​15th Annual Postdoc Symposiumhttps://engineering.wustl.edu/Events/Pages/15th-annual-postdoc-symposium-20190321.aspx1970​15th Annual Postdoc Symposium2019-03-21T05:00:00Z11 a.m.5 p.m.Eric P. Newman Education Center<p>The 2019 Postdoc Symposium will be held from 11 a.m. to 5 p.m. on Thursday, March 21, in the Eric P. Newman Education Center. This year's event will feature keynote speaker Holden Thorp, <g class="gr_ gr_12 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="12" data-gr-id="12"><g class="gr_ gr_12 gr-alert gr_gramm gr_inline_cards gr_run_anim Punctuation multiReplace" id="12" data-gr-id="12">PhD</g></g>, who will present "The Importance of Postdocs: Our impact in academia and beyond." Thorp is provost and executive vice chancellor for Academic Affairs and the Rita Levi-Montalcini Distinguished University Professor.<br/></p><p>The Postdoc Symposium is an annual opportunity to recognize and showcase the important contributions of postdocs to the university’s research and teaching mission. This annual event is sponsored by the WashU Postdoc Society, the Office of Postdoctoral Affairs and the Office of the Vice Chancellor for Research.</p><p><a href="https://mailingsresponse.wustl.edu/trk/click?ref=z1030up2e7_2-c76fx3c7ccx05048&">Register to attend online.</a><br/></p>WashU Postdoc Society, the Office of Postdoctoral Affairs and the Office of the Vice Chancellor for Research.
Colloquia Series-Qi Lihttps://engineering.wustl.edu/Events/Pages/Colloquia-Series-Qi-Li.aspx2008Colloquia Series-Qi Li2019-03-22T05:00:00Z11:00 a.m.Lopata Hall, Room 101<p style="text-align: center;"><strong>Pattern-Based Mining of Entity/Relation Structures from Massive Text</strong></p><p><strong>Abstract</strong></p><p>The majority of information nowadays is carried by massive and unstructured text, in the form of news, articles, reports, or social media messages.  This poses a major research challenge on mining entity/relation structures from unstructured text.  Manual curation or labeling cannot be scalable to match the rapid growth of text.  Most existing information extraction approaches rely on heavy human annotations, which can be too expensive to tune and not adaptable to new domains.</p><p>In this talk, I will present a pattern-based methodology that conducts information extraction from the massive corpora using existing resources with little human effort. The first component, WW-PIE, discovers meaningful textual patterns that contain the entities of interest. The second component, TruePIE, discovers high quality textual patterns for target relation types. I will demonstrate how semi-supervised methods can empower information extraction for broad applications and provide explainable results.</p><p><strong>Biography</strong></p><p>Qi Li is currently a postdoc researcher and adjunct professor at Department of Computer Science, University of Illinois at Urbana-Champaign, working with Prof. Jiawei Han. Her research interests lie in the area of data mining with a focus on the extraction and aggregation of information from multiple data sources. Qi obtained her PhD in Computer Science and Engineering from the State University of New York at Buffalo in 2017 advised by Prof. Jing Gao, and MS in Statistics from University of Illinois at Urbana-Champaign in 2012. She has received several awards including the 2018 Data Mining Research Excellent Award (Bronze) at UIUC, the Presidential Fellowship of University at Buffalo, the Best CSE Graduate Research Award and the CSE Best Dissertation Award at Department of Computer Science and Engineering, University at Buffalo. More information can be found at <a href="https://publish.illinois.edu/qili5/">https://publish.illinois.edu/qili5/</a>.<br/></p>
