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IMA Newsletter #424

February 2012

2011-2012 Program

See http://www.ima.umn.edu/2011-2012/ for a full description of the 2011-2012 program on Mathematics of Information.

IMA Events

IMA Workshop

Second Abel Conference: A Celebration of John Milnor

January 30 - February 1, 2012

Organizers: Alejandro Adem (University of British Columbia), Geir Ellingsrud (University of Oslo), Michael J. Hopkins (Harvard University), Robion Kirby (University of California), James Stasheff (University of Pennsylvania)

Summit on 2007-08 Thematic Year on the Mathematics of Molecular and Cellular Biology

February 5-7, 2012

Organizers: Fadil Santosa (University of Minnesota Twin Cities), De Witt L. Sumners (Florida State University)

IMA Annual Program Year Workshop

Group Testing Designs, Algorithms, and Applications to Biology

February 13-17, 2012

Organizers: Yaniv Erlich (Whitehead Institute for Biomedical Research), Anna Gilbert (AT&T Laboratories - Research), Olgica Milenkovic (University of Illinois at Urbana-Champaign), Ely Porat (Bar-Ilan University)

IMA Annual Program Year Workshop

Network Links: Connecting Social, Communication, and Biological Network Analysis

February 27 - March 2, 2012

Organizers: Mathieu Blanchette (McGill University), Graham Cormode (AT&T Laboratories - Research), Natasha Przulj (Imperial College London), Ben J. Raphael (Brown University), S. Cenk Sahinalp (Simon Fraser University), Eric van den Berg (Telcordia)
Schedule

Wednesday, February 1

8:30am-9:00am CoffeeKeller Hall 3-176 SW1.30-2.1.12
9:00am-10:00am The Milnor Conjectures and Quadratic FormsParimala Raman (Emory University)Keller Hall 3-180 SW1.30-2.1.12
10:00am-10:30am Coffee breakKeller Hall 3-176 SW1.30-2.1.12
10:30am-11:30am A solution to the Arf-Kervaire invariant problemDouglas Conner Ravenel (University of Rochester)Keller Hall 3-180 SW1.30-2.1.12
11:30am-11:40am Break SW1.30-2.1.12
11:40am-12:40pm On the homology of Lie groups made discreteFabien Morel (Ludwig-Maximilians-Universität München)Keller Hall 3-180 SW1.30-2.1.12
12:40pm-1:00pm Closing remarksKeller Hall 3-180 SW1.30-2.1.12
2:30pm-3:00pm Coffee breakLind Hall 400

Thursday, February 2

2:30pm-3:00pm Coffee breakLind Hall 400
3:00pm-4:00pm Computational Knot Theory with KnotPlotRobert Glenn Scharein (Hypnagogic Software)Lind Hall 305 PS

Friday, February 3

2:30pm-3:00pm Coffee breakLind Hall 400

Monday, February 6

2:30pm-3:00pm Coffee breakLind Hall 400

Tuesday, February 7

2:30pm-3:00pm Coffee breakLind Hall 400
3:00pm-4:00pm TBDArthur Szlam (University of Minnesota)Lind Hall 305 PS

Wednesday, February 8

2:30pm-3:00pm Coffee breakLind Hall 400

Thursday, February 9

2:30pm-3:00pm Coffee breakLind Hall 400

Friday, February 10

2:30pm-3:00pm Coffee breakLind Hall 400

Monday, February 13

9:00am-9:45am Coffee and RegistrationKeller Hall 3-176 W2.13-17.12
9:45am-10:00am Welcome and IntroductionKeller Hall 3-180 W2.13-17.12
10:00am-11:00am Tutorial: Cost effective sequencing of rare genetic variationsYaniv Erlich (Whitehead Institute for Biomedical Research)Keller Hall 3-180 W2.13-17.12
11:00am-11:15am BreakKeller Hall 3-176 W2.13-17.12
11:15am-12:15pm Tutorial: Sparse signal recoveryAnna Gilbert (AT&T Laboratories - Research)Keller Hall 3-180 W2.13-17.12
12:15pm-2:00pm Lunch W2.13-17.12
2:00pm-3:00pm Identification of rare alleles and their carriers using compressed se(que)nsingNoam Shental (Open University of Israel)Keller Hall 3-180 W2.13-17.12
3:00pm-3:15pm BreakKeller Hall 3-176 W2.13-17.12
3:15pm-3:45pm Mining Rare Human Variations using Combinatorial PoolingDina Esposito (Whitehead Institute for Biomedical Research)Keller Hall 3-180 W2.13-17.12
3:45pm-4:15pm RNA Structure Characterization from High-Throughput Chemical Mapping ExperimentsSharon Aviran (University of California, Berkeley)Keller Hall 3-180 W2.13-17.12
4:15pm-4:30pm BreakKeller Hall 3-176 W2.13-17.12
4:30pm-5:30pm RUMP sessionOlgica Milenkovic (University of Illinois at Urbana-Champaign)Keller Hall 3-180 W2.13-17.12
6:30pm-8:00pm Social Hour at the Campus Club
403 Coffman Memorial Union Map
Campus Club W2.13-17.12

Tuesday, February 14

8:30am-9:00am CoffeeKeller Hall 3-176 W2.13-17.12
9:00am-10:00am Tutorial: Group Testing and Coding TheoryAtri Rudra (University at Buffalo (SUNY))Keller Hall 3-180 W2.13-17.12
10:00am-10:15am Group PhotoKeller Hall 3-180 W2.13-17.12
10:15am-11:15am Network topology as a source of biological informationNatasha Przulj (Imperial College London)Keller Hall 3-180 W2.13-17.12
11:15am-11:30am BreakKeller Hall 3-176 W2.13-17.12
11:30am-12:30pm Superimposed Codes and Designs for Group Testing ModelsVyacheslav V. Rykov (University of Nebraska)Keller Hall 3-180 W2.13-17.12
12:30pm-2:00pm Lunch W2.13-17.12
2:00pm-3:00pm Testing Boolean functionsAlex Samorodnitsky (Hebrew University)Keller Hall 3-180 W2.13-17.12
3:00pm-3:15pm Coffee BreakKeller Hall 3-176 W2.13-17.12
3:15pm-3:45pm Combinatorial Pooling Enables Selective Sequencing of the Barley Gene SpaceStefano Lonardi (University of California, Riverside)Keller Hall 3-180 W2.13-17.12
3:45pm-4:15pm Superimposed codesZoltan Furedi (Hungarian Academy of Sciences (MTA))Keller Hall 3-180 W2.13-17.12
4:15pm-5:30pm Poster Session and ReceptionLind Hall 400 W2.13-17.12
Poster - Finding one of m defective elementsChristian Deppe (Universität Bielefeld)
Poster - Upgraded Separate Testing of Inputs in Compressive SensingMikhail B Malyutov (Northeastern University)

