- talks - lists.cs.princeton.edu

Fwd: Princeton Optimization Seminar, Bernard Chazelle, Thursday, Dec. 1, 4:30 PM, Sherrerd 101
by Nicole E. Wagenblast 28 Nov '16

28 Nov '16

----- Princeton Optimization Seminar ----- DATE: TODAY Thursday, December 1, 2016 TIME : 4:30PM LOCATION: Sherrerd Hall 101 SPEAKER: Bernard Chazelle , Princeton University TITLE: The Mathematics of Natural Algorithms Abstract: I will review some of the recent techniques we've used in our study of natural algorithms. These include Dirichlet series for matrix products, mean-field approximations in opinion dynamics, graph sequence grammars, anti-mixing methods for iterated learning, and tools for renormalizing network-based dynamical systems. The talk will be self-contained and non-technical. Bio: Bernard Chazelle is Eugene Higgins Professor of Computer Science at Princeton University, where he has been on the faculty since 1986. His current research focuses on the “algorithmic nature” of living systems. A professor at the Collège de France in Paris in recent years as well as a member of the Institute for Advanced Study in Princeton, he received his PhD in computer science from Yale University in 1980. The author of the book, "The Discrepancy Method," he is a fellow of the American Academy of Arts and Sciences, the European Academy of Sciences, the Association for Computing Machinery, and the recipients of three Best-Paper awards from SIAM. *** If you would like to subscribe to the mailing list of the optimization seminar series, please visit the link below or send an email with SUBSCRIBE opt-seminar in the body to listserv(a)lists.princeton.edu . https://lists.princeton.edu/cgi-bin/wa?SUBED1=opt-seminar&A=1

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Colloquium Speaker: Alexis Battle, Tuesday, Nov 29, 12:30pm
by Nicole E. Wagenblast 22 Nov '16

22 Nov '16

Colloquium Speaker Prof. Alexis Battle, Johns Hopkins University Tuesday, November 29, 2016 - 12:30pm Computer Science 105 Predicting the impact of rare regulatory variation The enormous increase in availability of full human genomic sequences presents great opportunity for understanding the impact of rare genetic variants. Based on current knowledge, however, we are still limited in our ability to interpret or predict regulatory consequences of rare variants. Association methods are inherently limited for variants observed in only one or a few individuals. Diverse genomic annotations such as growing resources of epigenetic data and have been shown to be informative regarding regulatory elements, but are only moderately predictive of impact for individual variants. The availability of personal RNA-seq and other cellular measurements for the same individuals with genome sequencing offers a new avenue for integrated methods for prioritizing rare functional variants. By considering informative genomic annotations along with molecular phenotyping, we are able to identify the regulatory impact of rare genetic variants largely excluded from previous analyses. We have evaluated the impact of rare regulatory variants using whole genome sequences and corresponding RNA-sequence in 44 different tissues samples from the GTEx project and report. We demonstrate that rare variants in promoter elements, conserved regions, and variants that affect splicing are associated with extreme changes in gene expression across multiple tissues. Additionally, we have developed a Bayesian machine learning approach that integrates whole genome sequencing with RNA-seq data from the same individual, leveraging gene expression levels along with diverse genomic annotations and performing joint inference to identify likely functional rare regulatory variants. We have applied this model to the GTEx data to prioritize the most likely functional rare regulatory variants for each individual. We demonstrate that integrative models perform better than predictions from DNA-sequencing or RNA-sequencing alone. Our probabilistic model of rare regulatory genetic variants offers potential for identifying deleterious regulatory variants from individual genomes. Alexis Battle’s research focuses on unraveling the impact of genetics on the human health, using machine learning and probabilistic methods to analyze large scale genomic data. She is interested in applications to personal genomics, genetics of gene expression, and gene networks in disease, leveraging diverse data to infer more comprehensive models of genetic effects on the cell. She earned her Ph.D. and Masters in Computer Science in 2014 from Stanford University in 2014, where she also received her Bachelors in Symbolic Systems (2003). Alexis also spent several years in industry as a member of the technical staff at Google. Prior to joining Hopkins, Alexis spent a year as a postdoc with Jonathan Pritchard with HHMI and the Genetics Department at Stanford. She joined JHU in July 2014.