Doctoral Student Seminar: ​Shenghua He and Rajagopal Venkatesaramihttps://engineering.wustl.edu/Events/Pages/DSS-Talks.aspx2014Doctoral Student Seminar: ​Shenghua He and Rajagopal Venkatesarami2019-03-22T05:00:00Z12:30pm1:30pmLopata 101<strong rtenodeid="11" style="text-decoration: underline;">Rajagopal Venkatesaramani</strong><div><p rtenodeid="13"><strong>Title:</strong> A Semantic Cover Approach to Topic Modeling<br/></p><p><strong>Abstract:</strong> We introduce a novel topic modeling approach based on constructing a semantic set cover for clusters of similar documents. </p><p>Specifically, our approach first clusters documents using their Tf-Idf representation, and then covers each cluster with a set of topic words based on semantic similarity, defined in terms of a word embedding. Computing a topic cover amounts to solving a minimum set cover problem. Our evaluation compares our topic modeling approach to LDA on three metrics: 1) qualitative topic match, measured using evaluations by MTurk human subjects, 2) performance on classification tasks using each topic model as a sparse feature representation, and 3) topic coherence. We find that qualitative judgments significantly favor our approach, the method outperforms LDA on topic coherence, and is comparable to LDA on document classification tasks.<br/></p><strong rtenodeid="12" style="text-decoration: underline;">Shenghua He </strong><br/><br/><br/></div>
Colloquia Series-Ashay Ranehttps://engineering.wustl.edu/Events/Pages/Colloquia-Series-Ashay-Rane.aspx2002Colloquia Series-Ashay Rane2019-03-25T05:00:00Z11:30 a.m.Jolley Hall, Room 309<p style="text-align: center;"><strong>Broad-Based Side-Channel Defenses for Modern Microarchitectures</strong></p><p><strong>Abstract</strong></p><p>Private or confidential information is used in several applications, including not just cryptographic implementations but also machine-learning algorithms, databases, and parsers. However, even after using techniques like encryption, authentication, and isolation, it is difficult to maintain the privacy or confidentiality of such information due to so-called side channels, using which attackers can infer sensitive information by monitoring program execution. Various side channels such as execution time, power consumption, exceptions, or micro-architectural components such as caches and branch predictors have been used to steal intellectual property, financial information, and sensitive document contents.</p><p>In this talk, I will present a solution for closing a broad class of side channels in a diverse set of applications running on modern microprocessors. Compared to prior solutions, which close an isolated number of side channels, our solution closes digital side channels (such as the cache, address trace, and branch predictor side channels) which carry information over discrete bits. Our solution also extends the capabilities of non-digital side-channel defenses, specifically power channel defenses, to a broad class of applications running on modern microprocessors. Finally, our solution is customizable, since it permits the defense to be tailored to the threat model, the program, and the microarchitecture.</p><p><strong>Biography</strong></p><p>Ashay Rane is a PhD student at the University of Texas at Austin, where he is advised by Professor Calvin Lin and Professor Mohit Tiwari. His current research is in the area of side-channel defenses, while his prior research included topics in high-performance computing.<br/></p>
Dissertation Defense: Chao Wanghttps://engineering.wustl.edu/Events/Pages/ChaoWangDissertation.aspx2022Dissertation Defense: Chao Wang2019-03-26T05:00:00Z1PM3PMJolley Hall, Room 309
Master's Project Defense: Leiquan Panhttps://engineering.wustl.edu/Events/Pages/panmastersproject.aspx2023Master's Project Defense: Leiquan Pan2019-03-26T05:00:00Z3PM4PMJolley Hall, Room 309<p><strong>​Title:</strong> Challenges in Integrating IoT in Smart Home-- A Reality Check<br/></p>
Dissertation Defense: Zhiyang Huanghttps://engineering.wustl.edu/Events/Pages/zhiyangdefense.aspx2026Dissertation Defense: Zhiyang Huang2019-03-27T05:00:00Z10AM12PMJolley Hall, Room 309
Doctoral Student Seminar: Xiaojian Xu and Sixie Yuhttps://engineering.wustl.edu/Events/Pages/doctoral-student-sem.aspx2015Doctoral Student Seminar: Xiaojian Xu and Sixie Yu2019-03-29T05:00:00Z12:30pm1:30pmLopata 101<strong rtenodeid="2" style="text-decoration: underline;">Sixie Yu</strong><div><p><strong>Title: </strong> Removing Malicious Nodes from Networks</p><p><strong>Abstract: </strong>A fundamental challenge in networked systems is detection and removal of suspected malicious nodes. In reality, detection is always imperfect, and the decision about which potentially malicious nodes to remove must trade off false positives (erroneously removing benign nodes) and false negatives (mistakenly failing to remove malicious nodes). However, in network settings this conventional tradeoff must now account for node connectivity.