Wednesday, February 15

8:30am-9:00am CoffeeKeller Hall 3-176 W2.13-17.12
9:00am-10:00am Shifted Transversal Design Smart-pooling: increasing sensitivity, specificity and efficiency in high-throughput biologyNicolas Thierry-Mieg (Centre National de la Recherche Scientifique (CNRS))Keller Hall 3-180 W2.13-17.12
10:00am-10:15am BreakKeller Hall 3-176 W2.13-17.12
10:15am-11:15am Weighted Pooling - Simple and Effective Techniques for Pooled High Throughput Sequencing DesignDavid Golan (Tel Aviv University)Keller Hall 3-180 W2.13-17.12
11:15am-11:30am BreakKeller Hall 3-176 W2.13-17.12
11:30am-12:30pm Genomic Privacy and the Limits of Individual Detection in a Pool Sriram Sankararaman (Harvard Medical School)Keller Hall 3-180 W2.13-17.12
12:30pm-2:00pm Lunch W2.13-17.12
2:00pm-3:00pm Superselectors: Efficient Constructions and ApplicationsFerdinando Cicalese (Università di Salerno)Keller Hall 3-180 W2.13-17.12
3:00pm-3:15pm Coffee BreakKeller Hall 3-176 W2.13-17.12
3:15pm-3:45pm Probabilistic and combinatorial models for quantized group testingOlgica Milenkovic (University of Illinois at Urbana-Champaign)Keller Hall 3-180 W2.13-17.12
3:45pm-4:15pm Improved Constructions for Non-adaptive Threshold Group TestingMahdi Cheraghchi (Carnegie Mellon University)Keller Hall 3-180 W2.13-17.12
4:15pm-4:30pm Break W2.13-17.12
4:30pm-5:30pm RUMP session Yaniv Erlich (Whitehead Institute for Biomedical Research)Keller Hall 3-180 W2.13-17.12

Thursday, February 16

8:30am-9:00am CoffeeKeller Hall 3-176 W2.13-17.12
9:00am-10:00am Tutorial: The Data Stream ModelDavid P. Woodruff (IBM Research Division)Keller Hall 3-180 W2.13-17.12
10:00am-10:15am BreakKeller Hall 3-176 W2.13-17.12
10:15am-11:15am Sparser Johnson-Lindenstrauss TransformsJelani Nelson (Princeton University)Keller Hall 3-180 W2.13-17.12
11:15am-11:30am Break W2.13-17.12
11:30am-12:30pm Length Reduction via PolynomialsAmihood Amir (Bar-Ilan University)Keller Hall 3-180 W2.13-17.12
12:30pm-2:00pm Lunch W2.13-17.12
2:00pm-3:00pm Reconstruction of bacterial communities using sparse representationOr Zuk (Broad Institute)Keller Hall 3-180 W2.13-17.12
3:00pm-3:15pm BreakKeller Hall 3-176 W2.13-17.12
3:15pm-3:45pm TBDMikhail B Malyutov (Northeastern University)Keller Hall 3-180 W2.13-17.12
3:45pm-4:15pm A dynamic and quantitative protein interaction network regulating angiogenesisSylvie Ricard-Blum (Université Claude-Bernard (Lyon I))Keller Hall 3-180 W2.13-17.12
4:15pm-4:30pm BreakKeller Hall 3-176 W2.13-17.12
4:30pm-5:30pm RUMP session Ely Porat (Bar-Ilan University)Keller Hall 3-180 W2.13-17.12

Friday, February 17

8:30am-9:00am CoffeeKeller Hall 3-176 W2.13-17.12
9:00am-10:00am Streaming algorithms for approximating the length of the longest increasing subsequenceAnna Gal (University of Texas at Austin)Keller Hall 3-180 W2.13-17.12
10:00am-10:15am BreakKeller Hall 3-176 W2.13-17.12
10:15am-11:15am From screening clone libraries to detecting biological agentsAlexander Schliep (Rutgers University)Keller Hall 3-180 W2.13-17.12
11:15am-11:30am BreakKeller Hall 3-176 W2.13-17.12
11:30am-12:30pm A Group Testing Approach to Corruption Localizing HashingAnnalisa De Bonis (Università di Salerno)Keller Hall 3-180 W2.13-17.12

Monday, February 20

2:30pm-3:00pm Coffee breakLind Hall 400

Tuesday, February 21

2:30pm-3:00pm Coffee breakLind Hall 400

Wednesday, February 22

2:30pm-3:00pm Coffee breakLind Hall 400

Thursday, February 23

2:30pm-3:00pm Coffee breakLind Hall 400

Friday, February 24

1:25pm-2:25pm Computing the demagnetizing field in micromagnetics with periodic boundariesMichael J. Donahue (National Institute of Standards and Technology)Lind Hall 305 IPS
2:30pm-3:00pm Coffee breakLind Hall 400

Monday, February 27

Tuesday, February 28

Wednesday, February 29

2:30pm-3:00pm Coffee breakLind Hall 400

Thursday, March 1

2:30pm-3:00pm Coffee breakLind Hall 400

Friday, March 2

2:30pm-3:00pm Coffee breakLind Hall 400
Abstracts
Amihood Amir (Bar-Ilan University) Length Reduction via Polynomials
Abstract: Efficient handling of sparse data is a key challenge in Computer Science. Binary convolutions, such as the Fast Fourier Transform or theWalsh Transform are a useful tool in many applications and are efficiently solved. In the last decade, several problems required efficient solution of sparse binary convolutions.

Both randomized and deterministic algorithms were developed for efficiently computing the sparse FFT. The key operation in all these algorithms was length reduction. The sparse data is mapped into small vectors that preserve the convolution result. The reduction method used to-date was the modulo function since it preserves location (of the ”1” bits) up to cyclic shift.

In this paper we present a new method for length reduction - polynomials. We show that this method allows a faster deterministic computation of the sparse FFT than currently known in the literature. It also enables the development of an efficient algorithm for computing the binary sparse Walsh Transform. To our knowledge, this is the first such algorithm in the literature.

(Joint work with Oren Kappah, Ely Porat, and Amir Rothschild)
Sharon Aviran (University of California, Berkeley) RNA Structure Characterization from High-Throughput Chemical Mapping Experiments
Abstract: New regulatory roles continue to emerge for both natural and engineered noncoding RNAs, many of which have specific secondary and tertiary structures essential to their function. This highlights a growing need to develop technologies that enable rapid and accurate characterization of structural features within complex RNA populations. Yet, available structure characterization techniques that are reliable are also vastly limited by technological constraints, while the accuracy of popular computational methods is generally poor. These limitations thus pose a major barrier to the comprehensive determination of structure from sequence and thereby to the development of mechanistic understanding of transcriptome dynamics. To address this need, we have recently developed a high-throughput structure characterization technique, called SHAPE-Seq, which simultaneously measures quantitative, single nucleotide-resolution, secondary and tertiary structural information for hundreds of RNA molecules of arbitrary sequence. SHAPE-Seq combines selective 2’-hydroxyl acylation analyzed by primer extension (SHAPE) chemical mapping with multiplexed paired-end deep sequencing of primer extension products. This generates millions of sequencing reads, which are then analyzed using a fully automated data analysis pipeline. Previous bioinformatics methods, in contrast, are laborious, heuristic, and expert-based, and thus prohibit high-throughput chemical mapping.

In this talk, I will review recent developments in experimental RNA structure characterization as well as advances in sequencing technologies. I will then describe the SHAPE-Seq technique, focusing on its automated data analysis method, which relies on a novel probabilistic model of a SHAPE-Seq experiment, adjoined by a rigorous maximum likelihood estimation framework. I will demonstrate the accuracy and simplicity of our approach as well as its applicability to a general class of chemical mapping techniques and to more traditional SHAPE experiments that use capillary electrophoresis to identify and quantify primer extension products.

