Institute for Mathematics and its Applications University of Minnesota 400 Lind Hall 207 Church Street SE Minneapolis, MN 55455 
20112012 Program
See http://www.ima.umn.edu/20112012/ for a full description of the 20112012 program on Mathematics of Information.
2:30pm3:00pm  Coffee break  Lind Hall 400 
11:15am12:15pm  On the Group Isomorphism Problem  Paolo Codenotti (University of Minnesota)  Keller Hall 3180  PS 
2:30pm3:00pm  Coffee break  Lind Hall 400 
11:00am12:00pm  Solute uptake in vessels with oscillatory walls  Leonardo Espin  Keller Hall 3180  MoI 
2:30pm3:00pm  Coffee break  Lind Hall 400 
1:25pm2:15pm  Topics in High Dimensional Phenomena Seminar  Lind Hall 401  S  
2:30pm3:00pm  Coffee break  Lind Hall 400 
1:25pm2:25pm  Statistics at Google Scale  Diane Lambert (Google Inc.)  Lind Hall 305  IPS 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
11:15am12:15pm  Geometry of maximum likelihood estimation in Gaussian graphical models  Caroline Uhler (University of Minnesota)  Keller Hall 3180  PS 
2:30pm3:30pm  Information passing and collective animal behavior  Naomi Ehrich Leonard (Princeton University)  Lind Hall 305  S 
3:30pm4:00pm  Coffee break  Lind Hall 400  
7:00pm8:00pm  Flocks and Fleets: Collective Motion in Nature and Robotics  Naomi Ehrich Leonard (Princeton University)  Willey Hall 175 
11:00am12:00pm  Computing Semantic Clusters by Semantic Mirroring and Spectral Graph Partitioning  Lars Eldén (Linköping University)  Keller Hall 3180  MoI 
2:30pm3:00pm  Coffee break  Lind Hall 400 
1:25pm2:15pm  Topics in High Dimensional Phenomena Seminar (moved to 305 Lind)  Lind Hall 401  S  
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
11:15am12:15pm  Necessary Conditions for SetBased Graphical Model Selection  Divyanshu Vats (University of Minnesota)  Keller Hall 3180  PS 
2:30pm3:00pm  Coffee break  Lind Hall 400 
11:00am12:00pm  Topology, Graphs, and Data  Isabel K. Darcy (University of Iowa)  Keller Hall 3180  MoI 
2:30pm3:00pm  Coffee break  Lind Hall 400 
1:25pm2:15pm  Topics in High Dimensional Phenomena Seminar  Lind Hall 401  S  
2:30pm3:00pm  Coffee break  Lind Hall 400 
2:30pm3:00pm  Coffee break  Lind Hall 400 
8:15am8:45am  Coffee and Registration  Keller Hall 3176  W10.2428.11  
8:45am9:00am  Welcome and Introduction  Keller Hall 3180  W10.2428.11  
9:00am10:00am  Trees, Graphs and Clusters: Learning Network Structure through Clustering  Brian Eriksson (University of WisconsinMadison) Robert Nowak (University of WisconsinMadison)  Keller Hall 3180  W10.2428.11 
10:00am10:30am  Break  Keller Hall 3176  W10.2428.11  
10:30am11:30am  Trees, Graphs and Clusters: Learning Network Structure through Clustering  Brian Eriksson (University of WisconsinMadison) Robert Nowak (University of WisconsinMadison)  Keller Hall 3180  W10.2428.11 
11:30am1:30pm  Lunch  Keller Hall 3176  W10.2428.11  
1:30pm2:30pm  Robust and Efficient Spectral Clustering of Large Graphs  Aarti Singh (Carnegie Mellon University)  Keller Hall 3180  W10.2428.11 
2:30pm3:00pm  Break  Keller Hall 3176  W10.2428.11  
3:00pm4:00pm  Sparse Signal Processing Bounds on Large Graphs  Venkatesh Saligrama (Boston University)  Keller Hall 3180  W10.2428.11 
4:00pm4:15pm  Break  Keller Hall 3176  W10.2428.11  
4:15pm5:15pm  Popularity versus Similarity in Growing Networks  Dmitri Krioukov (University of California, San Diego)  Keller Hall 3180  W10.2428.11 
9:00am10:00am  Extracting insight from large networks: smallscale structures, largescale structures, and their implications for machine learning and data analysis  Michael W. Mahoney (Stanford University)  Keller Hall 3180  W10.2428.11 
10:00am10:30am  Break  Keller Hall 3176  W10.2428.11  
10:30am11:30am  Extracting insight from large networks: smallscale structures, largescale structures, and their implications for machine learning and data analysis  Michael W. Mahoney (Stanford University)  Keller Hall 3180  W10.2428.11 
11:30am1:30pm  Lunch  W10.2428.11  
1:30pm2:30pm  Graphs of Shapes and Images  Leonidas Guibas (Stanford University)  Keller Hall 3180  W10.2428.11 
2:30pm3:00pm  Break  Keller Hall 3176  W10.2428.11  
3:00pm4:00pm  Regularized Online Optimization: Tracking Regret, Risk Bounds, and Applications to Dynamic Networks  Rebecca Willett (Duke University)  Keller Hall 3180  W10.2428.11 