</p><p>In particular, malicious nodes may exert malicious influence, so that mistakenly leaving some of these in the network may cause damage to spread.On the other hand, removing benign nodes causes direct harm to these, and indirect harm to their benign neighbors who would wish to communicate with them. We formalize the problem of removing potentially malicious nodes from a network under uncertainty  through an objective that takes connectivity into account. We show that optimally solving the resulting problem is NP-Hard. We then propose a tractable solution approach based on a convex relaxation of the objective.</p><p>Finally, we experimentally demonstrate that our approach significantly outperforms both a simple baseline that ignores network structure, as well as a state-of-the-art approach for a related problem, on both synthetic and real-world datasets.</p><p><span style="text-decoration: underline;"><strong></strong></span><strong rtenodeid="109" style="text-decoration: underline;">Xiaojian Xu</strong><br rtenodeid="111"/></p></div>
Colloquia Series-Netanel Ravivhttps://engineering.wustl.edu/Events/Pages/Colloquia-Series-Netanel-Raviv.aspx2009Colloquia Series-Netanel Raviv2019-03-29T05:00:00Z11:00 a.m.Lopata Hall, Room 101<p style="text-align: center;"><strong>Codes, Computation, and Privacy in Data Science</strong></p><p><strong>Abstract</strong></p><p>Data intensive tasks have been ubiquitous ever since the data science revolution. The immensity of contemporary datasets no longer allows computations to be done on a single machine, and distributed computations are inevitable. Since most users cannot afford to maintain a network of commodity servers, burdensome computations are often outsourced to third party cloud services. However, this approach opens a Pandora's box of potential woes, such as malicious intervention in computations, privacy infringement, and workload imbalance.</p><p>Error correcting codes are mathematical devices that were originally developed to obtain noise resilience in digital communication. Recently, these devices have found surprising applications in solving various problems in distributed computing. This newly emerging topic, which addresses resiliency, security, and privacy issues in distributed environments through a coding-theoretic lens, is often called coded computing. In this talk I will survey some of my work on the topic, which includes coding for distributed gradient descent, an exciting new framework called Lagrange Coded Computing, and finally, an important extension of Private Information Retrieval called Private Computation.</p><p><strong>Biography</strong></p><p>Netanel received a B.Sc. in mathematics and computer science in 2010, an M.Sc. and Ph.D. in computer science in 2013 and 2017, respectively, all from the Technion, Israel. He is now a postdoctoral scholar at the Center for the Mathematics of Information (CMI) at the California Institute of Technology. He is an awardee of the IBM Ph.D. fellowship, the first prize in the Feder family competition for best student work in communication technology, the CMI postdoctoral fellowship, and the Lester-Deutsche postdoctoral fellowship. His research interests include applications of coding techniques to computation, storage, and privacy.<br/></p>
Colloquia Series-Shuochao Yaohttps://engineering.wustl.edu/Events/Pages/Colloquia-Series-Shuochao-Yao.aspx2030Colloquia Series-Shuochao Yao2019-04-01T05:00:00Z11:30 a.m.Jolley Hall, Room 309<p style="text-align: center;"><strong>Deep Learning for the Internet of Things</strong></p><p><strong>Abstract</strong></p><p>The Internet of Things (IoT) heralds the emergence of multitudes of computing-enabled networked everyday devices with sensing capabilities in homes, cars, workplaces, and on our persons, leading to ubiquitous smarter environments and smarter cyber-physical "things". The next natural step in this computing evolution is to develop the infrastructure needed for these computational things to collectively learn. Recent advances in deep learning revolutionized related fields, such as vision and speech recognition, but the question is: how can we bring advantages of deep learning to the emerging world of embedded IoT devices? In this talk, I will discuss core challenges in (i) foundational building blocks, (ii) physical-resource efficiency, (iii) human-resource efficiency, (iv) predictability, and (v) system design of deep learning solutions to meet needs of IoT applications.