This is joint work with Lior Pachter, Julius Lucks, Stefanie Mortimer, Shujun Luo, Cole Trapnell, Gary Schroth, Jennifer Doudna and Adam Arkin.
Mahdi Cheraghchi (Carnegie Mellon University) Improved Constructions for Non-adaptive Threshold Group Testing
Abstract: The basic goal in combinatorial group testing is to identify a set of up to d defective items within a large population of size n >> d using a pooling strategy. Namely, the items can be grouped together in pools, and a single measurement would reveal whether there are one or more defectives in the pool. The threshold model is a generalization of this idea where a measurement returns positive if the number of defectives in the pool exceeds a fixed threshold u, negative if this number is below a fixed lower threshold L 0.
Ferdinando Cicalese (Università di Salerno) Superselectors: Efficient Constructions and Applications
Abstract: Superselectors subsume several important combinatorial structures used in the past few years to solve problems in group testing, compressed sensing, multi-channel conflict resolution and data security. We prove close upper and lower bounds on the size of superselectors and we provide efficient algorithms for their constructions. Albeit our bounds are very general, when they are instantiated on the combinatorial structures that are particular cases of superselectors (e.g., $(p,k,n)$-selectors cite{DGV}, $(d,ell)$-list-disjunct matrices, $MUT_k(r)$-families, $FUT(k, alpha)$-families, etc.) they match the best known bounds in terms of size of the structures (the relevant parameter in the applications). For appropriate values of parameters, our results also provide the first efficient deterministic algorithms for the construction of such structures.
Annalisa De Bonis (Università di Salerno) A Group Testing Approach to Corruption Localizing Hashing
Abstract: Efficient detection of integrity violations is crucial for the reliability of both data at rest and data in transit. In addition to detecting corruptions, it is often desirable to have the capability of obtaining information about the location of corrupted data blocks. While ideally one would want to always find all changes in the corrupted data, in practice this capability may be expensive, and one may be content with localizing or finding a superset of any changes. Corruption-localizing hashing is a cryptographic primitive that enhances collision-intractable hash functions thus improving the detection property of these functions into a suitable localization property. Besides allowing detection of changes in the input data, corruption-localizing hash schemes also obtain some superset of the changes location, where the accuracy of this superset with respect to the actual changes is a metric of special interest, called localization factor. In this talk we will address the problem of designing corruption-localizing hash schemes with reduced localization factor and with small time and storage complexity. We will show that this problem corresponds to a particular variant of nonadaptive group testing, and illustrate a construction technique based on superimposed codes. This group testing approach allowed to obtain corruption-localizing hash schemes that greatly improve on previous constructions. In particular, we will present a corruption-localizing hash scheme that achieves an arbitrarily small localization factor while only incurring a sublinear time and storage complexity.
Christian Deppe (Universität Bielefeld) Poster - Finding one of m defective elements
Abstract: In contrast to the classical goal of group testing we want to find onw defective elements of $D$ defective elements. We examine four different test functions. We give adaptive strategies and lower bounds for the number of tests. We treat the cases if the number of defectives are known and if the number of defectives are bounded.
Michael J. Donahue (National Institute of Standards and Technology) Computing the demagnetizing field in micromagnetics with periodic boundaries
Abstract: Micromagnetics is a classical model of magnetism in magnetic materials, operative at the nanometer length scale. Typical micromagnetic simulations model magnetic parts of dimensions ranging from tens of nanometers up to a few micrometers. The most computationally expensive portion of a micromagnetic simulation is the evaluation of the long-range self-magnetostatic (aka dipole or demagnetizing) field. In this talk I will provide some history of micromagnetics at NIST, and discuss in detail some of the numerical and computational challenges involved in a fast, accurate method for computing the demagnetizing field in a simulation with periodic boundaries.

Michael Donahue is a mathematician in the Applied and Computational Mathematics Division at the National Institute of Standards and Technology (NIST) in Gaithersburg, Maryland, where he does research on micromagnetics and leads development of the OOMMF public domain micromagnetics package. Prior to joining NIST, he was an industrial postdoctoral research associate at the IMA, working in conjunction with Siemens Corporate Research on artificial neural networks and computer vision. Dr. Donahue holds PhDs in mathematics and engineering from The Ohio State University, and has authored over 50 journal publications.
Yaniv Erlich (Whitehead Institute for Biomedical Research) Tutorial: Cost effective sequencing of rare genetic variations
Abstract: In the past few years, we have experienced a paradigm shift in human genetics. Accumulating lines of evidence have highlighted the pivotal role of rare genetic variations in a wide variety of traits and diseases. Studying rare variations is a needle in a haystack problem, as large cohorts have to be assayed in order to trap the variations and gain statistical power. The performance of DNA sequencing is exponentially growing, providing sufficient capacity to profile an extensive number of specimens. However, sample preparation schemes do not scale as sequencing capacity. A brute force approach of preparing hundredths to thousands of specimens for sequencing is cumbersome and cost-prohibited. The next challenge, therefore, is to develop a scalable technique that circumvents the bottleneck in sample preparation.

My tutorial will provide background on rare genetic variations and DNA sequencing. I will present our sample prep strategy, called DNA Sudoku, that utilizes combinatorial pooling/compressed sensing approach to find rare genetic variations. More importantly, I will discuss several major distinction from the classical combinatorial due to sequencing specific constraints.
Dina Esposito (Whitehead Institute for Biomedical Research) Mining Rare Human Variations using Combinatorial Pooling
Abstract: Finding rare genetic variations in large cohorts requires tedious preparation of large numbers of specimens for sequencing. We are developing a solution, called DNA Sudoku, to reduce prep time and increase the throughput of samples. By using a combinatorial pooling approach, we multiplex specimens and then barcode the pools, rather than individuals, for sequencing in a single lane on the Illumina platform.

We have developed a protocol for quantifying, calibrating, and pooling DNA samples using a liquid-handling robot, which has required a significant amount of testing in order to reduce volume variation. I will discuss our protocol and the steps we have taken to reduce CV. For accurate decoding and to reduce the possibility of specimen dropout, it is important that the DNA samples are accurately quantified and calibrated so that equal amounts can be pooled and sequenced. We can determine the number of carriers in each pool from sequencing output and reconstruct the original identity of individual specimens based on the pooling design, allowing us to identify a small number of carriers in a large cohort.
Zoltan Furedi (Hungarian Academy of Sciences (MTA)) Superimposed codes
Abstract: There are many instances in Coding Theory when codewords must be restored from partial information, like defected data (error correcting codes), or some superposition of the strings. These lead to superimposed codes, a close relative of group testing problems.

There are lots of versions and related problems, like Sidon sets, sum-free sets, unionfree families, locally thin families, cover-free codes and families, etc. We discuss two cases cancellative and union-free codes.

A family of sets Ƒ (and the corresponding code of 0-1 vectors) is called union-free if A ∪ B≠ C ∪ D and A,B,C,D ∈ F imply {A,B} = {C,D}. Ƒ is called t-cancellative if for all distict t + 2 members A1, … ,At and B,C ∈ Ƒ

A1 ∪ ... ∪ At ∪ B ≠ A1 ∪ … At ∪C:


Let ct(n) be the size of the largest t-cancellative code on n elements. We significantly improve the previous upper bounds of Körner and Sinaimeri, e.g., we show c2(n) ≤ 20:322n (for n > n0).
Anna Gal (University of Texas at Austin) Streaming algorithms for approximating the length of the longest increasing subsequence
Abstract: The data stream model allows only one way access to the data, possibly with several passes. This model is motivated by problems related to processing very large data sets, when it is desirable to keep only a small part of the data in active memory at any given point during the computation.

I will talk about proving lower bounds on how much space (memory) is necessary to still be able to solve the given task. I will focus on the problem of approximating the length of the longest increasing subsequence, which is a measure of how well the data is sorted.

