- talks - lists.cs.princeton.edu

Young-suk Lee generals
by Melissa M. Lawson 08 May '12

08 May '12

Young-suk Lee will present his research seminar/general exam on Tuesday May 15 at 2PM in Room 401 (note room!). The members of his committee are: Olga Troyanskaya (advisor), Mona Singh, and David Blei. Everyone is invited to attend his talk and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. --------------------------- A microarray experiment measures the abundance of thousands of transcript in a given biological sample in order to quantify its unique transcriptome. Many research groups and institutions have used microarrays, and have been publishing them and their associated sample description on the GEO (Gene Expression Omnibus) database website. The most valuable biological sample can arguably be human tissue samples, and so most human microarray studies have been so far limited to few experiments for certain tissue-types. GEO has made these human microarray data publicly available, but the free-text sample description hinders a large-scale tissue-specific microarray analysis. We present a hierarchical multi-label tissue prediction algorithm that returns a rank of predicted tissue-types for a given microarray data. This algorithm may be used to annotate the many human microarrays in GEO in which their tissue information is hidden or even absent. We propose that all tissue prediction algorithms must return a rank because most, if not all, biological samples consist of multiple tissue types that have a hierarchical order. So even a single accurate prediction may not completely describe the biological sample. We compare the performance of prediction algorithms with and without hierarchical information, and our algorithm that uses Bayesian correction to combine multiple tissue classifiers. In the biological community, this algorithm may be used as a comprehensive background or sanity-check on new human microarray datasets that measures potential contamination and tissue composition of the biological sample. Reading list Introduction to Machine Learning (Adaptive Computation and Machine Learning) by Ethem Alpaydin Ch 1 Introduction Ch 2 Supervised Learning Ch 3 Bayesian Decision Theory Ch 4 Parametric Methods Ch 5 Multivariate Methods Ch 8 Nonparametric Methods Ch 10 Linear Discrimination Ch 14 Assessing and Comparing Classification Algorithms Ch 15 Combining Multiple Learners Papers: Matthew N. McCall, Karan Uppal, Harris A. Jaffee, Michael J. Zilliox, and Rafael A. Irizarry The Gene Expression Barcode: leveraging public data repositories to begin cataloging the human and murine transcriptomes Nucl. Acids Res. (2011) 39(suppl 1): D1011-D1015 doi:10.1093/nar/gkq1259 Zafer Barutcuoglu, Robert E. Schapire, and Olga G. Troyanskaya Hierarchical multi-label prediction of gene function Bioinformatics (2006) 22(7): 830-836 first published online January 12, 2006 doi:10.1093/bioinformatics/btk048 Seon-Young Kim and David J Volsky PAGE: Parametric Analysis of Gene Set Enrichment BMC Bioinformatics (2005) 6:144 doi:10.1186/1471-2105-6-144 Kilpinen S, Autio R, Ojala K, Iljin K, Bucher E, Sara H, Pisto T, Saarela M, Skotheim RI, Björkman M, Mpindi JP, Haapa-Paananen S, Vainio P, Edgren H, Wolf M, Astola J, Nees M, Hautaniemi S, Kallioniemi O. Systematic bioinformatic analysis of expression levels of 17,330 human genes across 9,783 samples from 175 types of healthy and pathological tissues. Genome Biol. 2008;9(9):R139. Disease signatures are robust across tissues and experiments. Dudley JT, Tibshirani R, Deshpande T, Butte AJ. Mol Syst Biol. 2009;5:307. Marion Gremse, Antje Chang, Ida Schomburg, Andreas Grote, Maurice Scheer, Christian Ebeling, and Dietmar Schomburg The BRENDA Tissue Ontology (BTO): the first all-integrating ontology of all organisms for enzyme sources Nucl. Acids Res. (2011) 39(suppl 1): D507-D513 first published online October 28, 2010 doi:10.1093/nar/gkq968 Tanya Barrett, Dennis B. Troup, Stephen E. Wilhite, Pierre Ledoux, Carlos Evangelista, Irene F. Kim, Maxim Tomashevsky, Kimberly A. Marshall, Katherine H. Phillippy, Patti M. Sherman, Rolf N. Muertter, Michelle Holko, Oluwabukunmi Ayanbule, Andrey Yefanov, and Alexandra Soboleva NCBI GEO: archive for functional genomics data sets—10 years on Nucl. Acids Res. (2011) 39(suppl 1): D1005-D1010 first published online November 21, 2010 doi:10.1093/nar/gkq1184 Shai S Shen-Orr, Robert Tibshirani, Purvesh Khatri, Dale L Bodian, Frank Staedtler, Nicholas M Perry, Trevor Hastie, Minnie M Sarwal, Mark M Davis and Atul J Butte Cell type–specific gene expression differences in complex tissues Nature Methods 7, 287 - 289 (2010) Published online: 7 March 2010 | doi:10.1038/nmeth.1439 Troyanskaya OG. Putting microarrays in a context: integrated analysis of diverse biological data. Brief Bioinform. 2005 Mar;6(1):34-43.

