Ma2018-02-23T06:00:00Z12:30 p.m.2 p.m.Lopata Hall, Room 101<p><strong>"Efficient Nonmyopic Batch Active Search"<br/></strong></p><p><strong>​Shali Jiang</strong><br/>Adviser: Roman Garnett</p><p>Active search is a learning paradigm for actively identifying as many members of a given class as possible. Important applications include drug discovery, fraud detection, and product recommendation. All existing work focuses on sequential policies, i.e., selecting one point to query at a time. However, in many real applications, it is possible to evaluate multiple points simultaneously. In this paper we investigate batch active search, the first such study we know of in the literature. We first derive the Bayesian optimal policy for batch active search, and prove a lower bound on the performance gap between sequential and batch optimal policies. Then we propose novel batch policies inspired by state-of-the-art sequential policies, and develop an aggressive pruning technique that can further speed up the computation by up-to nearly 50 times. We conduct thorough experiments on three application domains: a citation network, material science, and drug discovery, testing all proposed policies (14 total) for a wide range of batch sizes. Results show that the empirical gap matches our theoretical bound; nonmyopic policies usually beat myopic ones significantly; we also find diversity to be an important consideration for batch policy design.<br/></p><p> <strong>"Modeling Gene Networks using TFA Inference"</strong></p><p><strong>Cynthia Ma</strong><br/>Adviser: Michael Brent<br/></p><p>A single cell, whether yeast or human, has no consciousness, but can respond to changes in its environment such as by building tools to harvest encountered nutrients while stopping the production of tools for nutrients no longer available. This is accomplished through complex networks of signals, looping in and out from genes in the cell’s DNA that encodes instructions for building said tools, as well as for building the signal carriers. To model these networks, it makes sense to include the activity of the signal carriers, particularly transcription factors (TFs) that directly activate or repress the copying of instructions from genes, but current technology is unable to efficiently measure these. As such, computational inference of transcription factor activity (TFA) from cell state information that can be efficiently measured is an ongoing problem. This talk will briefly review previous work, the common obstacles, and the current trajectory of TFA inference.<br/></p>
Colloquia Series: XiaoFeng Wang Series: XiaoFeng Wang2018-02-23T06:00:00Z11 a.m.Lopata Hall, Room 101<p style="text-align: center;"><strong></strong></p><p style="text-align: center;"><strong>System Security Research: From Discovery to Innovation</strong></p><p><strong>Abstract</strong></p><p><strong></strong>Innovations in security research often come from the curiosity about how rules can be bent. The interdisciplinary nature of system security further presents the researcher a vast space to explore such opportunities. In this talk, I will share our experience in finding and understanding security weaknesses on the technology frontier, demonstrating how big questions can be asked to help discover subtle but fundamental security problems inside modern computing systems, and how such findings can reshape system security designs, bringing forth new techniques, new research directions.  More specifically, using mobile and IoT as examples, I will show that discovery and analysis of their surprising side channel weaknesses (which can be exploited by even the apps without permissions to expose one's identity, locations, health information, etc.) questions the "security by construction" designs of these systems, identifying what need to be addressed to better protect them.  Further to be presented is the preliminary effort to automate such a discovery process, by leveraging the knowledge automatically recovered from documents to guide detection of security-critical vulnerabilities.  Finally, I will highlight the key insights of system security research and discuss the directions that might impact the development of new security technologies in the years to come.</p><p><strong>Biography</strong></p><p>Dr. XiaoFeng Wang is a James H. Rudy Professor of Computing at Indiana University, Co-director of IU's Center for Security and Privacy in Informatics, Computing and Engineering, and the Vice Chair of the ACM SIGSAC (special interest group on security, audit and control).  He is also a PC Co-Chair of the 2018 ACM Conference on Computer and Communications Security (CCS).  Dr. Wang received his Ph.D. in Electrical and Computer Engineering from Carnegie Mellon University.  He is considered to be one of the most prominent system security researchers, among the most productive authors at leading security venues (#5 among over 6,000 authors in the past 18 years according to online statistics:  Dr. Wang is known for his high-impact research on security analysis of real-world systems and biomedical data privacy. Particularly the projects he led on payment and single-sign-on API integrations, Android and iOS security and IoT protection have changed the way the industry built these systems.  Also he is a pioneer researcher on human genome privacy and a co-founder of the iDASH Genome Privacy Competition that bridges the frontline security and cryptography research and the real-world demands for biomedical data sharing and computing protection. More recently, he is actively working on Data-Centric Intelligent Security, Cybercrimes, Hardware-support Protection and IoT Security. For his work, Dr. Wang has received numerous awards, including the Award for Outstanding Research in Privacy Enhancing Technologies (the PET Award) and the Best Practical Paper Award at the 32nd IEEE Symposium on Security and Privacy.  His research has been extensively reported by the public media, including CNN, MSNBC, Forbes, Slashdot, Nature News, etc.<br/></p>
Engineers Week Event: Google Assistant Workshop Week Event: Google Assistant Workshop2018-02-23T06:00:00Z3:30 p.m.4:45 p.m.Simon Hall, Room 023<p>​From EnCouncil: "Google will be having a Google Assistant Workshop. Pizza will be provided."<br/></p>EnCouncil