Joint work with Parikshit Gopalan.
Anna Gilbert (AT&T Laboratories - Research) Tutorial: Sparse signal recovery
Abstract: My talk will be a tutorial about sparse signal recovery but, more importantly, I will provide an overview of what the research problems are at the intersection of biological applications of group testing, streaming algorithms, sparse signal recovery, and coding theory. The talk should help set the stage for the rest of the workshop.
David Golan (Tel Aviv University) Weighted Pooling - Simple and Effective Techniques for Pooled High Throughput Sequencing Design
Abstract: Despite the rapid decrease in sequencing costs, sequencing a large group of individuals is still prohibitively expensive. Recently, several sophisticated pooling designs were suggested that can identify carriers of rare alleles in large cohorts with a significantly smaller number of lanes, thus dramatically reducing the cost of such large scale genotyping projects. These approaches all use combinatorial pooling designs where each individual is either present in a pool or absent from it. One can then infer the number of carriers in a pool, and by combining information across pools, reconstruct the identity of the carriers.

We show that one can gain further efficiency and cost reduction by using “weighted” designs, in which different individuals donate different amounts of DNA to the pools. Intuitively, in this situation the number of mutant reads in a pool does not only indicate the number of carriers, but also of the identity of the carriers.

We describe and study a simple but powerful example of such weighted designs, with non-overlapping pools. We demonstrate that even this naive approach is not only easier to implement and analyze but is also competitive in terms of accuracy with combinatorial designs when identifying very rare variants, and is superior to the combinatorial designs when genotyping more common variants.

We then discuss how weighting can be further incorporated into existing designs to increase their accuracy and demonstrate the resulting improvement in reconstruction efficiency using simulations. Finally, we argue that these weighted designs have enough power to facilitate detection of common alleles, so they can be used as a cornerstone of whole-exome or even whole-genome sequencing projects.
Stefano Lonardi (University of California, Riverside) Combinatorial Pooling Enables Selective Sequencing of the Barley Gene Space
Abstract: We propose a new sequencing protocol that combines recent advances in combinatorial pooling design and second-generation sequencing technology to efficiently approach de novo selective genome sequencing. We show that combinatorial pooling is a cost-effective and practical alternative to exhaustive DNA barcoding when dealing with hundreds or thousands of DNA samples, such as genome-tiling gene-rich BAC clones. The novelty of the protocol hinges on the computational ability to efficiently compare hundreds of million of short reads and assign them to the correct BAC clones so that the assembly can be carried out clone-by-clone. Experimental results on simulated data for the rice genome show that the deconvolution is extremely accurate (99.57% of the deconvoluted reads are assigned to the correct BAC), and the resulting BAC assemblies have very high quality (BACs are covered by contigs over about 77% of their length, on average). Experimental results on real data for a gene-rich subset of the barley genome confirm that the deconvolution is accurate (almost 70% of left/right pairs in paired-end reads are assigned to the same BAC, despite being processed independently) and the BAC assemblies have good quality (the average sum of all assembled contigs is about 88% of the estimated BAC length).

Joint work with D. Duma (UCR), M. Alpert (UCR), F. Cordero (U of Torino), M. Beccuti (U of Torino), P. R. Bhat (UCR and Monsanto), Y. Wu (UCR and Google), G. Ciardo (UCR), B. Alsaihati (UCR), Y. Ma (UCR), S. Wanamaker (UCR), J. Resnik (UCR), and T. J. Close (UCR).

Preprint available at http://arxiv.org/abs/1112.4438
Mikhail B Malyutov (Northeastern University) Poster - Upgraded Separate Testing of Inputs in Compressive Sensing
Abstract: Screening experiments (SE) deal with finding a small number s Active Inputs (AIs) out of a vast total amount t of inputs in a regressionmodel. Of special interest in the SE theory is finding the so-called maximal rate (capacity). For a set of t elements, denote the set of its s-subsets by (s, t). Introduce a noisy system with t binary inputs and one output y. Suppose that only s 0. The f-capacity Cf (s) = limt!1(log t/Nf (s, t, )) for any > 0 is the ‘limit for the per- formance of the f-analysis’. We obtained tight capacity bounds [4], [5] by formalizing CSS as a special case of Multi-Access Communication Channels (MAC) of information transmission capacity region construction developed by R. Ahlswede in [1] and comparing CSS’ maximal rate (capacity) with small error for two practical methods of outputs’ analysis under the optimal CSS design motivated by applications like [2] . Recovering Active Inputs with small error probability and accurate parameter estimation are both possible with rates less than capacity and impossible with larger rates. A staggering amount of attention was recently devoted to the study of compressive sensing and related areas using sparse priors in over parameterized linear models which may be viewed as a special case of our models with continuous input levels. The threshold phenomenon was empirically observed in early papers [3], [6] : as the dimension of a randominstance of a problem grows there is a sharp transition from successful recovery to failure as a function of the number of observations versus the dimension and sparsity of the unknown signal. Finding this threshold is closely related to our capacity evaluation. Some threshold bounds for the compressive sensing were made using standard information-theoretic tools, e.g. in [15]
Olgica Milenkovic (University of Illinois at Urbana-Champaign) Probabilistic and combinatorial models for quantized group testing
Abstract: We consider a novel group testing framework where defectives obey a probabilistic model in which the number and set of defectives is governed by a graphical model. We furthermore assume that the defectives have importance factors that influence their strength on the test outcomes and the accuracy with which the outcomes are read. This kind of scenario arises, for example, in MAC channels with networked users using different communication powers, or in DNA pooling schemes which involve large families.
Fabien Morel (Ludwig-Maximilians-Universität München) On the homology of Lie groups made discrete
Abstract: In this talk I will recall and discuss the conjecture of John Milnor on the homology of Lie groups made discrete, as well as its algebraic analogue, the Friedlander conjecture.

In a work still partially in progress, we give a proof of that conjecture for algebraic groups G over algebraically closed fields. I will sketch some ideas behind this proof, in particular the role of A1-homotopy theory (already used by V. Voevodsky to prove other conjectures of John Milnor concerning mod 2 Galois cohomology and quadratic forms) and the role of a new object attached to G, its simplicial building. We will emphasize the case G = SL_2, SL_3,...
Jelani Nelson (Princeton University) Sparser Johnson-Lindenstrauss Transforms
Abstract: The Johnson-Lindenstrauss (JL) lemma (1984) states that any n points in d-dimensional Euclidean space can be embedded into k = O((log n)/eps^2) dimensions so that all pairwise distances are preserved up to 1+/-eps. Furthermore, this embedding can be achieved via a linear mapping. The JL lemma is a useful tool for speeding up solutions to several high-dimensional problems: closest pair, nearest neighbor, diameter, minimum spanning tree, etc. It also speeds up some clustering and string processing algorithms, reduces the amount of storage required to store a dataset, and can be used to reduce memory required for numerical linear algebra problems such as linear regression and low rank approximation.

The original proofs of the JL lemma let the linear mapping be specified by a random dense k x d matrix (e.g. i.i.d. Gaussian entries). Thus, performing an embedding requires dense matrix-vector multiplication. We give the first construction of linear mappings for JL in which only a subconstant fraction of the embedding matrix is non-zero, regardless of how eps and n are related, thus always speeding up the embedding time. Previous constructions only achieved sparse embedding matrices for 1/eps >> log n.

This is joint work with Daniel Kane (Stanford).
Natasha Przulj (Imperial College London) Network topology as a source of biological information
Abstract: Sequence-based computational approaches have revolutionized biological understanding. However, they can fail to explain some biological phenomena. Since proteins aggregate to perform a function instead of acting in isolation, the connectivity of a protein interaction network (PIN) will provide additional insight into the inner working on the cell, over and above sequences of individual proteins. We argue that sequence and network topology give insights into complementary slices of biological information, which sometimes corroborate each other, but sometimes do not. Hence, the advancement depends on the development of sophisticated graph-theoretic methods for extracting biological knowledge purely from network topology before being integrated with other types of biological data (e.g., sequence). However, dealing with large networks is non-trivial, since many graph-theoretic problems are computationally intractable, so heuristic algorithms are sought.