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T Wang generals
by Melissa M. Lawson 08 May '12

08 May '12

Tian Wang will present his research seminar/general exam on Tuesday May 15 at 10:30AM in Room 402. The members of his committee are: David Blei (advisor), Rob Schapire, and Rebecca Fiebrink. Everyone is invited to attend his talk and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. -------------------------- Title: Supervised Hierarchical Dirichlet Process Abstract: Hierarchical Dirichlet Process have been proved effective as a Bayesian non-parametric model for text document topic modeling. HDP places a non-parametric prior on distribution of topics, thus allowing the number of topics to be learnt through data. It works as a Bayesian non-parametric extension to Latent Dirichlet Allocation. In my research, I introduce Supervised Hierarchical Dirichlet Process, a Bayesian non-parametric extension to Supervised Latent Dirichlet Allocation. In topic modeling setting, Supervised Hierarchical Dirichlet Process relates a continuous response variable, typically a rating to a document, to the topic assignments of all words in a document. For learning I use Markov Chain Monte Carlo method to sample the intractable posterior. More specifically, I derived and implemented a collapsed Gibbs sampler for Supervised Hierarchal Dirichlet Process. I also derived posterior sampling equations for Supervised Latent Dirichlet Allocation, which is not published before or in any software package. Like Supervised Latent Dirichlet Allocation, the main application of this model is to predict document ratings given the documents. I use movie reviews and movie review ratings as my data set. Supervised Hierarchical Dirichlet Process is compared with a conventional Hierarchical Dirichlet Process followed by a separate regression with the response variable in terms of prediction error on new documents. Reading List: [1] C. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics), chapter 8,11. Springer-Verlag New York, Inc., Secaucus, NJ, USA, 2006. [2] D. Blei and P. Frazier. Distance dependent chinese restaurant processes. Journal of Machine Learning Research, 12:2461–2488, 2011. [3] D. Blei and M. Jordan. Variational methods for the dirichlet process. Journal of Bayesian Analysis, 1[1]:121–144, 2006. [4] D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 2003.  1 [5] D. Blei and J. McAuliffe. Supervised topic models. In J.C. Platt, D. Koller, Y. Singer, and S. Roweis, editors, Advances in Neural Information Processing Systems 20, pages 121–128. MIT Press, Cambridge, MA, 2008. [6] S. Gershman and D. Blei. A tutorial on bayesian nonparametric models. June 2011. [7] R. Neal. Markov chain sampling methods for dirichlet process mixture models. JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 9(2):249–265, 2000. [8] Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical dirichlet processes. Journal of the American Statistical Association, 101[476]:1566–1581, 2006. [9] Y. W. Teh. Dirichlet processes. In Encyclopedia of Machine Learning. Springer, 2010. [10] C. Wang, J. Paisley, and D. Blei. Online variational inference for the hierarchical dirichlet process. Artificial Intelligence and Statistics, 2011.

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S Berkiten general exam
by Melissa M. Lawson 07 May '12