4:00pm4:15pm  Break  Keller Hall 3176  W10.2428.11  
4:15pm5:15pm  Can We Quantify & Exploit Treelike Intermediate Structure in Complex Networks?  Blair Dowling Sullivan (Oak Ridge National Laboratory)  Keller Hall 3180  W10.2428.11 
6:00pm8:00pm  Social Hour Stub and Herbs 227 SE Oak St Minneapolis, MN 55455 Map  W10.2428.11 
All Day  Chair for Wednesday morning (10/26) is Alessandro Rinaldo Chair of Wednesday afternoon (10/26) is Lars Elden  W10.2428.11  
9:00am10:00am  Network analysis of human brains  Guillermo R. Sapiro (University of Minnesota)  Keller Hall 3180  W10.2428.11 
10:00am10:30am  Break  Keller Hall 3176  W10.2428.11  
10:30am11:30am  Managing Interference in Wireless Networks  Robert Calderbank (Duke University)  Keller Hall 3180  W10.2428.11 
11:30am1:30pm  Lunch  W10.2428.11  
1:30pm2:30pm  Learning Graphical Models by Competitive Assembly of Marginals  Donald Geman (Johns Hopkins University)  Keller Hall 3180  W10.2428.11 
2:30pm3:00pm  Break  Keller Hall 3176  W10.2428.11  
3:00pm4:00pm  The Block TwoLevel ErdösRényi (BTER) Graph Model  Tamara G. Kolda (Sandia National Laboratories)  Keller Hall 3180  W10.2428.11 
4:00pm4:15pm  Group Photo  W10.2428.11  
4:15pm5:30pm  Reception and Poster Session Poster submissions invited from all participants  Lind Hall 400  W10.2428.11  
Online Subspace Estimation and Tracking from Incomplete and Corrupted Data  Laura Balzano (University of WisconsinMadison)  
Cops and Robbers on Geometric Graphs  Andrew John Beveridge (Macalester College)  
Simultaneous clustering of timeevolving graphs  Lars Eldén (Linköping University)  
2cancellative families and codes  Zoltan Furedi (Hungarian Academy of Sciences (MTA))  
A Centrality Measure for Directed Graphs Using Average Hitting Cost  Golshan Golnari (University of Minnesota)  
Finding the ‘Needle’: Locating Interesting Nodes Using the KShortest Paths Algorithm in MapReduce  J.T. Halbert (TexelTek)  
Simulating complex networks via algorithms based on (t,r)regular graphs  Debra Knisley (East Tennessee State University)  
On the von Luxburg Approximation for Hitting and Commute Times in Large Dense Digraphs  Gyan Ranjan (University of Minnesota)  
Sparsistency of the Edge Lasso over Graph  James Sharpnack (Carnegie Mellon University)  
Dot Product Embedding in Large (Errorfully Observed) Graphs with Applications in Statistical Connectomics  Daniel L Sussman (Johns Hopkins University)  
Large Graph Classification: Theory and Statistical Connectomics Applications  Joshua Tzvi Vogelstein (Johns Hopkins University)  
Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs  Hyokun Yun (Purdue University) 
9:00am10:00am  Statistical analysis of populations with interacting and interfering units a joint work with Edo Airoldi  Simon Lunagomez (Harvard University)  Keller Hall 3180  W10.2428.11 
10:00am10:30am  Break  W10.2428.11  
10:30am11:30am  Statistical analysis of populations with interacting and interfering units  Simon Lunagomez (Harvard University)  Keller Hall 3180  W10.2428.11 
11:30am1:30pm  Lunch  W10.2428.11  
1:30pm2:30pm  Graphs in Network Commerce and Their Applications  Neel Sundaresan (eBay)  Keller Hall 3180  W10.2428.11 
2:30pm3:00pm  Break  Keller Hall 3176  W10.2428.11  
3:00pm4:00pm  Learning the Network Structure of Cortical Microcircuits  Stuart Geman (Brown University) Matthew T Harrison (Brown University)  Keller Hall 3180  W10.2428.11 
4:00pm4:15pm  Break  Keller Hall 3176  W10.2428.11  
4:15pm5:15pm  Recent Ideas on Subspace Clustering  Gilad Lerman (University of Minnesota)  Keller Hall 3180  W10.2428.11 
9:00am10:00am  Multiscale analysis of dynamic graphs  Mauro Maggioni (Duke University)  Keller Hall 3180  W10.2428.11 
10:00am10:15am  Break  Keller Hall 3176  W10.2428.11  
10:15am11:15am  Nonparametric Link Prediction  Purnamrita Sarkar (University of California, Berkeley)  Keller Hall 3180  W10.2428.11 
11:15am11:30am  Break  Keller Hall 3176  W10.2428.11  
11:30am12:30pm  An Asymmetric Laplacian and Commute Times for Directed Graphs.  Daniel Boley (University of Minnesota)  Keller Hall 3180  W10.2428.11 
2:30pm3:00pm  Coffee break  Lind Hall 400 
Event Legend: 

IPS  Industrial Problems Seminar 
MoI  2011/2012 Math of Info Seminar 
PS  IMA Postdoc Seminar 