Analogous to sequence alignments, alignments of biological networks will likely impact biomedical understanding. We introduce a family of topology-based network alignment (NA) algorithms, (that we call GRAAL algorithms), that produces by far the most complete alignments of biological networks to date: our alignment of yeast and human PINs demonstrates that even distant species share a surprising amount of PIN topology. We show that both species phylogeny and protein function can be extracted from our topological NA. Furtermore, we demonstrate that the NA quality improves with integration of additional data sources (including sequence) into the alignment algorithm: surprisingly, 77.7% of proteins in the baker’s yeast PIN participate in a connected subnetwork that is fully contained in the human PIN suggesting broad similarities in internal cellular wiring across all life on Earth. Also, we demonstrate that topology around cancer and non-cancer genes is different and when integrated with functional genomics data, it successfully predicts new cancer genes in melanogenesis-related pathways.
Parimala Raman (Emory University) The Milnor Conjectures and Quadratic Forms
Abstract: The Milnor conjectures relating the graded Witt ring and the mod-2 Galois cohomology ring of a field have been a driving force in the algebraic theory of quadratic forms.  The degree 2 norm residue isomorphism, due to Merkurjev, established the first case of the Milnor conjectures and ushered in a new era in the theory of quadratic forms. We shall explain some of the consequences of the Milnor conjectures, with particular reference to invariants of fields associated to quadratic forms.
Douglas Conner Ravenel (University of Rochester) A solution to the Arf-Kervaire invariant problem
Abstract: The 1963 work of Kervaire-Milnor on the classification of exotic spheres in terms of the stable homotopy groups of spheres left one unanswered question in dimensions congruent to two modulo four. Subsequent work by Brown-Peterson and Browder gave the answer in all dimensions that are not two less than a power of two.  We solve the problem in all remaining cases except 126. Our answer is the opposite of the one that many sought in the 1970s.
Sylvie Ricard-Blum (Université Claude-Bernard (Lyon I)) A dynamic and quantitative protein interaction network regulating angiogenesis
Abstract: Angiogenesis, consisting in the formation of blood vessels from preexisting ones, is of crucial importance in pathological situations such as cancer and diabetes and is a therapeutic target for these two diseases. We have developed protein and sugar arrays probed by surface plasmon resonance (SPR) imaging to identify binding partners of proteins, polysaccharides and receptors regulating angiogenesis. Interactions collected from our own experiments and from literature curation have been used to build a network comprising protein-protein and protein-polysaccharide interactions. To switch from a static to a dynamic and quantitative interaction network, we have measured kinetics and affinity of interactions by SPR to discriminate transient from stable interactions and to prioritize interactions in the network. We have also identified protein interaction sites either experimentally or by molecular modeling to discriminate interactions occurring simultaneously from those taking place sequentially. The ultimate step is to integrate, using bioinformatics tools, all these parameters in the interaction network together with inhibitors of these interactions and with gene and protein expression data available from Array Express or Gene Expression Omnibus and from the Human Protein Atlas. This dynamic network will allow us to understand how angiogenesis is regulated in a concerted fashion via several receptors and signaling pathways, to identify crucial interactions for the integrity of the network that are potential therapeutic targets, and to predict the side effects of anti-angiogenic treatments.
Atri Rudra (University at Buffalo (SUNY)) Tutorial: Group Testing and Coding Theory
Abstract: Group testing was formalized by Dorfman in his 1943 paper and was originally used in WW-II to identify soldiers with syphilis. The main insight in this application is that blood samples from different soldiers can be combined to check if at least one of soldiers in the pool has the disease. Since then group testing has found numerous applications in many areas such as (computational) biology, combinatorics and (theoretical) computer science.

Theory of error-correcting codes, or coding theory, was born in the works of Shannon in 1948 and Hamming in 1950. Codes are ubiquitous in our daily life and have also found numerous applications in theoretical computer science in general and computational complexity in particular.

Kautz and Singleton connected these two areas in their 1964 paper by using "code concatenation" to design good group testing schemes. All of the (asymptotically) best know explicit constructions of group testing schemes use the code concatenation paradigm. In this talk, we will focus on the "decoding" problem for group testing: i.e. given the outcomes of the tests on the pools, identify the infected soldiers. Recent applications of group testing in data stream algorithm require sub-linear time decoding, which is not guaranteed by the traditional constructions.

The talk will first survey the Kautz-Singleton construction and then will will show how recent developments in list decoding of codes lead in a modular way to sub-linear time decodable group testing schemes.
Vyacheslav V. Rykov (University of Nebraska) Superimposed Codes and Designs for Group Testing Models
Abstract: We will discuss superimposed codes and non-adaptive group testing designs arising from the potentialities of compressed genotyping models in molecular biology. The given survey is also motivated by the 30th anniversary of our recurrent upper bound on the rate of superimposed codes published in 1982.
Alex Samorodnitsky (Hebrew University) Testing Boolean functions
Abstract: I will talk about property testing of Boolean functions, concentrating on two topics: testing monotonicity (an example of a combinatorial property) and testing the property of being a low-degree polynomial (an algebraic property).
Sriram Sankararaman (Harvard Medical School) Genomic Privacy and the Limits of Individual Detection in a Pool
Abstract: Statistical power to detect associations in genome-wide association studies can be enhanced by combining data across studies in meta-analysis or replication studies. Such methods require data to flow freely in the scientific community, however, and this raises privacy concerns.

Till recently, many studies pooled individuals together, making only the allele frequencies of each SNP in the pool publicly available. However a technique that could be used to detect the presence of individual genotypes from such data prompted organizations such as the NIH to restrict public access to summary data . To again allow public access to data from association studies, we need to determine which set of SNPs can be safely exposed while preserving an acceptable level of privacy.

To answer this question, we provide an upper bound on the power achievable by any detection method as a function of factors such as the number and the allele frequencies of exposed SNPs, the number of individuals in the pool, and the false positive rate of the method. Our approach is based on casting the problem in a statistical hypothesis testing framework for which the likelihood ratio test (LR-test) attains the maximal power achievable.

Our analysis provides quantitative guidelines for researchers to make SNPs public without compromising privacy. We recommend, based on our analysis, that only common independent SNPs be exposed. The final decision regarding the exposed SNPs should be based on the analytical bound in conjunction with empirical estimates of the power of the LR test. To this end, we have implemented a tool, SecureGenome, that determines the set of SNPs that can be safely exposed for a given dataset.
Robert Glenn Scharein (Hypnagogic Software) Computational Knot Theory with KnotPlot
Abstract: Mathematical knot theory is an exciting branch of topology that is relevant to physics, chemistry and biology. After a brief introduction to knot theory, we will explore several applications where computational methods play a useful role. When experimenting with knots on a computer, it is helpful to have powerful tools for the creation of initial conformations, for performing simulations on those conformations and also to compute topological and geometric properties of any resulting products. One such tool is the software KnotPlot, developed by the speaker over a period of many years. KnotPlot permits a wide range of interesting experiments to be performed. Several of these will be discussed, one important area being the tangling and untangling of DNA. Finally, we will dip into the more esoteric realm of higher dimensional knotting.
Alexander Schliep (Rutgers University) From screening clone libraries to detecting biological agents
Abstract: Group testing has made many contributions to modern molecular biology. In the Human Genome Project large clone libraries where created to amplify DNA. Widely used group testing schemes vastly accelerated the detection of overlaps between the individual clones in these libraries through experiments, realizing savings both in effort and materials.

