- talks - lists.cs.princeton.edu

P Przytycki generals
by Melissa M. Lawson 17 May '13

17 May '13

Pawel Przytycki will present his research seminar/general exam on Wednesday May 22 at 10AM in Room 402. The members of his committee are: Mona Singh (advisor), Olga Troyanskaya, and Andrea LaPaugh. Everyone is welcome 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. ---------------------------- ABSTRACT Even though the first human genome was sequenced more than a decade ago, it is largely still unknown what specific genetic variations contribute to the diversity of human life. It is only recently that whole-genome sequencing has become affordable enough to utilize at a large enough scale to precisely examine the genetic differences between people. Furthermore, exome sequencing, which is sequencing only the 2% of the human genome that codes for proteins, has surfaced as a cheap way to find minute differences between people’s genetic codes. I propose a method called Nexome designed for network driven analysis of exome sequencing data. Nexome is a degree-aware gene prioritization algorithm that uses a random walker method to diffuse variation information mapped to genes over a protein-protein interaction network. As opposed to many previous algorithms which rely on a pre-defined set of seed genes, Nexome is designed to work with dense non-binary data. Degree correction is derived from diffusion of an uniformed prior. Nexome confirms that there is modularity in cancer variation data when it is mapped to the protein interaction network. Furthermore, this method is capable of discovering known cancer genes that are not frequently mutated. Finally, when applied to specific cancer types Nexome reveals that there is a modular structure to genes that are uniquely enriched in these cancers. BOOK An Introduction to Bioinformatics Algorithms - Pevzner and Jones PAPERS Erten, S., Bebek, G., Ewing, R. M., & Koyutürk, M. (2011). DADA: Degree-Aware Algorithms for Network-Based Disease Gene Prioritization. BioData mining, 4(1), 19. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21699738 Fu, W. et. al. (2013). Analysis of 6,515 exomes reveals the recent origin of most human protein-coding variants. Nature 493, 216–220. Retreived from: http://www.nature.com/nature/journal/v493/n7431/full/nature11690.html Kim, Y., Przytycka, A. (2012). Bridging the Gap between Genotype and Phenotype via Network Approaches. Frontiers in Genetics, 3, 00227. Retrieved from http://www.frontiersin.org/Journal/Abstract.aspx?s=1187&name=statistical_ge… Lee, I., Blom, U. M., Wang, P. I., Shim, J. E., & Marcotte, E. M. (2011). Prioritizing candidate disease genes by network-based boosting of genome-wide association data. Genome Research, 21(7), 1109–1121. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/21536720 Navlakha, S., & Kingsford, C. (2010). The power of protein interaction networks for associating genes with diseases. Bioinformatics, 26(8), 1057–1063. Retrieved from http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2853684&tool=pmce… O’Roak, B. J., Vives, L., Girirajan, S., Karakoc, E., Krumm, N., Coe, B. P., Levy, R., et al. (2012). Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature, 485(7397), 1-7. Nature Publishing Group. Retrieved from http://www.nature.com/doifinder/10.1038/nature10989 The Cancer Genome Atlas Research Network. (2012). Comprehensive genomic characterization of squamous cell lung cancers. Nature 489, 519–525. Retreived from: http://www.nature.com/nature/journal/v489/n7417/full/nature11404.html Rossin, E.J. (2011). Proteins Encoded in Genomic Regions Associated with Immune-Mediated Disease Physically Interact and Suggest Underlying Biology. Plos Genetics 7(1). Retrieved from http://www.plosgenetics.org/article/info%3Adoi%2F10.1371%2Fjournal.pgen.100… Wang, P. I., & Marcotte, E. M. (2010). It’s the machine that matters: Predicting gene function and phenotype from protein networks. Journal of proteomics, 73(11), 2277–2289. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/20637909 1000 Genomes Project Consortium. (2010). A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073. Retreived from: http://www.nature.com/nature/journal/v467/n7319/full/nature09534.html

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12-1pm Mon May 20 talk on ICT convergence by CTO of KT (Korea Telecom) in E-Quad B327
by Jennifer Rexford 17 May '13