</p><p><strong>Biography</strong></p><p>Shuochao Yao is a Ph.D. in Computer Science at University of Illinois at Urbana-Champaign, advised by Professor Tarek Abdelzaher. His research lies in the modeling, system efficiency, human-resources efficiency, reliability, and related applications of deep learning enabled IoT. He is the recipient of the SenSys Best Paper Award Nominee (2017) and the ICCPS Best Paper Award (2017).<br/></p>
Doctoral Student Seminar:Jinghan Yang and Hai Lehttps://engineering.wustl.edu/Events/Pages/student-seminar.aspx2016Doctoral Student Seminar:Jinghan Yang and Hai Le2019-04-05T05:00:00Z12:30pm1:30pmLopata 101<span rtenodeid="2" style="text-decoration: underline;"><strong>Jinghan Yang</strong></span><div><span rtenodeid="2" style="text-decoration: underline;"><strong></strong></span><div><strong>Title: </strong>Protecting Geolocation Privacy of Photo Albums</div><div><br/></div><div><strong>Abstract: </strong>As people increasingly share personal information, including their photos and photo albums, on social media, there have been increasing concerns about personal privacy. We consider the specific issue of location privacy as potentially revealed by posting photo albums, which facilitate accurate geolocation with the help of deep learning methods even in the absence of geotags. We formalize this problem as limiting the number of photos to remove from an album to cause incorrect geolocation prediction and study this problem algorithmically. While we show that this problem is NP-Hard for several variants, we exhibit effective solution techniques based on integer programming, as well as an important tractable special case. Our experiments on real photo albums demonstrate that our approaches are indeed highly effective at preserving geolocation privacy with removal of a small fraction of photos.</div><div><br rtenodeid="5"/><strong rtenodeid="4" style="text-decoration: underline;">Hai Le</strong><br/></div><div><div><strong>Title:</strong> Conditional Sparse L_p-norm Regression With Optimal Probability</div><div><br/></div><div rtenodeid="6"><strong>Abstract: </strong>We consider the following conditional linear regression problem: the task is to identify both (i) a k-DNF condition c and (ii) a linear rule f such that the probability of c is (approximately) at least some given bound µ, and minimizing the l_p loss of f at predicting the target z in the distribution conditioned on c. Thus, the task is to identify a portion of the distribution on which a linear rule can provide a good fit. Algorithms for this task are useful in cases where portions of the distribution are not modeled well by simple, learnable rules, but on other portions such rules perform well. The prior state-of-the-art for such algorithms could only guarantee finding a condition of probability O(µ/n^k ) when a condition of probability µ exists, and achieved a O(n^k)-approximation to the target loss. Here, we give efficient algorithms for solving this task with a condition c that nearly matches the probability of the ideal condition, while also improving the approximation to the target loss to a O ~(n^{k/2}) factor. We also give an algorithm for finding a k-DNF reference class for prediction at a given query point, that obtains a sparse regression fit that has loss within O(n^k) of optimal among all sparse regression parameters and sufficiently large k-DNF reference classes containing the query point.</div><div><br/><br/></div><strong rtenodeid="4" style="text-decoration: underline;"><br/></strong></div><b><u><br/></u></b><p><br/></p></div>
Women & Engineering Leadership Society Summithttps://engineering.wustl.edu/Events/Pages/Women-Engineering-Leadership-Society-Summit-2019.aspx2004Women & Engineering Leadership Society Summit2019-04-06T05:00:00ZCortex Innovation CommunityJulie Anderson: engineering.alumni@wustl.edu or 314-935-8730.