Modern molecular biology also contributed to group testing. The problem of generalized group testing (in the combinatorial sense) arises naturally, when one uses oligonucleotide probes to identify biological agents present in a sample. In this setting a group testing design cannot be chosen arbitrarily. The possible columns of a group testing design matrix are prescribed by the biology, namely by the hybridization reactions between target DNA and probes
Noam Shental (Open University of Israel) Identification of rare alleles and their carriers using compressed se(que)nsing
Abstract: Identification of rare variants by resequencing is important both for detecting novel variations and for screening individuals for known disease alleles. New technologies enable low-cost resequencing of target regions, although it is still prohibitive to test more than a few individuals. We propose a novel pooling design that enables the recovery of novel or known rare alleles and their carriers in groups of individuals. The method is based on combining next-generation sequencing technology with a Compressed Sensing (CS) approach. The approach is general, simple and efficient, allowing for simultaneous identification of multiple variants and their carriers. It reduces experimental costs, i.e., both sample preparation related costs and direct sequencing costs, by up to 70 fold, and thus allowing to scan much larger cohorts. We demonstrate the performance of our approach over several publicly available data sets, including the 1000 Genomes Pilot 3 study. We believe our approach may significantly improve cost effectiveness of future association studies, and in screening large DNA cohorts for specific risk alleles.

We will present initial results of two projects that were initiated following publication. The first project concerns identification of de novo SNPs in genetic disorders common among Ashkenazi Jews, based on sequencing 3000 DNA samples. The second project in plant genetics involves identifying SNPs related to water and silica homeostasis in Sorghum bicolor, based on sequencing 3000 DNA samples using 1-2 Illumina lanes.

Joint work with Amnon Amir from the Weizmann Institute of Science, and Or Zuk from the Broad Institute of MIT and Harvard
Nicolas Thierry-Mieg (Centre National de la Recherche Scientifique (CNRS)) Shifted Transversal Design Smart-pooling: increasing sensitivity, specificity and efficiency in high-throughput biology
Abstract: Group testing, also know as smart-pooling, is a promising strategy for achieving high efficiency, sensitivity, and specificity in systems-level projects. It consists in assaying well-chosen pools of probes, such that each probe is present in several pools, hence tested several times. The goal is to construct the pools so that the positive probes can usually be directly identified from the pattern of positive pools, despite the occurrence of false positives and false negatives. While striving for this goal, two interesting mathematical or computational problems emerge: the pooling problem (how should the pools be designed?), and the decoding problem (how to interpret the outcomes?). In this talk I will discuss these questions and the solutions we have proposed: a flexible and powerful combinatorial construction for designing smart-pools (the Shifted Transveral Design, STD), and an efficient exact algorithm for interpreting results (interpool). I will then present the results of validation experiments that we have performed in the context of yeast two-hybrid interactome mapping.
David P. Woodruff (IBM Research Division) Tutorial: The Data Stream Model
Abstract: The data stream model has emerged as a way of analyzing algorithmic efficiency in the presence of massive data sets. Typically the algorithm is allowed a few (usually one) passes over the input, and must use limited memory and have very fast per input item processing time. I will give a survey of algorithms and lower bounds in this area, with an emphasis on problems such as norm estimation, numerical linear algebra, empirical entropy, l_p-sampling, and heavy hitters. Time-permitting I'll also discuss the extension to functional monitoring, in which there are multiple sites each with a data stream and a central coordinator should continuously maintain a statistic over the union of the data streams.
Or Zuk (Broad Institute) Reconstruction of bacterial communities using sparse representation
Abstract: Determining the identities and frequencies of species present in a sample is a central problem in metagenomics, with scientific, environmental and clinical implications.

A popular approach to the problem is sequencing the Ribosomal 16s RNA gene in the sample using universal primers, and using variation in the gene's sequence between different species to identify the species present in the sample. We present a novel framework for community reconstruction, based on sparse representation; while millions of microorganisms are present on earth, with known 16s sequences stored in a database, only a small minority (typically a few hundreds) are likely to be present in any given sample,