17 May '13

Speaker: Dr. James Won-Ki Hong, CTO KT (Korea Telecom) Title: Shaping the Future Society with ICT Convergence Date: Monday, May 20, 2013 Time: 12:00 pm Room: E-Quad, Room B327 Host: Princeton EDGE Lab and Princeton KSA Abstract: Korea, also known as the nation of Psy, is one of the most dynamic countries in the world. Thanks to advanced information and communications infrastructures and unique culture, people in Korea are said to be living in the future of many other countries. It is this customer environment that facilitated the emergence of instant messaging and social platforms such as Kakaotalk and Line. It may be surprising to know that the entire country of Korea was actually devastated by the Korean War more than 60 years ago. Many factors drove Korea’s development for the past 60 years. Not only did its high education fever and speedy culture but also the government's strong will and drive on ICT (Information and Communications Technology) development and decisive investments by private sector businesses, such as KT (formerly Korea Telecom), contributed to the Korea’s development. In fact, KT led the deployment of the most advanced wireline/wireless Internet infrastructure in the world during the last few decades. Now, KT is continuing to shape the society we live in with further advances in ICT. To bring the future into reality, KT is taking on numerous challenging and interesting problems in ICT convergence areas. In this talk, we will provide an outline of Korea’s ICT status, KT’s efforts in infrastructure deployment, and peek into future lifestyles based on emerging technologies. We also offer opportunities for talented students and researchers to collaborate with KT.

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R Ranganath generals - time change to 10:30
by Melissa M. Lawson 16 May '13

16 May '13

The start time for Rajesh's generals on Tuesday May 21 has been moved to 10:30. Please note. thanks, Melissa

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Hanjun Kim FPO
by Melissa M. Lawson 16 May '13

16 May '13

Hanjun Kim will present his FPO on Wednesday May 22 at 9:30 AM in Room 302 (note room!) The members of his committee are: David August, advisor; Kai Li and David Wentzlaff (ELE), readers; JP Singh and Sharad Malik (ELE), nonreaders. A copy of his thesis, "ASAP: Automatic Speculative Acyclic Parallelization for Clusters", is on display in Room 310. Everyone is invited to attend his talk.

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R Ranganath generals
by Melissa M. Lawson 16 May '13

16 May '13

Rajesh Ranganath will present his research seminar/general exam on Tuesday May 21 at 10AM in Room 402. The members of his committee are: David Blei (advisor), Rob Schapire, and Philippe Rigollet (ORF). 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: An Adaptive Learning Rate for Stochastic Variational Inference Abstract: Stochastic variational inference finds good pos- terior approximations of probabilistic models with very large data sets. It optimizes the variational objective with stochastic optimization, following noisy estimates of the natural gradient. Operationally, stochastic inference iteratively subsamples from the data, analyzes the subsample, and updates parameters with a decreasing learning rate. However, the algorithm is sensitive to that rate, which usually requires hand-tuning to each application. We solve this problem by developing an adaptive learning rate for stochastic inference. Our method requires no tuning and is easily implemented with computations already made in the algorithm. We demonstrate our approach with latent Dirichlet allocation applied to three large text corpora. Inference with the adaptive learning rate converges faster and to a better approximation than the best settings of hand-tuned rates. This is joint work with Chong Wang, Dave Blei, and Eric Xing. Reading List: Textbook Chapters Pattern Recognition & Machine Learning, Chapters 8, 10, 11 (C. Bishop) Probability & Stochastics, Chapter 6 (E. Cinlar.) Artificial Intelligence: A Modern Approach 3rd edition, Chapters 17, 18, 20, 21 (Russel + Norvig) Papers: Integration of Early Physiological Responses Predicts Later Illness Severity in Preterm Infants [Sci Transl Med 8 September 2010] (S. Saria et al.) Learning individual and population level traits from Clinical Temporal data [NIPS 2010] (S. Saria et al.) {Posterior Predictive Assessment of Model Fitness Via Realized Discrepancies [Statistica Sinica 1996] (A. Gelman, X. Meng, H. Stern) Stochastic Variation Inference [JMLR to appear] (M. Hoffman et al) A Stochastic Gradient Method with an Exponential Convergence Rate for Strongly-Convex Optimization with Finite Training Sets. [NIPS 2012] (N. LeRoux et al) Bootstrap Methods for Standard Errors, Confidence Intervals, and Other Measures of Statistical Accuracy [Statistical Science 1986] (B. Efron, R. Tibishirani) The Indian Buffet Process: An Introduction and Review [JMLR 2011] (T. Griffiths, Z. Gharamani)