07 May '12

Sema Berkiten will present her research seminar/general exam on Monday May 14 at 10AM in Room 402. The members of her committee are: Szymon Rusinkiewicz (advisor), Adam Finkelstein, and Kai Li. Everyone is invited to attend her talk and those faculty wishing to remain for the oral exam following are welcome to do so. Her abstract and reading list follow below. ----- Original Message ----- Abstract: Image alignment is one of the very first steps for most computer vision algorithms. Image fusion, image mosaicing, image panorama, object recognition and detection, photometric stereo enhanced rendering are some of the examples in which image alignment is a crucial step for a promising result. In this work, we focused on a specific problem which is alignment of high-resolution images taken from the same viewpoint under different light directions. Although images are taken from the same viewpoint, there might be some misalignment due to perturbations to the camera and the effect of optical image stabilization. For this specific alignment problem, we made a broad literature survey and try different tools and algorithms to compare different approaches and find out the best approach to solve this problem. Based on our experiments, we saw that the best approaches are the feature-based ones. We found SIFT and SURF reliable for most cases. For feature-based approaches, one of the main problems is elimination of outliers, and we solved this problem with using RANSAC framework. Also datasets that we focus on have many images, between 30-60, of the same object, and in order to take advantage of having many images, we constructed a graph on images so that the most similar images are connected. Minimum spanning tree is one way of constructing such a data structure. As a future work we are planning to explore how to distribute the accumulated alignment error because of the tree structure. Reading List: 1. Chunming Tang; Yan Dong; Xiaohong Su; , "Automatic Registration Based on Improved SIFT for Medical Microscopic Sequence Images," Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on , vol.1, no., pp.580-583, 20-22 Dec. 2008 2. Matthew Brown and David G. Lowe. 2007. Automatic Panoramic Image Stitching using Invariant Features. Int. J. Comput. Vision 74, 1 (August 2007), 59-73. DOI=10.1007/s11263-006-0002-3 http://dx.doi.org/10.1007/s11263-006-0002-3 3. David G. Lowe. 2004. Distinctive Image Features from Scale-Invariant Keypoints. Int. J. Comput. Vision 60, 2 (November 2004), 91-110. DOI=10.1023/B:VISI.0000029664.99615.94 http://dx.doi.org/10.1023/B:VISI.0000029664.99615.94 4. Bruce D. Lucas and Takeo Kanade. 1981. An iterative image registration technique with an application to stereo vision. In Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2 (IJCAI'81), Vol. 2. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 674-679. 5. Greg Ward. Fast, robust image registration for compositing high dynamic range photographcs from hand-held exposures. Journal of Graphics Tools, 8(2):17–30, 2003. 6. Bingjian Wang; Dongliang Wu; Wenzheng Xu; Quan Lu; Fan Li; Shangqian Liu; Guowang Gao; Rui Lai; , "A new image registration method for infrared images and visible images," Image and Signal Processing (CISP), 2010 3rd International Congress on , vol.4, no., pp.1745-1749, 16-18 Oct. 2010 7. Barbara Zitová, Jan Flusser, Image registration methods: a survey, Image and Vision Computing, Volume 21, Issue 11, October 2003, Pages 977-1000, ISSN 0262-8856, 10.1016/S0262-8856(03)00137-9. 8. Herbert Bay, Andreas Ess, Tinne Tuytelaars, and Luc Van Gool. 2008. Speeded-Up Robust Features (SURF). Comput. Vis. Image Underst. 110, 3 (June 2008), 346-359. DOI=10.1016/j.cviu.2007.09.014 http://dx.doi.org/10.1016/j.cviu.2007.09.014 9. Feng Zhao; Qingming Huang; Wen Gao; , "Image Matching by Normalized Cross-Correlation," Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on , vol.2, no., pp.II, 14-19 May 2006 10. Shun'ichi Kaneko, Yutaka Satoh, Satoru Igarashi, Using selective correlation coefficient for robust image registration, Pattern Recognition, Volume 36, Issue 5, May 2003, Pages 1165-1173, ISSN 0031-3203, 10.1016/S0031-3203(02)00081-X. 11. Martin A. Fischler and Robert C. Bolles. 1981. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 6 (June 1981), 381-395. DOI=10.1145/358669.358692 http://doi.acm.org/10.1145/358669.358692 12. Winder, S. & Brown, M. Picking the best DAISY. IEEE Conference on Computer Vision and Pattern Recognition (2009) 0, 178-185 (2009).

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J Cesareo and D Drutskoy MSE talks rescheduled
by Melissa M. Lawson 07 May '12

07 May '12

Due to a conflict this morning, the two MSE talks have been rescheduled for today at 2:45 (Cesareo) and 3:15 (Drutskoy) in Room 302.