S  Seminar 
W10.2428.11  Large Graphs: Modeling, Algorithms, and Applications 
Laura Balzano (University of WisconsinMadison)  Online Subspace Estimation and Tracking from Incomplete and Corrupted Data 
Abstract: Lowdimensional linear subspace approximations to highdimensional data are a common approach to handling problems of estimation, detection and prediction, with applications such as network monitoring, collaborative filtering, object tracking in computer vision, and environmental sensing. Corrupt and missing data are the norm in many highdimensional situations, not only because of errors and failures, but because it may be impossible to collect all the interesting measurements or impractical to compute in realtime using all the data available. Recently developed algorithms for "Matrix Completion" and "Robust PCA" have offered tools to find lowdimensional approximations to data even when data are missing and corrupt. However, these algorithms operate on all the available data at once and assume that the underlying lowdimensional subspace remains constant. This poster describes two alternatives, a subspace tracking algorithm called GROUSE (Grassmannian Rankone Update Subspace Estimation) and its robust counterpart GRASTA (Grassmannian Robust Adaptive Subspace Tracking Algorithm). Both are incremental algorithms that process one incomplete and/or corrupted measurement vector at a time. Thus GROUSE and GRASTA operate with considerably less computation than the other algorithms, while at the same time allowing flexibility for realtime applications such as tracking and anomaly detection. I will present these algorithms and show their application to subspace tracking, matrix completion, robust PCA, and background and foreground separation in video surveillance.  
Andrew John Beveridge (Macalester College)  Cops and Robbers on Geometric Graphs 
Abstract: In the game of cops and robbers, one robber is pursued by a set of cops on a graph G. In each round, these agents move between vertices along the edges of the graph. The cop number c(G) denotes the minimum number of cops required to catch the robber in finite time. We study the cop number of geometric graphs. We prove that c(G)  
Daniel Boley (University of Minnesota)  An Asymmetric Laplacian and Commute Times for Directed Graphs. 
Abstract: Directed graphs are better than undirected graphs in capturing the connectivity in many applications like the WWW and networks of wireless devices. For undirected graphs, it is well known how to obtain the expected intervertex average commute times from the graph Laplacian matrix. We show the same formulas hold in the case of strongly connected directed graphs. Our result is obtained by deriving a close relation between the Laplacian with a particular scaling and the socalled Fundamental matrix for an associated random walk over the graph. We find that the commute times still form a metric and give bounds in terms of the stationary probabilities for the random walk. Using a simple model of wireless devices, we observe that these commute times can differ from those obtained from a previously proposed symmetrized Laplacian derived from a related weighted undirected graph.  
Robert Calderbank (Duke University)  Managing Interference in Wireless Networks 
Abstract: We consider a framework for fullduplex communication in adhoc wireless networks recently proposed by Dongning Guo. An individual node in the wireless network either transmits or it listens to transmissions from other nodes but it cannot to both at the same time. There might be as many nodes as there are 48 bit MAC addresses but we assume that only a small subset of nodes contribute to the superposition received at any given node in the network. We use ideas from compressed sensing to show that simultaneous communication is possible across the entire network. Our approach is to manage interference through configuration rather than to eliminate or align it through extensive exchange of finegrained Channel State Information. Our approach to configuration makes use of old fashioned coding theory. 

Paolo Codenotti (University of Minnesota)  On the Group Isomorphism Problem 
Abstract: We consider the problem of testing isomorphism of groups of order n
given by Cayley tables.