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V Yao generals
by Melissa M. Lawson 16 May '13

16 May '13

Vicky Yao will present her research seminar/general exam on Monday May 20 at 10AM in Room 402. The members of her committee are: Olga Troyanskaya (advisor), Mona Singh, and John Storey (MOL). 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: Integration of genomics data has already been shown to be helpful in improving our understanding of functional relationships between genes in a wide range of organisms, including yeast, worm, mouse, and human. As the quantity of these datasets increases, we are presented with the opportunity not only in improving functional predictions between genes, but also in attempting to attack this difficult problem of understanding functional relationships in tissue-specific contexts, with the eventual goal of developing a better understanding of alternative splicing and isoform expression in different tissues. We create tissue-specific functional networks in C. elegans by utilizing knowledge of already curated tissue-specific genes in our integration of available genomics data using Bayesian learning methods. By extending our tissue-specific functional predictions in genes to differentially spliced exons and leveraging known protein domain interaction data, we hope to be able to predict potential tissue-specific protein interactions. These predictions would be extremely useful for improving our understanding of tissue-specific processes. Reading list: Introduction to Machine Learning (second edition) 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 Bayesian Estimation Ch 17 Combining Multiple Learners Ch 19 Design and Analysis of Machine Learning Experiments Papers: Anders S, Huber W (2010) Differential expression analysis for sequence count data. Genome Biology 11:R106. Anders S, Reyes A, Huber W (2012) Detecting differential usage of exons from RNA-seq data. Genome Research 22: 2008-2017. Bossi A, Lehner B (2009) Tissue specificity and the human protein interaction network. Mol Syst Biol 5: 260. Buljan M, Chalancon G, Eustermann S, Wagner GP, Fuxreiter M, Bateman A, Babu MM (2012) Tissue-specific splicing of disordered segments that embed binding motifs rewires protein interaction networks. Mol Cell 46(6): 871-83. Chikina MD, Huttenhower C, Murphy CT, Troyanskaya OG (2009) Global Prediction of Tissue-Specific Gene Expression and Context-Dependent Gene Networks in Caenorhabditis elegans. PLoS Comput Biol 5(6): e1000417. Guan Y, Gorenshteyn D, Burmeister M, Wong AK, Schimenti JC, Handel MA, Bult CJ, Hibbs MA, Troyanskaya OG (2012) Tissue-specific functional networks for prioritizing phenotype and disease genes. PLoS Comput Biol 8(9): e1002694. Kim D, Pertea G, Trapnell C, Pimentel H, Kelley H, Salzberg SL (2013) TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biology 14:R36. Li Y, Breitling R, Snoek BL, van der Velde KJ, Swertz MA, Riksen J, Jansen RC, Kammenga JE (2010) Global Genetic Robustness of the Alternative Splicing Machinery in Caenorhabditis elegans . Genetics 186: 405-410. Merkin J, Russel C, Chen P, Burge CB (2012) Evolutionary Dynamics of Gene and Isoform Regulation in Mammalian Tissues. Science 338(6114): 1593-9. Wang ET, Sandberg R, Luo S, Khrebtukova I, Zhang L, Mayr C, Kingsmore SF, Schorth GP, Burge CB (2008) Alternative isoform regulation in human tissue transcriptomes. Nature 456: 470-476.