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Colloquium Speaker Ben Livshits Monday May 7th 1:30pm
by Nicole E. Wagenblast 07 May '12

07 May '12

Finding Malware on a Web Scale Ben Livshits , Microsoft Research Monday, May 7 1:30pm Computer Science 105 Over the last several years, JavaScript malware has emerged as one of the most popular ways to deliver drive-by attacks to unsuspecting users through the browser. This talk covers recent Microsoft Research experiences with finding malware on the web. It highlights two tools: Nozzle and Zozzle. Nozzle is a runtime malware detector that focuses on finding heap spraying attacks. Zozzle is a mostly static detector that finds heap sprays and other types of JavaScript malware. Both are extremely precise: Nozzle false positive rate is close to one in a billion; Zozzle's is about one in a million. Both are deployed by Bing and are used daily to find thousands of malicious web sites. This talk will focus on interesting interplay between static and runtime analysis and cover what it takes to migrate research ideas into real-world products. Ben Livshits is a researcher at Microsoft Research in Redmond, WA and an affiliate professor at the University of Washington. Originally from St. Petersburg, Russia, he received a bachelor's degree in Computer Science and Math from Cornell University in 1999, and his M.S. and Ph.D. in Computer Science from Stanford University in 2002 and 2006, respectively. Dr. Livshits' research interests include application of sophisticated static and dynamic analysis techniques to finding errors in programs. Ben has published papers at PLDI, POPL, Oakland Security, Usenix Security, CCS, SOSP, ICSE, FSE, and many other venues. He is known for his work in software reliability and especially tools to improve software security, with a primary focus on approaches to finding buffer overruns in C programs and a variety of security vulnerabilities (cross-site scripting, SQL injections, etc.) in Web-based applications. He is the author of several dozen academic papers and patents. Lately he has been focusing on how Web 2.0 application and browser reliability, performance, and security can be improved through a combination of static and runtime techniques. Ben generally does not speak of himself in the third person.

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D Drutskoy MSE talk
by Melissa M. Lawson 01 May '12

01 May '12

Dmitry Drutskoy will present his MSE talk on Monday May 7 at 11:30AM in Room 302 (note room!). The members of his committee are Jen Rexford, advisor, and Michael Freedman, reader. Everyone is invited to attend his talk. His abstract follows below. -------------------- FlowN: Software-Defined Network Virtualization With the standardization of the interface between the control plane and data plane, software defined networking is a natural fit with network virtualization as any software can run to interact with the data plane. A virtualization layer can then be created which then modifies the view of the physical network and enables multiple control planes to run concurrently -- each targeting their view of the dataplane. In contrast to previous virtualization solutions, we present the FlowN system, which implements full network virtualization. With this, each virtual network: (i) is independent and can control the entire flow address space, (ii) can control the entire path within the network to enable control over, for example, bandwidth allocation and handling failure, and (iii) can be an arbitrary topology. The FlowN architecture is designed to be scalable through the use of existing database technology for mapping virtual to physical topologies, as well as a shared controller platform analogous to container-based virtualization. Our prototype extends the OpenFlow NOX controller and uses a MySQL database. We demonstrate the latency efficiency of our container based approach by comparing it to FlowVisor and unvirtualized NOX.

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T Liu generals
by Melissa M. Lawson 01 May '12