The $n^{log n}$ bound on the time complexity for the general case has
not been improved over
the past four decades. We demonstrate that the obstacle to efficient
algorithms is the presence
of abelian normal subgroups; we show this by giving a polynomialtime
isomomrphism test for
"semisimple groups,'' defined as groups without abelian normal
subgroups (joint work L. Babai,
Y. Qiao, in preparation). This concludes a project started with with
L. Babai, J. A. Grochow, Y. Qiao
(SODA 2011). That paper gave an $n^{loglog n}$ algorithm for this
class, and gave polynomialtime
algorithms assuming boundedness of certain parameters. The key
ingredients for this algorithm are:
(a) an algorithm to test permutational isomorphism of permutation
groups in time polynomial in the
order and simply exponential in the degree; (b) the introduction of
the "twisted code equivalence problem,"
a generalization of the classical code equivalence problem (which asks
whether two sets of strings over a
finite alphabet are equivalent by permuting the coordinates), by
admitting a group action on the alphabet;
(c) an algorithm to test twisted code equivalence; (d) a reduction of
semisimple group isomorphism to
permutational isomorphism of transitive permutation groups under the
given time limits via
twisted code equivalence. The twisted code equivalence problem and the problem of permutational isomorphism of permutation groups are of interest in their own right. In this talk I will give the definitions of the problems that make up the overall plan, show how they fit together, and then give some details on the algorithm to test permutational isomorphism of transitive groups. This talk is based on joint work with: Laszlo Babai (University of Chicago), Joshua A. Grochow (University of Chicago) and Youming Qiao (Tsingua University). 

Isabel K. Darcy (University of Iowa)  Topology, Graphs, and Data 
Abstract: I will give two short introductory talk. First, I will describe a graph where the vertices represent knots and the knots are connected by an edge if one can be converted to the other via an operation such as changing one crossing. These graphs were developed to better understand the action of proteins which can knot circular DNA. Some proteins will cut DNA and change the DNA configuration before resealing the DNA. Thus, if the DNA is circular, the DNA can become knotted. Second, I will give a very elementary introduction to algebraic topology and how it can be used in a variety of applications. 

Lars Eldén (Linköping University)  Simultaneous clustering of timeevolving graphs 
Abstract: Spectral partitioning is a wellknown method for the clustering and partitioning of graphs. The Fiedler vector of the graph Laplacian is used to reorder the vertices of the graph. This makes it possible to find a partitioning that is rather close to being optimal. Alternatively the Fiedler vector can be computed using the adjacency matrix of the graph. We discuss the generalization of this method to tensors, which made up from a graph that evolves with time. Our approach is based on a generalization of the Rayleigh quotient characterization of the Fiedler vector. The solution of the tensor Rayleigh quotient maximization problem is computed using a tensorKrylov method. A couple of examples are given, with a clustering of a sparse tensor obtained from the Enron email data set.  
Brian Eriksson (University of WisconsinMadison), Robert Nowak (University of WisconsinMadison)  Trees, Graphs and Clusters: Learning Network Structure through Clustering 
Abstract: Graphs are commonly used to represent complex networks, such as the internet or biological systems. The structure of a graph can be inferred by clustering vertices based on dissimilarity measures correlated with the underlying graphdistances. For example, internet hosts can be clustered by measured latencies or traffic correlations and genes can be clustered according to responses to varied experimental conditions. These clusters reveal the structure of the underlying graph; e.g., internet routing or functional pathways in biological systems. This tutorial talk will discuss theory, methods, and applications of graphlearning based on clustering. Learning with missing or incomplete data, which is the norm in practice, will be a key theme in the discussion. The theory draws from ideas in statistical learning, spectral and subspace clustering, and matrix completion. The main concepts will be illustrated with examples from communication and biological network inference problems.  
Leonardo Espin  Solute uptake in vessels with oscillatory walls 
Abstract: We study computationally the absorption of a passive solute through the walls of an oscillating channel of finite length. The channel is filled with an incompressible fluid which carries the solute. The channel walls pulsate in a prescribed manner and these pulsations generate a fluid flow that modifies the solute transference to the medium that surrounds the channel, and consumes solute at a constant rate. The fluid motion is governed by the incompressible NavierStokes equations and noslip conditions at the solidliquid interface. We apply our numerical results to a two dimensional model of a surgical technique used for treating patients with coronary artery disease.  