We discuss the statistical framework, algorithms used and results in terms of accuracy and species resolution.
Visitors in Residence
Alejandro Adem University of British Columbia 1/29/2012 - 2/1/2012
Anar Akhmedov University of Minnesota 1/30/2012 - 2/1/2012
Zack W Almquist University of California, Irvine 2/26/2012 - 3/2/2012
Brendan P.W. Ames University of Minnesota 8/31/2011 - 8/30/2012
Amihood Amir Bar-Ilan University 2/12/2012 - 2/16/2012
John David Arellano Rice University 2/25/2012 - 3/2/2012
Amir Asiaee Taheri University of Minnesota 2/27/2012 - 3/2/2012
Sharon Aviran University of California, Berkeley 2/12/2012 - 2/15/2012
Gary Bader University of Toronto 2/26/2012 - 3/1/2012
Joel S Bader Johns Hopkins University 2/26/2012 - 3/2/2012
Arindam Banerjee University of Minnesota 9/1/2011 - 6/30/2012
Alexander Barg University of Maryland 2/12/2012 - 2/16/2012
Hyman Bass University of Michigan 1/29/2012 - 2/1/2012
Maria Basterra University of New Hampshire 1/29/2012 - 2/1/2012
Jeremy Bellay University of Maryland 2/26/2012 - 3/2/2012
Tanya Berger-Wolf University of Illinois at Chicago 2/26/2012 - 3/2/2012
Julie Bergner University of California, Riverside 1/29/2012 - 2/1/2012
Andrew John Beveridge Macalester College 9/1/2011 - 5/15/2012
Smriti Bhagat Technicolor Inc. 2/27/2012 - 2/29/2012
Manisha Bhardwaj University of Houston 2/26/2012 - 3/2/2012
Jodi Black Bucknell University 1/29/2012 - 2/1/2012
Mathieu Blanchette McGill University 2/26/2012 - 3/3/2012
Sergey G Bobkov University of Minnesota 9/1/2011 - 6/30/2012
Shyam Boriah University of Minnesota 2/27/2012 - 3/2/2012
William Browder Princeton University 1/29/2012 - 2/1/2012
Yiqing Cai University of Pennsylvania 2/26/2012 - 3/2/2012
Luca Capogna University of Minnesota 8/15/2011 - 6/10/2012
Sylvain Edward Cappell New York University 1/29/2012 - 2/1/2012
Aycil Cesmelioglu University of Minnesota 9/30/2010 - 8/30/2012
Huilan Chang National University of Kaohsiung 2/12/2012 - 2/17/2012
Mahdi Cheraghchi Carnegie Mellon University 2/12/2012 - 2/17/2012
Mung Chiang Princeton University 2/27/2012 - 3/2/2012
Sou-Cheng Choi Argonne National Laboratory 2/12/2012 - 2/16/2012
Ferdinando Cicalese Università di Salerno 2/11/2012 - 2/16/2012
Ionut Ciocan-Fontanine University of Minnesota 1/31/2012 - 2/1/2012
Barry Cipra NONE 1/30/2012 - 2/1/2012
Paolo Codenotti University of Minnesota 9/1/2011 - 8/30/2012
Frederick R. Cohen University of Rochester 1/29/2012 - 2/1/2012
Graham Cormode AT&T Laboratories - Research 2/27/2012 - 3/2/2012
Lenore J. Cowen Tufts University 2/26/2012 - 3/2/2012
Mark Crovella Boston University 2/26/2012 - 3/2/2012
Jintao Cui University of Minnesota 8/31/2010 - 8/30/2012
Xiaoyi Cui University of Minnesota 1/30/2012 - 2/1/2012
Phuong Dao Simon Fraser University 2/26/2012 - 3/2/2012
Isabel K. Darcy University of Iowa 9/1/2011 - 6/30/2012
Ingrid Daubechies Duke University 2/12/2012 - 2/17/2012
James F Davis Indiana University 1/29/2012 - 2/1/2012
Annalisa De Bonis Università di Salerno 2/12/2012 - 2/17/2012
Christian Deppe Universität Bielefeld 2/12/2012 - 2/18/2012
Raamesh Deshpande University of Minnesota 2/27/2012 - 3/2/2012
Michael J. Donahue National Institute of Standards and Technology 2/23/2012 - 2/24/2012
Ding-Zhu Du University of Texas 2/12/2012 - 2/15/2012
William G. Dwyer University of Notre Dame 1/29/2012 - 2/2/2012
Robert Edwards University of California, Los Angeles 1/30/2012 - 2/1/2012
Geir Ellingsrud University of Oslo 1/29/2012 - 2/1/2012
Amin Emad University of Illinois at Urbana-Champaign 2/12/2012 - 2/17/2012
Yaniv Erlich Whitehead Institute for Biomedical Research 2/12/2012 - 2/17/2012
Leonardo Espin NONE 9/1/2011 - 6/30/2012
Dina Esposito Whitehead Institute for Biomedical Research 2/12/2012 - 2/17/2012
Pedro Andres Forero University of Minnesota 2/27/2012 - 3/2/2012
John Fornaess University of Michigan 1/28/2012 - 2/1/2012
Zoltan Furedi Hungarian Academy of Sciences (MTA) 2/12/2012 - 2/17/2012
Anna Gal University of Texas at Austin 2/12/2012 - 2/17/2012
Tingran Gao Duke University 1/29/2012 - 2/1/2012
Carlos Andres Garavito-Garzon University of Minnesota 9/8/2011 - 6/30/2012
Nikolaos Gatsis University of Minnesota 2/27/2012 - 3/2/2012
Hillel H. Gershenson NONE 1/30/2012 - 2/1/2012
Lise Getoor University of Maryland 2/26/2012 - 3/1/2012
Georgios B Giannakis University of Minnesota 2/27/2012 - 3/2/2012
Anna Gilbert AT&T Laboratories - Research 2/12/2012 - 2/17/2012
Samuel Gitler CINVESTAV 1/29/2012 - 2/1/2012
David Golan Tel Aviv University 2/12/2012 - 2/17/2012
Manuel Gomez Rodriguez Max-Planck-Institut für Intelligente Systeme 2/26/2012 - 3/3/2012
Robert Gulliver University of Minnesota 1/30/2012 - 2/1/2012
Ian Hambleton McMaster University 1/29/2012 - 2/1/2012
Michael Hill University of Virginia 1/29/2012 - 2/1/2012
Shawndra Hill University of Pennsylvania 2/26/2012 - 3/1/2012
Michael J. Hopkins Harvard University 1/28/2012 - 2/1/2012
Yulia Hristova University of Minnesota 9/1/2010 - 8/31/2012
Miriam Huntley Harvard University 1/30/2012 - 2/1/2012
Dale Husemoller Max Planck Institute for Mathematics 1/29/2012 - 2/1/2012
Ali Jadbabaie University of Pennsylvania 2/29/2012 - 3/2/2012
Bjørn Jahren University of Oslo 1/29/2012 - 2/2/2012
Vuk Janjic Imperial College London 2/26/2012 - 3/2/2012
Dihua Jiang University of Minnesota 1/30/2012 - 2/1/2012
Niles Johnson University of Georgia 1/29/2012 - 2/1/2012
Igor Jurisica University of Toronto 2/26/2012 - 3/2/2012
Donald Kahn University of Minnesota 1/30/2012 - 2/1/2012
Jaya Kawale University of Minnesota 2/27/2012 - 3/2/2012
Hee Jung Kim Pohang University of Science and Technology (POSTECH) 1/29/2012 - 2/1/2012
Carl Kingsford University of Maryland 2/26/2012 - 3/2/2012
Robion Kirby University of California, Berkeley 1/29/2012 - 2/1/2012
Elizabeth Koch University of Minnesota 2/27/2012 - 3/2/2012
Eric Kolaczyk Boston University 2/26/2012 - 2/29/2012
Mehmet Koyuturk Case Western Reserve University 2/28/2012 - 3/2/2012
Ravi Kumar Yahoo! Inc. 2/27/2012 - 3/2/2012
Kee Yuen Lam University of British Columbia 1/29/2012 - 2/1/2012
Tsit Yuen Lam University of California, Berkeley 1/29/2012 - 2/1/2012
Tyler Lawson University of Minnesota 1/30/2012 - 2/1/2012
Young-Ju Lee Rutgers University 2/19/2012 - 2/21/2012
Gilad Lerman University of Minnesota 9/1/2011 - 6/30/2012
Jure Leskovec Stanford University 2/29/2012 - 3/2/2012
Lizao Li University of Minnesota 1/30/2012 - 2/1/2012
Tian-Jun Li University of Minnesota 1/30/2012 - 2/1/2012
Tong Li University of Iowa 2/26/2012 - 3/3/2012
Wenbo Li University of Delaware 9/1/2011 - 5/30/2012
David Liben-Nowell Carleton College 2/27/2012 - 3/2/2012
Lek-Heng Lim University of Chicago 2/1/2012 - 6/10/2012
Ayelet Lindenstrauss Indiana University 1/29/2012 - 2/1/2012
Xin Liu University of Minnesota 8/31/2011 - 8/30/2012
Stefano Lonardi University of California, Riverside 2/12/2012 - 2/16/2012
Ying Lu University of Minnesota 2/27/2012 - 3/2/2012
Tom Lyche University of Oslo 1/29/2012 - 2/1/2012
Shiqian Ma University of Minnesota 8/31/2011 - 8/30/2013
Ib Madsen University of Copenhagen 1/29/2012 - 2/1/2012
Mikhail B Malyutov Northeastern University 2/12/2012 - 2/18/2012
Yi Mao Michigan State University 2/12/2012 - 2/17/2012
Yu (David) Mao University of Minnesota 8/31/2010 - 8/30/2012
Albert Marden University of Minnesota 1/30/2012 - 2/1/2012