01 May '12

Tianqiang Liu will present his research seminar/general exam on Tuesday May 8 at 2PM in Room 401 (note room!). The members of his committee are: Tom Funkhouser (advisor), Szymon Rusinkiewicz, and Moses Charikar. Everyone is invited to attend his talk and those faculty wishing to remain for the oral exam following are welcome to do so. His abstract and reading list follow below. ----- Original Message ----- Abstract : In this project, we propose an automatic algorithm for finding a correspondence map between two 3D surfaces. The key insight is that global reflective symmetry axes are stable, recognizable, semantic features of most real-world surfaces. Thus, it is possible to find a useful map between two surfaces by first extracting symmetry axis curves, aligning the extracted curves, and then extrapolating correspondences found on the curves to both surfaces. The main advantages of this approach are efficiency and robustness: the difficult problem of finding a surface map is reduced to three significantly easier problems: symmetry detection, curve alignment, and correspondence extrapolation, each of which has a robust, polynomial-time solution (e.g. optimal alignment of 1D curves is possible with dynamic programming). We investigate of this approach on a wide range of examples, including both intrinsically symmetric surfaces and polygon soups, and find that it is superior to previous methods in cases where two surfaces have different overall shapes but similar reflective symmetry axes, a common case in computer graphics. Reading list: 1. VAN KAICK, O., ZHANG, H., HAMARNEH, G., AND COHEN- OR, D. 2010. A survey on shape correspondence. Eurographics State-of-the-Art report. 2. BRONSTEIN, A. M., BRONSTEIN, M. M., AND KIMMEL, R. 2006. Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching. PNAS 3 . KIM, V. G., LIPMAN, Y., AND FUNKHOUSER, T. 2011. Blended intrinsic maps. ACM Transactions on Graphics (Proc of SIGGRAPH 2011). 4. MARZAL, A., AND PALAZO ́ N, V. 2005. Dynamic time warping of cyclic strings for shape matching. Pattern Recognition and Image Analysis, 5. SCHREINER, J., ASIRVATHAM, A., PRAUN, E., AND HOPPE, H. 2004. Inter-surface mapping. ACM Transactions on Graphics (Proc of SIGGRAPH 2004). 6. XU, K., ZHANG, H., TAGLIASACCHI, A., LIU, L., LI, G., MENG, M., AND XIONG, Y. 2009. Partial intrinsic reflectional symmetry of 3d shapes. ACM Transactions on Graphics 7. ZHANG, H., SHEFFER, A., COHEN-OR, D., ZHOU, Q., VAN KAICK, O., AND TAGLIASACCHI, A. 2008. Deformation driven shape correspondence. Computer Graphics Forum, vol. 27, Wiley Online Library, 1431–1439. 8. PODOLAK, J., SHILANE, P., GOLOVINSKIY, A., RUSINKIEWICZ, S., AND FUNKHOUSER, T. 2006. A planar-reflective symmetry transform for 3d shapes, ACM Transactions on Graphics (Proc of SIGGRAPH 2006) 9. PRAUN, E., SWELDENS, W., AND SCHRO ̈ DER, P. 2001. Consistent mesh parameterizations. ACM Transactions on Graphics (Proc of SIGGRAPH 2001). 10. HILAGA, M., SHINAGAWA, Y., KOHMURA, T., AND KUNII, T. 2001. Topology matching for fully automatic similarity estimation of 3d shapes. Proceedings of the 28th annual conference on Computer graphics and interactive techniques, ACM, 203– 212. 11. SUNDAR, H., SILVER, D., GAGVANI, N., AND DICKINSON, S. 2003. Skeleton based shape matching and retrieval. In Shape Modeling International, 2003, IEEE, 130–139. 12. DONALD D. HEARN, M. PAULINE BAKER , Computer Graphics with Open GL ( 4th Edition ):

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J Cesareo MSE talk
by Melissa M. Lawson 01 May '12

01 May '12

Jacopo Cesareo will present his MSE talk on Monday May 7 at 11AM in Room 302 (note room!). The members of his committee are: Jen Rexford, advisor and Michael Freedman, reader. Everyone is invited to attend his talk. His abstract follows below. ------------------------ Traffic filters block clients from communicating with certain Internet destinations. To prevent clients from evading the filtering policies, traffic filters may also block access to well- known anonymizing proxies. In response, researchers have designed more sophisticated circumvention techniques that rely on implicit proxies, lying along the path to unfiltered destinations. An implicit proxy transparently deflects traffic directed to an unfiltered destination toward the filtered destination. However, the effectiveness of implicit proxies highly depends on their presence in paths between clients and unfiltered destinations. In this paper we formulate and solve the problem of proxy placement, and evaluate our algorithms on snapshots of the Internet topology for a variety of client and destination sets. We also consider smart filtering techniques that select alternate routes to avoid implicit proxies, as well as the effects of asymmetric Internet routing. Our results show that a relatively small number of proxies can satisfy a large group of clients across a range of geographic locations.

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A Feldman FPO
by Melissa M. Lawson 01 May '12

01 May '12

Ari Feldman will present his FPO on Tuesday May 8 at 11AM in Sherrerd Hall 306 (note location!). The members of his committee are: Ed Felten, advisor; Michael Freedman and Andrew Appel, readers; Brian Kernighan and David Walker, nonreaders. A copy of his thesis, "Privacy and Integrity in the Untrusted Cloud", is on display in Room 310. Eveyrone is invited to attend his talk.

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J Calandrino FPO
by Melissa M. Lawson 01 May '12

01 May '12

Joe Calandrino will present his FPO on Tuesday May 8 at 9AM in Sherrerd Hall 306 (note location!). The members of his committee are: Ed Felten, advisor; Jen Rexford and Andrew Appel, readers; Brian Kernighan and David Walker, nonreaders. A copy of his thesis, "Control of Sensitive Data in Systems with Novel Functionality", is on display in Room 310. Everyone is invited to attend his talk.

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