Zoltan Furedi (Hungarian Academy of Sciences (MTA))  2cancellative families and 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 can lead to Sidon sets, sumfree
sets, etc). A family of sets F (and the corresponding family of 01
vectors) is called cancellative if A and Acup B determine B
(in case of A, Bin F). Equivalently, for all A,B,Cin F we have
Acup B=Acup C imply B=C. A family of sets F (and the corresponding code of 01 vectors) is called tcancellative if for all distict t+2 members A_1,..., A_t and B,C in F the union of A_1, ... A_t and B is different from the union of A_1, ..., A_t and C. For c_1(n) it is know that it equals to (1.5)^{n+o(n)}. Let c_t(n) be the size of the largest tcancellative code on n elements. We significantly improve the previous upper bounds of Korner and Sinaimeri, e.g., we show c_2(n)< 2^0.322n (for n> n_0). We also consider c_2(n,k), the size of the largest 2cancellative kuniform family on n vertices. This is in fact, a Turan type problem, and has many connections to other wellknown questions like the RuzsaSzemeredi (6,3)theorem. Using an algebraic construction we show that c_2(n,2k)=Theta (n^k) for each k when n tends to infinity. 

Donald Geman (Johns Hopkins University)  Learning Graphical Models by Competitive Assembly of Marginals 
Abstract: Learning highdimensional probability distributions with a very reduced number of samples is no more difficult than with a great many. However, arranging for such models to generalize well in the smallsample domain is hard. Our approach is motivated by compositional models and Bayesian networks, and designed to adapt to sample size. We start with a large, overlapping set of elementary statistical building blocks, or "primitives", which are lowdimensional marginal distributions learned from data. Subsets of primitives are combined in a legolike fashion to construct a probabilistic graphical model. Model complexity is controlled by adapting the primitives to the amount of training data and imposing strong restrictions on merging them into allowable compositions. In the case of binary forests, structure optimization corresponds to an integer linear program and the maximizing composition can be computed for reasonably large numbers of variables.  
Stuart Geman (Brown University), Matthew T Harrison (Brown University)  Learning the Network Structure of Cortical Microcircuits 
Abstract: The spiking dynamics of simultaneously recorded neurons from a small region of cortex reflect the local network structure of excitatory and inhibitory connections between observed neurons, as well as the time varying response of the neurons to their many unobserved and correlated inputs. Inference about the local network is easily contaminated by these unobserved nonstationary influences. We have been exploring conditional inference as an approach for statistically isolating local network dynamics from background nonstationarities. The approach is promising, but there remain many modeling questions and computational challenges.  
Golshan Golnari (University of Minnesota)  A Centrality Measure for Directed Graphs Using Average Hitting Cost 
Abstract: There are many relations and formulas derived for undirected graphs in the literature while there is much less analysis on directed graphs, although many problems can only be represented by directed graphs. In this work, using a special asymmetric Laplacian matrix introduced in a previous work, we present a unified formula for “the number of passages through nodes” which is used in our work to measure the centrality of a node in a network. Moreover, we extend the definition of Hitting/Commute cost for undirected graphs to directed graphs and show that commute cost has an interesting relation with commute time. This result is useful in modifying a network and reducing the overall cost. 

Leonidas Guibas (Stanford University)  Graphs of Shapes and Images 
Abstract: In this talk we consider certain graphs that arise out of matching shapes or images. We begin by exploring how to define isometricallyinvariant descriptors of shape neighborhoods and how these can be used to compute maps between shapes. We then analyze map networks with the goal of improving individual maps connecting the shapes. The fact that maps compose algebraically allows certain novel approaches that are not possible when edges encode only some proximity or similarity of the nodes. Finally we look at much larger networks of images connected via matching feature sets and look at how methods of spectral graph theory and algebraic topology can help both the construction and the simplification of such networks.  
J.T. Halbert (TexelTek)  Finding the ‘Needle’: Locating Interesting Nodes Using the KShortest Paths Algorithm in MapReduce 
Abstract: Understanding how nodes interconnect in large graphs is an important problem in many ﬁelds. We wish to ﬁnd connecting nodes between two nodes or two groups of source nodes. In order to ﬁnd these connecting nodes in huge graphs, we have devised a highly parallelized variant of a kshortest path algorithm that levies the power of the Hadoop distributed computing system and HBase distributed key/value store. We show how our system enables previously unobtainable graph analysis by ﬁnding these connecting nodes in graphs as large as one billion nodes or more on modest commodity hardware in a time frame of just minutes.  
Debra Knisley (East Tennessee State University)  Simulating complex networks via algorithms based on (t,r)regular graphs 
Abstract: The concept of degree regularity in a graph can be generalized by considering the cardinality of the neighborhood union of a set of vertices of size t. Since simple graphs, the most frequently used models, do not permit selfadjacency, we require the set of vertices of order t to be an independent (edgeless) set. For positive integers t and r , a graph is defined to be (t,r) –regular if every independent set of vertices of order t is collectively adjacent to exactly r vertices in the graph. If t > 1 and r >1 are fixed and the graph is sufficiently large, these graphs exhibit smallworld properties, especially for small t. In this work we investigate the spectrum of large (2,r)regular graphs and we define several algorithms that produce a smallworld and/or scalefree graph by systematically rewiring a random graph until it is “almost” a (t,r)regular graph for small t.  