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Colloquium Speaker Chinmay Kulkarni, Today- 12:30pm
by Nicole E. Wagenblast 17 Nov '16

17 Nov '16

Colloquium Speaker Chinmay Kulkarni, Carnegie Mellon University Thursday, November 17, 2016 - 12:30pm Computer Science 105 Structuring Peer Interactions for Learning Expertise at Scale Learning with peers helps students reflect, generalize knowledge and apply it more successfully to new problems. How can we scale successful peer learning from the controlled environment of the small classroom to the wild, massive scale of online classes? In his talk, Chinmay Kulkarni will introduce computational systems that structure peer learning at massive scale. These systems demonstrate how insights from educational theory can be distilled into interfaces that scale teaching to thousands of learners. For instance, he will describe how students using his PeerStudio obtain improvement-oriented feedback on open-ended work in just twenty minutes at any time of day, enabling them to revise and gain mastery. Similarly, students use the Talkabout system to engage in globe-spanning discussions that improve learning through diversity. This talk will be a reflection of the growing maturity of research in this area, from articulating design patterns, to randomized controlled experiments at scale, to studies of long-term effects on learners and learning. Finally, Kulkarni hopes to reflect on how the large scale and diversity of online classes can enrich learning at universities, and his ongoing efforts to prepare engineering students for tomorrow's grand challenges. Chinmay Kulkarni is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. His research group investigates how new software and pedagogical systems can leverage peer processes at massive scale. Over the past three years, his work has been used by more than 100,000 learners online, in classes taught by more than twenty universities, including Harvard. In his idle time, Chinmay is either making coffee, drinking coffee, or clicking "View Source" on web pages he visits.

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Colloquium Speaker Chinmay Kulkarni, Thurs. Nov 17th- 12:30pm- reminder
by Nicole E. Wagenblast 16 Nov '16

16 Nov '16

Colloquium Speaker Chinmay Kulkarni, Carnegie Mellon University Thursday, November 17, 2016 - 12:30pm Computer Science 105 Structuring Peer Interactions for Learning Expertise at Scale Learning with peers helps students reflect, generalize knowledge and apply it more successfully to new problems. How can we scale successful peer learning from the controlled environment of the small classroom to the wild, massive scale of online classes? In his talk, Chinmay Kulkarni will introduce computational systems that structure peer learning at massive scale. These systems demonstrate how insights from educational theory can be distilled into interfaces that scale teaching to thousands of learners. For instance, he will describe how students using his PeerStudio obtain improvement-oriented feedback on open-ended work in just twenty minutes at any time of day, enabling them to revise and gain mastery. Similarly, students use the Talkabout system to engage in globe-spanning discussions that improve learning through diversity. This talk will be a reflection of the growing maturity of research in this area, from articulating design patterns, to randomized controlled experiments at scale, to studies of long-term effects on learners and learning. Finally, Kulkarni hopes to reflect on how the large scale and diversity of online classes can enrich learning at universities, and his ongoing efforts to prepare engineering students for tomorrow's grand challenges. Chinmay Kulkarni is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. His research group investigates how new software and pedagogical systems can leverage peer processes at massive scale. Over the past three years, his work has been used by more than 100,000 learners online, in classes taught by more than twenty universities, including Harvard. In his idle time, Chinmay is either making coffee, drinking coffee, or clicking "View Source" on web pages he visits.

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Colloquium Speaker Chinmay Kulkarni, Thurs. Nov 17th- 12:30pm
by Nicole E. Wagenblast 11 Nov '16

11 Nov '16

Colloquium Speaker Chinmay Kulkarni, Carnegie Mellon University Thursday, November 17, 2016 - 12:30pm Computer Science 105 Structuring Peer Interactions for Learning Expertise at Scale Learning with peers helps students reflect, generalize knowledge and apply it more successfully to new problems. How can we scale successful peer learning from the controlled environment of the small classroom to the wild, massive scale of online classes? In his talk, Chinmay Kulkarni will introduce computational systems that structure peer learning at massive scale. These systems demonstrate how insights from educational theory can be distilled into interfaces that scale teaching to thousands of learners. For instance, he will describe how students using his PeerStudio obtain improvement-oriented feedback on open-ended work in just twenty minutes at any time of day, enabling them to revise and gain mastery. Similarly, students use the Talkabout system to engage in globe-spanning discussions that improve learning through diversity. This talk will be a reflection of the growing maturity of research in this area, from articulating design patterns, to randomized controlled experiments at scale, to studies of long-term effects on learners and learning. Finally, Kulkarni hopes to reflect on how the large scale and diversity of online classes can enrich learning at universities, and his ongoing efforts to prepare engineering students for tomorrow's grand challenges. Chinmay Kulkarni is an Assistant Professor in the Human-Computer Interaction Institute at Carnegie Mellon University. His research group investigates how new software and pedagogical systems can leverage peer processes at massive scale. Over the past three years, his work has been used by more than 100,000 learners online, in classes taught by more than twenty universities, including Harvard. In his idle time, Chinmay is either making coffee, drinking coffee, or clicking "View Source" on web pages he visits.