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Prof. Radu Teodorescu, Seminar today at 12:30 in CS402.
by Margaret Martonosi 14 May '13

14 May '13

Reminder that Prof. Radu Teodorescu will be giving a seminar today at 12:30 in CS402. > Designing Energy-Efficient Microprocessors in the Era of Unpredictable Transistors > > ABSTRACT: > Energy efficiency is now a principal design constraint across all computing markets - from supercomputers to smartphones. Performance growth in these markets can only be sustained going forward through significant improvements in the energy efficiency of computation. A very effective approach to improving microprocessor energy efficiency is to lower supply voltage to very close to the transistor's threshold voltage, into the so-called near-threshold region. Near-threshold operation reduces energy consumption by an order of magnitude but comes at the cost of reduced frequency, degraded reliability and increased parameter variability. > > In this talk I will describe hardware and software co-design techniques that address and alleviate the challenges of low-voltage computing, bringing it closer to commercial feasibility. These include techniques for reducing the effects of process and voltage variability and a new approach for runtime reduction of voltage margins prototyped in a state-of-the-art server processor. > > BIO: > Radu Teodorescu is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University where he leads the Computer Architecture Research Lab. He received his PhD from University of Illinois at Urbana-Champaign in 2008. His research interests include computer architecture with a focus on energy efficient microprocessor design and the impact of technology scaling on reliability and process variability. He received a CAREER award from the National Science Foundation in 2012, the W. J. Poppelbaum award from University of Illinois in 2008 and an Intel Fellowship in 2007. Margaret Martonosi Hugh Trumbull Adams '35 Professor of Computer Science Princeton University http://www.princeton.edu/~mrm mrm(a)princeton.edu

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M Homilius general exam
by Melissa M. Lawson 10 May '13

10 May '13

Max Homilius will present his research seminar/general exam on Friday March 17 at 10AM in Room 302 (note room!). The members of his committee are: Olga Troyanskaya (advisor), Mona Singh, and Tom Funkhouser. 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. ---------------------- Abstract ======== Classical pharmacological approaches for the identification of new drug candidates often follow a one disease - one target - one drug paradigm. At the same time, there is little knowledge about the specific mode of action for many compounds and the principles underlying interactions between multiple drugs. I developed a machine learning approach for the prediction of synergistic drug pairs for fungicidal and anti-cancer compounds. This approach takes into account the effects of a compound on multiple target genes that are part of complex biological network, and combines experimental results from different large-scale studies to improve predictions. In addition, I constructed functional networks specific to non-steroidal anti-inflammatory drugs. Measures of differential expression and coexpression of genes after drug treatment were reconciled with these networks and are predictive of the mode of action of the administered drug. Reading list ========== [1] Ethem Alpaydin (2004). Introduction to Machine Learning. First edition. MIT Press. 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 [2] Aryee, M. J., Gutiérrez-Pabello, J. a, Kramnik, I., Maiti, T., & Quackenbush, J. (2009). An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation). BMC bioinformatics, 10, 409. [3] Cokol, M., Chua, H. N., Tasan, M., Mutlu, B., Weinstein, Z. B., Suzuki, Y., Nergiz, M. E., et al. (2011). Systematic exploration of synergistic drug pairs. Molecular systems biology, 7(544), 544. [4] Dawson, J. a, & Kendziorski, C. (2012). An empirical Bayesian approach for identifying differential coexpression in high-throughput experiments. Biometrics, 68(2), 455–65. [5] Grosser, T., Fries, S., & FitzGerald, G. (2006). Biological basis for the cardiovascular consequences of COX-2 inhibition: therapeutic challenges and opportunities. Journal of Clinical Investigation, 116(1). [6] Jansen, G., Lee, A. Y., Epp, E., Fredette, A., Surprenant, J., Harcus, D., Scott, M., et al. (2009). Chemogenomic profiling predicts antifungal synergies. Molecular systems biology, 5(338), 338. [7] Laenen, G., Thorrez, L., Börnigen, D., & Moreau, Y. (2013). Finding the targets of a drug by integration of gene expression data with a protein interaction network. Molecular BioSystems. [8] Nitsch, D., Gonçalves, J. P., Ojeda, F., De Moor, B., & Moreau, Y. (2010). Candidate gene prioritization by network analysis of differential expression using machine learning approaches. BMC bioinformatics, 11(2007), 460. [9] Poirel, C. L., Rahman, A., Rodrigues, R. R., Krishnan, A., Addesa, J. R., & Murali, T. M. (2013). Reconciling differential gene expression data with molecular interaction networks. Bioinformatics, 29(5), 622–9. [10] Wong, A. K., Park, C. Y., Greene, C. S., Bongo, L. a, Guan, Y., & Troyanskaya, O. G. (2012). IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks. Nucleic acids research, 40, W484–90.