Tamara G. Kolda (Sandia National Laboratories)  The Block TwoLevel ErdösRényi (BTER) Graph Model 
Abstract: Largescale graph analysis is becoming increasingly prevalent in our quest to understand the massive amounts of data arising from computer communications, social networks, email messages, purchase records, financial transactions, etc. In general, however, largescale data sets are not available for study because they may contain private data or may relinquish some competitive advantage. Consequently, most researchers must rely on graph models as a proxy for real data. Moreover, even when real data is available, graph models may be utilized to test methodologies at varying scales or under a range of scenarios. Modeling realworld largescale graphs, at scales upwards of a trillion nodes as proposed by Graph500, is the ultimate objective. In order to scale to large sizes, we require a model whose edges can be generated independently and thus in parallel. On the other hand, largescale graphs contain many smallscale and even minusculescale interactions, generically referred to as community structure, that should be accurately represented in any graph model. Existing graph models either fail to generate edges independently or fail to capture community structure. We present analysis of the Stochastic Kronecker Graph (SKG) model, which is the current generator for the Graph500 benchmark, including a comparison to the ChungLu model (also known the edge configuration model). We propose a new approach, the Block TwoLevel ErdösRényi (BTER) model which naturally contains community structure, even at the minuscule scale, but is able to generate edges independently and thus can scale to trillions of nodes.  
Dmitri Krioukov (University of California, San Diego)  Popularity versus Similarity in Growing Networks 
Abstract: Preferential attachment is a powerful mechanism explaining the emergence of scaling in growing networks. If new connections are established preferentially to more popular nodes in a network, then the network is scalefree. Here we show that not only popularity but also similarity is a strong force shaping the network structure and dynamics. We develop a framework where new connections, instead of preferring popular nodes, optimize certain tradeoffs between popularity and similarity. The framework admits a geometric interpretation, in which preferential attachment emerges from local optimization processes. As opposed to preferential attachment, the optimization framework accurately describes largescale Internet evolution, predicting new links in the Internet with a remarkable precision. The developed framework can thus be used for predicting new links in evolving networks, and provides a different perspective on preferential attachment as an emergent phenomenon.  
Diane Lambert (Google Inc.)  Statistics at Google Scale 
Abstract: From the perspective of a statistician, Google is a big statistical analysis engine that collects, organizes, summarizes and analyzes data to provide users with information anywhere, any time. This talk will present some of the challenges in combining huge amounts of data (some of which would not traditionally be thought of as data), wellestablished statistical principles and sometimes new twists to improve search, ads and apps. Diane Lambert is a statistician who has made a long career out of learning how to wrestle with, and sometimes tame, data. She is now a research scientist at Google, focused on solving Googlescale problems ranging from network monitoring to display ad effectiveness. 

Naomi Ehrich Leonard (Princeton University)  Flocks and Fleets: Collective Motion in Nature and Robotics 
Abstract: From bird flocks to fish schools, animals move together and respond to their environment in remarkable ways; their natural collective motion patterns appear well choreographed and their collective survival strategies seem ingenious. These animal group behaviors inspire design for groups of mobile, sensorequipped robots, where demanding cooperative sensing tasks, such as exploration and mapping in uncertain, dynamic environments in land, sea, air, or space, find their analogue in natural group behaviors, such as foraging and feeding. However, bioinspired design of this kind is not immediate because the natural mechanisms are not well understood. Mathematical modeling and analysis play a critical role in addressing this joint challenge to explain the enabling mechanisms in animal groups and to define provable mechanisms for robotic groups. A common framework based on notions of synchrony will be used to discuss connections among spatial pattern, information passing, and collective behavior in robot and animal networks. Applications to be presented include the design of an adaptive ocean observation system using a fleet of underwater robotic vehicles and an investigation of motion and decisionmaking in bird flocks and fish schools. 

Naomi Ehrich Leonard (Princeton University)  Information passing and collective animal behavior 
Abstract: Information passing through social interactions in moving animal groups, such as bird flocks and fish schools, is credited both with improving group responsiveness to external environmental stimuli and with maintaining group cohesiveness in the presence of uncertainty. Agentbased dynamical models with interaction terms that enable information diffusion have been used successfully to reproduce a range of observed collective motions. I will discuss analytic approaches for examining group decision making and exploring group robustness to uncertainty. Of particular interest is the role of the topology of the interconnections among individuals on the emergent outcomes and performance at the level of the group.  