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Colloquium Speaker Uri Keich, Thurs Nov 10, 12:30pm
by Nicole E. Wagenblast 10 Nov '16

10 Nov '16

Colloquium Speaker Prof. Uri Keich, University of Sydney Thursday, November 10, 12:30pm Computer Science 105 Controlling the rate of false discoveries in tandem mass spectrum identifications A typical shotgun proteomics experiment produces thousands of tandem mass spectra, each of which can be tentatively assigned a corresponding peptide by using a database search procedure that looks for a peptide-spectrum match (PSM) that optimizes the score assigned to a matched pair. Some of the resulting PSMs will be correct while others will be false, and we have no way to verify which is which. The statistical problem we face is of controlling the false discovery rate (FDR), or the expected proportion of false PSMs among all reported pairings. While there is a rich statistical literature on controlling the FDR in the multiple hypothesis testing context, controlling the FDR in the PSM context is mostly done through the "home grown" method called target-decoy competition (TDC). After a brief introduction to the problem of tandem mass spectrum identification we will explore the reasons why the mass spec community has been using this non-standard approach to controlling the FDR. We will then discuss how calibration can increase the number of correct discoveries and offer an alternative method for controlling the FDR in the presence of calibrated scores. We will conclude by arguing that our analysis extends to a more general setup than the mass spectrum identification problem. Joint work with Bill Noble (University of Washington)

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Colloquium Speaker Uri Keich, Thurs Nov 10, 12:30pm- reminder
by Nicole E. Wagenblast 09 Nov '16

09 Nov '16

Colloquium Speaker Prof. Uri Keich, University of Sydney Thursday, November 10, 12:30pm Computer Science 105 Controlling the rate of false discoveries in tandem mass spectrum identifications A typical shotgun proteomics experiment produces thousands of tandem mass spectra, each of which can be tentatively assigned a corresponding peptide by using a database search procedure that looks for a peptide-spectrum match (PSM) that optimizes the score assigned to a matched pair. Some of the resulting PSMs will be correct while others will be false, and we have no way to verify which is which. The statistical problem we face is of controlling the false discovery rate (FDR), or the expected proportion of false PSMs among all reported pairings. While there is a rich statistical literature on controlling the FDR in the multiple hypothesis testing context, controlling the FDR in the PSM context is mostly done through the "home grown" method called target-decoy competition (TDC). After a brief introduction to the problem of tandem mass spectrum identification we will explore the reasons why the mass spec community has been using this non-standard approach to controlling the FDR. We will then discuss how calibration can increase the number of correct discoveries and offer an alternative method for controlling the FDR in the presence of calibrated scores. We will conclude by arguing that our analysis extends to a more general setup than the mass spectrum identification problem. Joint work with Bill Noble (University of Washington)

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12pm Mon Nov 14 talk on "Efficient fault-tolerant quantum computing" by Martin Suchara in Bowen Auditorium
by Jennifer Rexford 07 Nov '16

07 Nov '16

> From: "Barbara J. Fruhling" <bfruhlin(a)Princeton.EDU> > Subject: EE Seminar - November 14, 2016, 12:00 pm, Bowen Auditorium - Dr. Martin Suchara > Date: November 7, 2016 at 1:42:17 PM EST > To: ee-seminar(a)Princeton.EDU > > > > Speaker: Dr. Martin Suchara > AT&T Labs Research > Title: Efficient Fault-Tolerant Quantum Computing > Date: Monday, November 14, 2016 > Time: 12:00 p.m. > Room: Bowen Auditorium > Light lunch served at 11:30 > Host: Prof. Jeff Thompson > > Abstract: Quantum error correction presents some of the most significant and interesting challenges that must be resolved before building an efficient quantum computer. Quantum error correcting codes allow to successfully run quantum algorithms on unreliable quantum hardware. Because quantum hardware suffers from errors such as decoherence, leakage or qubit loss, and these errors corrupt delicate quantum states rather than binary information, the known error correction techniques are complex and have a high overhead. > > In my talk I first introduce the two main families of quantum error correcting codes and quantify their overhead using specific examples of algorithms and hardware technologies. Then I describe several new techniques that I developed to reduce this overhead. For example, the maximum likelihood decoder (MLD) is an efficient algorithm that finds the recovery operation that maximizes the probability of a successful error correction given the observed error syndrome. Numerical simulations of the MLD algorithm for physical error rates around 10% showed a 100 fold reduction of the logical error probability compared to earlier techniques. I also show new designs of error correcting codes that are tailored to work more efficiently with the constraints of specific physical technologies. > > Bio: Martin Suchara is a Principal Inventive Scientist at AT&T Labs Research since 2015. Prior to joining AT&T he was a Postdoctoral Scholar in the quantum computing group at IBM T. J. Watson Research Center. His work focuses on making computation with quantum computers more efficient and reliable. He developed new quantum error correcting codes that improve error decoding efficiency. Martin received his PhD from the Computer Science department at Princeton University and postdoctoral training from UC Berkeley. Between 2011 and 2013 he coordinated the work of a small group of postdocs and students on the IARPA Quantum Computer Science Program and delivered the results to the Program Manager. Martin is the recipient of the Best Student Paper Award at ACM Sigmetrics 2011.

