LEAP Registration Deadlinehttps://engineering.wustl.edu/Events/Pages/leap-registration-deadline-20200127.aspx2421LEAP Registration Deadline2020-01-27T06:00:00Z<p>Win LEAP funding, unleash the impact of your science, advance your research towards commercialization, and develop personal connections with industry experts. Twice per year, the Leadership and Entrepreneurial Acceleration Program (LEAP) awards funding for translational research and inventions with the goal of commercialization. LEAP is open to any person/team with existing or potential WashU intellectual property<br/></p><p>Survey results from the Fall 2019 Cycle showed that 100 percent of participants agreed that "LEAP interactions helped me assess our technology's readiness for commercialization" and they would "recommend participation in LEAP to colleagues." Now it is your chance to participate. Registration for the Spring 2020 Cycle is open now until <strong>end-of-day Monday, Jan. 27</strong>. It only takes a few minutes to apply and can be <a href="https://skandalaris.wustl.edu/programs/launch/leap/" target="_blank">completed online</a>.<br/><br/><strong>Benefits of participating LEAP:</strong></p><ul><li>Be considered for funding (up to $50k for top-scoring projects; one drug discovery project may be awarded $100k per judges decision)<br/></li><li>An educational and interactive process providing:</li><ol><li>Guidance to turn your project into an industry asset with a clear developmental plan for commercialization</li><li>Essential skills to attract commercial funding partners (e.g. federal grants, investors, etc.)</li><li>Feedback from industry experts and opportunities to build long-lasting relationships</li><li>Development of a written summary and presentation for projects as applied to the market</li><li>Access to a dedicated team, led by the Skandalaris Center's Assistant Director of LEAP and Research Innovation, that provides support in navigating the scientific entrepreneurial ecosystem at WUSTL (ICTS, CDD, OTM, etc.)</li></ol></ul><p><strong>BONUS funding and</strong><strong></strong><strong> resources available:</strong></p><p><strong></strong>Sun Pharma Advanced Research Company Limited (SPARC) may provide up to $2 million in value towards the development of a therapeutic. You can indicate your interest in the SPARC support when register.<br/></p>
CSE Colloquia Series: Pavithra Prabhakar https://engineering.wustl.edu/Events/Pages/CSE-Colloquia-Series-Pavithra_Prabhakar.aspx2459CSE Colloquia Series: Pavithra Prabhakar 2020-01-31T06:00:00Z11 A.M.LOPATA HALL, ROOM 101<p style="text-align: center;"><span rtenodeid="3" style="font-size: 11pt; font-family: "segoe ui", sans-serif; color: #212121;"><strong>Scalable Formal Verification of Cyber-Physical Systems</strong></span>​<br/></p><p rtenodeid="13"><strong>Abstract</strong></p><p>Cyber-physical systems (CPSs) consist of complex systems that combine control, computation and communication to achieve sophisticated functionalities as in autonomous driving in driverless cars and automated load balancing in smart grids. The safety criticality of these systems demands strong guarantees about their correct functioning. In this talk, we will present some of our work on formal verification techniques for cyber-physical systems analysis using the framework of hybrid systems. Hybrid systems capture an important feature of CPSs, namely, mixed discrete-continuous behaviors that arise due to the interaction of complex digital control software (discrete elements) with physical systems (continuous elements). We will focus on certain foundational properties of these systems, and present scalable techniques based on abstraction-refinement for their analyses.</p><p rtenodeid="14"><strong>Bio</strong></p><p>Pavithra Prabhakar is an associate professor in the Department of Computer Science and Peggy and Gary Edwards Chair in Engineering at the Kansas State University.  She obtained her doctorate in Computer Science and a masters in Applied Mathematics from the University of Illinois at Urbana-Champaign, followed by a CMI postdoctoral fellowship at the California Institute of Technology. Her main research interest is in formal analysis of cyber-physical systems with emphasis on both foundational and practical aspects related to automated and scalable techniques for verification and synthesis of hybrid systems. She is the recipient of a Marie Curie Career Integration Grant from the EU, a National Science Foundation CAREER Award and an Office of Naval Research Young Investigator Award.<br/></p>Sanjoy Baruah
Doctoral Student Seminar: Xiaojian Xu and Yifan Xu https://engineering.wustl.edu/Events/Pages/csedss2020-3.1.aspx2461Doctoral Student Seminar: Xiaojian Xu and Yifan Xu 2020-01-31T06:00:00Z12:30 P.M.1:30 P.M.Lopata 101<p><span style="text-decoration: underline;"><strong>Xiaojian Xu</strong></span></p><p><strong>Title: </strong>Boosting the Performance of Plug-and-Play Priors via Denoiser Scaling</p><p><strong>Abstract:  </strong>Plug-and-play priors (PnP) is an image reconstruction framework that uses an image denoiser as an imaging prior. Unlike traditional regularized inversion, PnP does not require the prior to be expressible in the form of a regularization function. This flexibility enables PnP algorithms to exploit the most effective image denoisers, leading to their state-of-the-art performance in various imaging tasks. In this paper, we propose a new denoiser scaling technique to explicitly control the amount of PnP regularization. Traditionally, the performance of PnP algorithms is controlled via intrinsic parameters of the denoiser related to the noise variance. However, many powerful denoisers, such as the ones based on convolutional neural networks (CNNs), do not have tunable parameters that would allow controlling their influence within PnP. To address this issue, we introduce a scaling parameter that adjusts the magnitude of the denoiser input and output. We theoretical justify the denoiser scaling from the perspectives of proximal optimization, statistical estimation, and consensus equilibrium. Finally, we provide numerical experiments demonstrating the ability of denoiser scaling to systematically improve the performance of PnP for denoising CNN priors that do not have explicitly tunable parameters.</p><p><span style="text-decoration: underline;"><strong>Yifan Xu </strong></span></p><p><strong>Title: </strong>Parallel Determinacy Race Detection for Futures</p><p><strong>Abstract: </strong>The use of futures can generate arbitrary dependences in the computation, making it difficult to detect races efficiently. Algorithms proposed by prior work to detect races on programs with futures all have to execute the program sequentially. We propose F-Order, the first known parallel race detection algorithm that detects races on programs that use futures. Given a computation with work T<sub>1</sub> and span T<sub>∞</sub>, our algorithm detects races in time O((T<sub>1</sub> lg k̂ + k<sup>2</sup> )/P + T<sub>∞</sub>(k + lg r lg k̂)) on P processors, where k is the number of future operations, r is the maximum number of readers per memory location, and k̂ is the maximum number of future operations done by a single future task, which is typically small. We have also implemented a prototype system based on the proposed algorithm and empirically demonstrates its practical efficiency and scalability.<br/></p>
EnWeekhttps://engineering.wustl.edu/Events/Pages/enweek-20200216.aspx2336EnWeek2020-02-16T06:00:00Z
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Computer Science Visit Dayhttps://engineering.wustl.edu/Events/Pages/CSEVisitDay.aspx2324Computer Science Visit Day2020-03-06T06:00:00Z9AM4:30PM
No Classes: Spring Breakhttps://engineering.wustl.edu/Events/Pages/no-classes-spring-break-20200308.aspx1873No Classes: Spring Break2020-03-08T06:00:00Z