Gilad Lerman (University of Minnesota)  Recent Ideas on Subspace Clustering 
Abstract: Motivated by talks from the first day of this workshop, we discuss in more detail modelling data by multiple subspaces, a.k.a., subspace clustering. We emphasize various theoretical results supporting the performance of some of these algorithms. In particular, we study in depth the minimizer obtained by a common energy minimization and its robustness to noise and outliers. We relate this work to recent works on robust PCA. We demonstrate how such theoretical insights guide us in practical choices and discuss some important practical questions raised in early talks of this workshop: choosing optimal local neighborhoods, modeling with missing data or high corruption and practical applications. This is based on joint works with Arthur Szlam, Yi Wang and Teng Zhang.  
Simon Lunagomez (Harvard University)  Statistical analysis of populations with interacting and interfering units a joint work with Edo Airoldi 
Abstract: A number of scientific endeavors of national and international interest today involve populations with interacting and/or interfering units.
In these problems, a collection of partial measurements about patterns of interaction and interference (e.g., social structure and familial relations) is available, in addition to the more traditional measurements about unitlevel outcomes and covariates. Formal statistical models for the analysis of this type of data have emerged as a major topic of interest in diverse areas of study. Probability models on networks date back to 1959. Along with empirical studies in social psychology and sociology from the 1960s, these early works generated an active community and a substantial literature in the 1970s. This effort moved into the statistical literature in the late 1970s and 1980s, and the past decade has seen a burgeoning literature in statistical physics and computer science. The growth of the World Wide Web and the emergence of online social networking websites such as Facebook and LinkedIn, and a host of more specialized professional networking communities has intensified interest in the study of networks, structured measurements and interference. In this tutorial, I will review a few ideas and open areas of research that are central to this burgeoning literature, placing emphasis on inference and other core statistical issues. Topics include elements of sampling and inference from nonignorable (network sampling) designs, parametric and nonparametric modeling, and estimation of latent processes on a network, with hints to the applications to social, biological and information networks that motivate these statistical problems. 

Mauro Maggioni (Duke University)  Multiscale analysis of dynamic graphs 
Abstract: Time series of graphs arise in many branches of sciences and applications. We discuss novel techniques for measuring distances between graphs, based on multiscale analysis of random walks, in a way that is both sensitive to localized changes and robust to ``noisy'' perturbations of the graph. From this we derive a framework for analyzing time series of graphs, together with fast algorithms. Finally, we discuss applications to families of graphs with multiscale structure, as well as real data mapped to dynamic graphs.  
Michael W. Mahoney (Stanford University)  Extracting insight from large networks: smallscale structures, largescale structures, and their implications for machine learning and data analysis 
Abstract: Recent empirical work has demonstrated that, although there often exists meaningful "small scale" structure (e.g., clustering structure around a single individual at the sizescale of roughly 100 individuals) in large social and information networks, analogous "large scale" structure (e.g., meaningful or statistically significant properties of tens or hundreds of thousands of individuals) either is lacking entirely or is of a form that is extremely difficult for traditional machine learning and data analysis tools to identify reliably. For example, there are often small clusters which provide a "bottleneck" to diffusions (e.g., diffusivebased dynamic processes of the form of interest in viral marketing applications and tipping point models of network dynamics); on the other hand, there are typically no large clusters that have analogous bottlenecks, and thus diffusionbased metrics (and the associated machine learning and data analysis tools) are simply much less meaningful (or discriminative or useful) if one is interested in analyzing the network at large sizes. This empirical work will be reviewed in some detail; its implications for extracting insight from large networks with popular machine learning and data analysis tools will be discussed; and several examples of novel machine learning and data analysis tools that were developed in response to these observations will be discussed.  
Gyan Ranjan (University of Minnesota)  On the von Luxburg Approximation for Hitting and Commute Times in Large Dense Digraphs 
Abstract: We explore the von Luxburg approximation for hitting and commute times in random walks between the vertices of a graph. Recent studies have established, in particular, that the lengths of random commutes, between a sourcedestination pair in large dense simple undirected graphs, can be well approximated in terms of scaled degrees of the endpoint vertices. These results implicate the relevance of commute times, or the lack of it, as a distance in machine learning tasks. Several corrected distances, based on commute times, have therefore been proposed to rectify these shortcomings. In this work, our principal contribution is to extend the von Luxburg approximation to the general case of strongly connected directed graphs, albeit in terms of the steadystate stationary probability distribution. Our methodology does not depend on the interplay between undirected graphs and electrical networks, an analogy on which previous studies rely heavily and, more importantly, one that does not have a parallel for directed graphs with which we deal in this work. Therefore, in so doing, we develop a theoretical framework to further generalize the von Luxburg approximations and establish conditions under which these approximations holds for the general case.  