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Colloquium Speaker Uri Keich, Thurs Nov 10, 12:30pm
by Nicole E. Wagenblast 07 Nov '16

07 Nov '16

Colloquium Speaker Prof. Uri Keich, University of Sydney Thursday, November 10, 12:30pm Computer Science 105 Controlling the rate of false discoveries in tandem mass spectrum identifications A typical shotgun proteomics experiment produces thousands of tandem mass spectra, each of which can be tentatively assigned a corresponding peptide by using a database search procedure that looks for a peptide-spectrum match (PSM) that optimizes the score assigned to a matched pair. Some of the resulting PSMs will be correct while others will be false, and we have no way to verify which is which. The statistical problem we face is of controlling the false discovery rate (FDR), or the expected proportion of false PSMs among all reported pairings. While there is a rich statistical literature on controlling the FDR in the multiple hypothesis testing context, controlling the FDR in the PSM context is mostly done through the "home grown" method called target-decoy competition (TDC). After a brief introduction to the problem of tandem mass spectrum identification we will explore the reasons why the mass spec community has been using this non-standard approach to controlling the FDR. We will then discuss how calibration can increase the number of correct discoveries and offer an alternative method for controlling the FDR in the presence of calibrated scores. We will conclude by arguing that our analysis extends to a more general setup than the mass spectrum identification problem. Joint work with Bill Noble (University of Washington)

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Jordan Fix will present his Pre-FPO on Monday, November 7th, 2016 at 4pm in CS 401
by Nicki Gotsis 07 Nov '16

07 Nov '16

Jordan Fix will present his Pre-FPO on Monday, November 7th, 2016 at 4pm in CS 401. The members of his committee are: Advisor: David August Non-readers: David Wentzlaff, Margaret Martonosi Readers: Andrew Appel, David Callahan (Facebook) Everyone is invited to attend his talk. Abstract: Speculation with transactional memory (TM) systems helps programmers and compilers produce profitable thread-level parallel programs. Prior work shows that supporting transactions that span multiple threads, rather than requiring transactions be contained within a single thread, enables new types of speculative parallelization techniques for both programmers and parallelizing compilers. The only prior TM systems with this multithreaded transactional (MTX) support are software-based TM (STM) systems. Unfortunately, these systems are notorious for their high overheads when validating speculative memory operations, which often make otherwise good parallelization unprofitable. Thus, parallelized programs using these prior STM systems with MTX support were forced to overcome this problem through significant manual effort by the programmer to optimize away most of this speculation validation. Unfortunately, compiler technology and static analyses have not proven strong enough to relieve the programmer of this burden. This thesis presents the first complete design and implementation of a hardware TM (HTM) system with MTX support. This system provides low overhead speculation validation, enabling the aforementioned new types of speculative parallelization techniques to achieve good performance even with high amounts of speculation validation. Profitable parallelization of complex programs can be achieved even with maximal speculation validation of every load and store inside transactions of tens to hundreds of millions of instructions, thus making parallelization less laborious and more feasible for both programmers and compilers. In addition to supporting MTXs, transactions in this system are more resilient than in many other prior works. Transactions avoid false misspeculation due to branch misprediction; provide for large read and write sets that commit in a lazy fashion; and allow for context switches (and thread migration) due to common operations such as preemption or virtual memory management. This allows the system to accommodate the large and long running transactions that are needed to parallelize complex programs. Across 6 SPEC benchmarks a geomean speedup of 2.11X over sequential execution is achieved on a multicore machine with 4 cores, while increasing energy usage by 10%.

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