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J Fix general exam
by Melissa M. Lawson 10 May '13

10 May '13

Jordan Fix will present his research seminar/general exam on Thursday May 16 at 10AM in Room 402. The members of his committee are: David August (advisor), David Wentzlaff (ELE), and Andrew Appel. 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. ----------------------------- Abstract: The emergence of multicore architectures necessitates new techniques such as transactional memory (TM) to gain performance without burdening programmers. This talk presents the first evaluation of a hardware multi-threaded transactional (MTX) memory system and improves upon prior proposed designs. MTX enables more diverse parallelization schemes than traditional TM systems which restrict transactions to a single thread, limiting their applicability. Prior software-based MTX memory systems require extra book keeping threads and instructions to manage speculative memory, imposing more overhead than a hardware-based implementation. The proposed hardware MTX memory system employs new transactional instructions, adds new cache state, and is implemented on top of a snoopy MOESI cache coherence protocol, allowing transactions spanning multiple threads to determine their state by snooping coherence messages. Reading List: Textbooks: • J. L. Hennessy and D. A. Patterson. Computer Architecture: A Quantitative Approach. Morgan Kaufmann, San Francisco, CA, 1996 • A. W. Appel. Modern Compiler Implementation in ML. Cambridge University Press, 1998 Papers: • Maurice Herlihy and J. Eliot B. Moss. 1993. Transactional memory: architectural support for lock-free data structures. In Proceedings of the 20th annual international symposium on computer architecture (ISCA '93) • S. Gopal, T. Vijaykumar, J. Smith, and G. Sohi. 1998. Speculative Versioning Cache. In Proceedings of the 4th International Symposium on High-Performance Computer Architecture (HPCA '98). • Lance Hammond, Vicky Wong, Mike Chen, Brian D. Carlstrom, John D. Davis, Ben Hertzberg, Manohar K. Prabhu, Honggo Wijaya, Christos Kozyrakis, and Kunle Olukotun. 2004. Transactional Memory Coherence and Consistency. In Proceedings of the 31st annual international symposium on Computer architecture (ISCA '04). • Nir Shavit and Dan Touitou. 1995. Software transactional memory. In Proceedings of the fourteenth annual ACM symposium on Principles of distributed computing (PODC '95). • Arun Raman, Hanjun Kim, Thomas R. Mason, Thomas B. Jablin, and David I. August. 2010. Speculative parallelization using software multi-threaded transactions. In Proceedings of the fifteenth edition of ASPLOS on Architectural support for programming languages and operating systems (ASPLOS XV). • N. Vachharajani. Intelligent Speculation for Pipelined Multithreading. PhD thesis, Department of Computer Science, Princeton University, Princeton, New Jersey, United States, November 2008. • J. Greggory Steffan, Christopher B. Colohan, Antonia Zhai, and Todd C. Mowry. 2000. A scalable approach to thread-level speculation. In Proceedings of the 27th annual international symposium on Computer architecture (ISCA '00) • María Jesús Garzarán, Milos Prvulovic, José María Llabería, Víctor Viñals, Lawrence Rauchwerger, and Josep Torrellas. 2005. Tradeoffs in buffering speculative memory state for thread-level speculation in multiprocessors. ACM Transactions on Architecture and Code Optimization (TACO '05). • James R. Goodman. 1983. Using cache memory to reduce processor-memory traffic. In Proceedings of the 10th annual international symposium on Computer architecture (ISCA '83). • P. Sweazey and A. J. Smith. 1986. A class of compatible cache consistency protocols and their support by the IEEE futurebus. In Proceedings of the 13th annual international symposium on Computer architecture (ISCA '86).

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P Prabhu FPO
by Melissa M. Lawson 09 May '13

09 May '13

Prakash Prabhu will present his FPO on Thursday May 16 at 2PM in Room 402. The members of his committee are: David August, advisor; David Walker and JP Singh, readers; Kai Li and Margaret Martonosi, nonreaders. A copy of his thesis, "Semantic Language Extensions for Implicit Parallel Programming", is on display in Room 310. Everyone is invited to attend his talk.

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