Athina Markopoulou University of California, Irvine 2/26/2012 - 3/2/2012
Gabriela Martínez University of Minnesota 8/31/2011 - 8/30/2013
John McCleary Vassar College 1/29/2012 - 2/2/2012
Dusa McDuff Barnard College 1/27/2012 - 2/2/2012
Danielle Paola Mersch Université de Lausanne 2/25/2012 - 3/3/2012
William Messing University of Minnesota 1/30/2012 - 2/1/2012
Olgica Milenkovic University of Illinois at Urbana-Champaign 2/12/2012 - 2/18/2012
Tijana Milenkovic University of Notre Dame 2/26/2012 - 3/2/2012
John Milnor SUNY 1/27/2012 - 2/2/2012
Tom Milnor University of British Columbia 1/27/2012 - 2/1/2012
Dimitrios Mitsotakis University of Minnesota 10/27/2010 - 8/31/2012
Jack Morava Johns Hopkins University 1/29/2012 - 2/2/2012
Fabien Morel Ludwig-Maximilians-Universität München 1/28/2012 - 2/2/2012
Chad Myers University of Minnesota 2/27/2012 - 3/2/2012
Jelani Nelson Princeton University 2/12/2012 - 2/17/2012
Jennifer Neville Purdue University 2/26/2012 - 3/2/2012
Hung Ngo University at Buffalo (SUNY) 2/12/2012 - 2/17/2012
Peter J. Olver University of Minnesota 1/30/2012 - 2/1/2012
Kent Orr Indiana University 1/29/2012 - 2/1/2012
Peter Ozsvath Massachusetts Institute of Technology 1/29/2012 - 2/1/2012
Mary Therese Padberg University of Iowa 2/12/2012 - 2/17/2012
Aditya Pal University of Minnesota 2/26/2012 - 3/2/2012
Hyungju Park Pohang University of Science and Technology (POSTECH) 1/29/2012 - 2/1/2012
Scott Pauls Dartmouth College 2/26/2012 - 3/2/2012
Erik Kjær Pedersen University of Copenhagen 1/29/2012 - 2/1/2012
John Pinney Imperial College London 2/25/2012 - 3/1/2012
Carles Pons University of Minnesota 2/27/2012 - 3/2/2012
Ely Porat Bar-Ilan University 2/12/2012 - 2/18/2012
Candice Renee Price University of Iowa 8/1/2011 - 7/31/2012
Natasha Przulj Imperial College London 2/26/2012 - 3/3/2012
Natasha Przulj Imperial College London 2/12/2012 - 2/17/2012
Teresa M Przytycka National Center for Biotechnology Information 2/26/2012 - 3/2/2012
Karthik Pulivendal University of Minnesota 1/30/2012 - 2/1/2012
Weifeng (Frederick) Qiu University of Minnesota 8/31/2010 - 8/30/2012
Changzheng Qu University of Minnesota 1/30/2012 - 2/1/2012
Anca Ruxandra Radulescu University of Colorado 1/29/2012 - 2/1/2012
Ketan Rajawat University of Minnesota 2/27/2012 - 3/2/2012
Parimala Raman Emory University 1/29/2012 - 2/1/2012
Andrew Ranicki University of Edinburgh 1/29/2012 - 2/1/2012
Ben J. Raphael Brown University 2/26/2012 - 3/3/2012
Holger Rauhut Rheinische Friedrich-Wilhelms-Universität Bonn 2/25/2012 - 3/17/2012
Douglas Conner Ravenel University of Rochester 1/29/2012 - 2/1/2012
Victor Reiner University of Minnesota 1/30/2012 - 2/1/2012
Sylvie Ricard-Blum Université Claude-Bernard (Lyon I) 2/12/2012 - 2/17/2012
Luis Rocha Indiana University 2/26/2012 - 3/2/2012
Jonathan Rogness University of Minnesota 1/30/2012 - 2/1/2012
Dale Preston Odin Rolfsen University of British Columbia 1/29/2012 - 2/1/2012
Xavier Ronaled University of Minnesota 1/30/2012 - 2/1/2012
Frederick Roth University of Toronto 2/26/2012 - 3/1/2012
Lee Rudolph Clark University 1/29/2012 - 2/1/2012
Atri Rudra University at Buffalo (SUNY) 2/12/2012 - 2/17/2012
Derek Ruths McGill University 2/26/2012 - 3/2/2012
Vyacheslav V. Rykov University of Nebraska 2/12/2012 - 2/19/2012
S. Cenk Sahinalp Simon Fraser University 2/26/2012 - 3/3/2012
Venkatesh Saligrama Boston University 2/12/2012 - 2/17/2012
Alex Samorodnitsky Hebrew University 2/11/2012 - 2/17/2012
Sriram Sankararaman Harvard Medical School 2/12/2012 - 2/17/2012
Guillermo R. Sapiro University of Minnesota 9/1/2011 - 5/31/2012
Radmila Sazdanović University of Pennsylvania 1/29/2012 - 2/1/2012
Robert Glenn Scharein Hypnagogic Software 1/5/2012 - 2/4/2012
Alexander Schliep Rutgers University 2/12/2012 - 2/17/2012
Michelle Kristin Schwalbe National Research Council 2/26/2012 - 3/2/2012
George R Sell University of Minnesota 1/30/2012 - 2/1/2012
Roded Sharan Tel Aviv University 2/26/2012 - 3/1/2012
Noam Shental Open University of Israel 2/12/2012 - 2/16/2012
Laurence Carl Siebenmann Université de Paris XI (Paris-Sud) 1/29/2012 - 2/3/2012
Mona Singh Princeton University 2/26/2012 - 2/29/2012
Steven Sperber University of Minnesota 1/30/2012 - 2/1/2012
Karsten Steinhaeuser University of Minnesota 2/27/2012 - 3/2/2012
Nathalie Stroeymeyt Université de Lausanne 2/25/2012 - 3/3/2012
Karthik Subbian University of Minnesota 2/27/2012 - 3/2/2012
Chiung-Jue Sung National Tsing Hua University 1/30/2012 - 2/1/2012
Arthur Szlam University of Minnesota 8/31/2011 - 8/30/2012
Vladimir Temlyakov University of South Carolina 2/1/2012 - 5/31/2012
Nicolas Thierry-Mieg Centre National de la Recherche Scientifique (CNRS) 2/12/2012 - 2/17/2012
Ulrike Tillmann University of Oxford 1/28/2012 - 2/1/2012
Vladimir D Tonchev Michigan Technological University 2/13/2012 - 2/18/2012
Donald Towsley University of Massachusetts 2/27/2012 - 3/2/2012
Masashi Toyoda University of Tokyo 2/26/2012 - 3/2/2012
Victor Turchin Kansas State University 1/29/2012 - 2/1/2012
Eric van den Berg Telcordia 2/26/2012 - 3/2/2012
Benjamin VanderSluis University of Minnesota 2/27/2012 - 3/2/2012
Fabio Vandin Brown University 2/26/2012 - 3/2/2012
Divyanshu Vats University of Minnesota 8/31/2011 - 8/30/2012
Jonathan Wahl University of North Carolina 1/30/2012 - 2/1/2012
Adwait Walimbe University of Minnesota 1/30/2012 - 2/1/2012
Lan Wang University of Minnesota 1/24/2012 - 5/12/2012
Walter Willinger AT&T Laboratories - Research 2/26/2012 - 3/2/2012
David P. Woodruff IBM Research Division 2/15/2012 - 2/17/2012
Mary Wootters University of Michigan 2/12/2012 - 2/17/2012
Zheyang Wu Worcester Polytechnic Institute 2/12/2012 - 2/17/2012
Lingzhou Xue University of Minnesota 9/1/2011 - 6/30/2012
Teng Zhang University of Minnesota 8/31/2011 - 8/30/2012
Ke Zhu University of Minnesota 1/30/2012 - 2/1/2012
Or Zuk Broad Institute 2/12/2012 - 2/17/2012
Legend: Postdoc or Industrial Postdoc Long-term Visitor

IMA Affiliates:
Arizona State University, Boeing, Colorado State University, Corning Incorporated, ExxonMobil, Ford, General Motors, Georgia Institute of Technology, Honeywell, IBM, Indiana University, Iowa State University, Korea Advanced Institute of Science and Technology (KAIST), Lawrence Livermore National Laboratory, Lockheed Martin, Los Alamos National Laboratory, Medtronic, Michigan State University, Michigan Technological University, Mississippi State University, Northern Illinois University, Ohio State University, Pennsylvania State University, Portland State University, Purdue University, Rice University, Sandia National Laboratories, Schlumberger Cambridge Research, Schlumberger-Doll, Seoul National University, Siemens, Telcordia, Texas A & M University, University of Central Florida, University of Chicago, University of Delaware, University of Houston, University of Illinois at Urbana-Champaign, University of Iowa, University of Kentucky, University of Maryland, University of Michigan, University of Minnesota, University of North Carolina, University of Notre Dame, University of Pennsylvania, University of Pittsburgh, University of Tennessee, University of Wisconsin-Madison, University of Wyoming, US Air Force Research Laboratory, Wayne State University, Worcester Polytechnic Institute, Zhejiang University