Colloquia Series: Qi (Alfred) Chen Series: Qi (Alfred) Chen2018-02-26T06:00:00Z11:30 a.m.Jolley Hall, Room 309<p style="text-align: center;"><strong>Securing Smart, Connected Systems through Systematic Problem Analysis and Mitigation</strong></p><p><strong>Abstract</strong></p><p><strong></strong> The world is increasingly connected through a series of smart, connected systems such as smartphone systems, smart home systems, and the emerging smart transportation and autonomous vehicle systems. While leading to improved services, such transformation also introduces new security challenges. To address these challenges, in contrast to existing defense mechanisms that are mostly ad hoc and reactive, my research aims at developing proactive defense approaches that can systematically discover, analyze, and mitigate new security problems in smart, connected systems.<br/></p><p style="text-align: justify;"> In this talk, I will focus on my research efforts in securing two most basic components in any smart, connected system: network stack and smart control. For network stack security, I will describe our discovery of a new attack vector (US-CERT alert TA16-144A) brought by the recent expansion in DNS and our subsequent systematic analysis at both network and software levels for remediation purposes. For smart control security, I will describe my most recent work that performed the first security analysis of the next-generation Connected Vehicle (CV) based traffic signal control, which discovers new vulnerabilities at the traffic signal control algorithm level. I will conclude by discussing my future research plans in securing existing and future smart, connected systems, especially those in critical domains such as transportation and automobile.</p><p><strong>Biography</strong></p><p>Qi Alfred Chen is a PhD candidate in the EECS department at University of Michigan advised by Professor Z. Morley Mao. His research interest is network and systems security, and the major theme of his research is to address security challenges through systematic problem analysis and mitigation. His research has discovered and mitigated security problems in various systems such as next-generation transportation systems, smartphone OSes, network protocols, DNS, GUI systems, and access control systems. His work has impact in both academia and industry with over 10 top-tier conference papers, news coverage and interviews, vulnerability disclosures, and industry discussions and responses. His current research focuses on smart systems and IoT, e.g., smart home, smart transportation, and autonomous vehicle systems.<br/></p>
PhD Visit Day Visit Day2018-03-02T06:00:00ZWhitaker Hall<p>This event is for prospective PhD students. Specific events, including panel sessions, will vary by department. <br/></p><p><strong>4-6 p.m.</strong> <br/>Reception in Whitaker Atrium<br/>Poster session on 2nd & 3rd floors.<br/></p>Graduate Student Services,
Colloquia Series: ​Paige Rodeghero​Paige-Rodeghero.aspx1469Colloquia Series: ​Paige Rodeghero2018-03-02T06:00:00Z11 a.m.Lopata Hall, Room 101
Colloquia Series: Yevgeniy Vorobeychik Series: Yevgeniy Vorobeychik 2018-03-05T06:00:00Z11:30 a.m.Jolley Hall, Room 309<div></div><div><p style="text-align: center;"><strong>Adversarial AI for Social Good</strong></p><p><strong>Abstract</strong></p><p><strong></strong>A major emerging research topic at the interdisciplinary interface of security, privacy, and AI is how to develop and deploy AI techniques in such adversarial environments.  My research in this area combines techniques from optimization, game theory, network science, machine learning, and systems security, to address fundamental problems such as how to learn classifiers that are robust to evasion attacks, protect elections from malicious influence, and share high-quality data while minimizing privacy risk.  In this talk, I will discuss our research on the latter two problems.<br/></p><p style="text-align: justify;">My research on protecting elections aims to develop effective methods to preserve the integrity of election results in the face of malicious attacks.  I will describe a general framework for reasoning about protection decisions (such as auditing) using a game theoretic approach which combines large-scale optimization with social choice theory.  I will then briefly mention several recent efforts at modeling how elections may be subverted through social influence (such as spreading fake news over social media), and how we can limit diffusion of such malicious influence.</p><p style="text-align: justify;">Next, I will describe how we approach two problems in the context of privacy-preserving data sharing: sharing structured data (such as portions of the EMR that include demographics and diagnostic codes, as well as genomic summary statistics) and sanitizing clinical notes. I will present a framework for modeling privacy risk from an adversarial perspective, and a game theoretic approach for balancing the utility from shared data with privacy risk. Finally, I will describe an approach for reasoning about privacy risk associated with sanitizing clinical notes using machine learning techniques, and present a novel algorithm for this task which has provable guarantees about privacy risk (given our threat model) and preserves most of the original content.</p><p><strong>Biography</strong></p><p>Yevgeniy Vorobeychik is an Assistant Professor of Computer Science and Biomedical Informatics at Vanderbilt University. He received a Ph.D. (2008) in Computer Science and Engineering from the University of Michigan. His work focuses on game theoretic modeling of security and privacy, adversarial machine learning, algorithmic and behavioral game theory, optimization, and network science. Dr. Vorobeychik received an NSF CAREER award in 2017, and was an invited IJCAI-16 early career spotlight speaker. He is one of the team leads for the NIH-funded Center for Genetic Privacy and Identity in Community Settings at Vanderbilt, and directs the Computational Economics Research Lab. Dr. Vorobeychik was nominated for the 2008 ACM Doctoral Dissertation Award and received honorable mention for the 2008 IFAAMAS Distinguished Dissertation Award.<br/></p></div>