Venkatesh Saligrama (Boston University)  Sparse Signal Processing Bounds on Large Graphs 
Abstract: Sparse signal processing on graphs arises in several applications including IP networks, wireless sensor networks (WSN) and infection propagation. For instance, in IP and WSNs a key problem is to identify a sparse subset of congested links and/or failing sensor nodes from path measurements. We develop sample complexity bounds for the number of path measurements required to recover such sparse subsets associated with the graph. To develop sample complexity scaling bounds we consider random path measurements associated with random walks on large graphs. Our main result is that sample complexity, namely, the number of path measurements required for sparse signal processing on expandertype graphs with path measurements is similar to that required when no such constraintsas in compressive sensing with random projections  are imposed on the measurements.  
Guillermo R. Sapiro (University of Minnesota)  Network analysis of human brains 
Abstract: In this talk I will describe recent results on large populations of brain connectivity networks. We analyze sex and kinship relations and their effects in brain networks metrics and topologies. The reported results are obtained in collaboration between the team of Paul Thompson at UCLA and my team at the UofM, in particular Neda Jahanshad and Julio DuarteCarvajalino.  
Purnamrita Sarkar (University of California, Berkeley)  Nonparametric Link Prediction 
Abstract: We propose a nonparametric link prediction algorithm for a sequence of graph snapshots over time. The model predicts links based on the features of its endpoints, as well as those of the local neighborhood around the endpoints. This allows for different types of neighborhoods in a graph, each with its own dynamics (e.g, growing or shrinking communities). We prove the consistency of our estimator, and give a fast implementation based on localitysensitive hashing. Experiments with simulated as well as three realworld dynamic graphs show that we outperform the state of the art, especially when sharp fluctuations or nonlinearities are present in the graph sequence.  
James Sharpnack (Carnegie Mellon University)  Sparsistency of the Edge Lasso over Graph 
Abstract: The fused lasso was proposed recently to enable recovery of highdimensional patterns which are piecewise constant on a graph, by penalizing the L1norm of differences of measurements at nodes that share an edge. While there have been some attempts at coming up with efficient algorithms for solving the fused lasso optimization, a theoretical analysis of its performance is mostly lacking except for the simple linear graph topology. In this paper, we investigate sparsistency of fused lasso for general graph structures, i.e. its ability to correctly recover the exact support of piecewise constant graphstructured patterns asymptotically (for largescale graphs). To emphasize this distinction over previous work, we will refer to it as Edge Lasso. We focus on the (structured) normal means setting, and our results provide necessary and sufficient conditions on the graph properties as well as the signaltonoise ratio needed to ensure sparsistency. We examplify our results using simple graphstructured patterns, and demonstrate that in some cases fused lasso is sparsistent at very weak signaltonoise ratios. In other cases, it performs no better than thresholding the difference of measurements at nodes which share an edge.  
Aarti Singh (Carnegie Mellon University)  Robust and Efficient Spectral Clustering of Large Graphs 
Abstract: Despite the empirical success of spectral clustering, its performance under noise and incomplete data is not well understood. This talk will provide a precise characterization of the misclustering rate of spectral clustering for large graphs. Using minimax theory, we establish information theoretic lower bounds on the amount of noise any clustering algorithm can tolerate and demonstrate that spectral clustering is nearoptimal. The gap relative to the optimal performance is the statistical price that needs to be paid for computational efficiency. Using this analysis, we propose a novel spectral method which optimizes the number of edge similarities that need to be measured to guarantee consistent recovery of the clusters. In high dimensional settings, the clusters can be very small relative to the size of the graph and are often characterized by a few relevant features. To enable simultaneous extraction of such a cluster and the relevant features, we consider a sparsity penalized spectral biclustering method and compare its performance to minimax limits. 

Blair Dowling Sullivan (Oak Ridge National Laboratory)  Can We Quantify & Exploit Treelike Intermediate Structure in Complex Networks? 
Abstract: Large complex networks naturally represent relationships in a variety of settings, e.g. social interactions, computer/communication networks, and genomic sequences. A significant challenge in analyzing these networks has been understanding the “intermediate structure” – those properties not captured by metrics which are local (e.g. clustering coefficient) or global (e.g. degree distribution). It is often this structure which governs the dynamic evolution of the network and behavior of diffusionlike processes on it. Although there is a large body of empirical evidence suggesting that complex networks are often “treelike” at intermediate to large sizescales (e.g. work of Boguna et al in physics, Kleinberg on internet routing, and Chung & Lu on powerlaw graphs), it remains a challenge to take algorithmic advantage of this structure in data analysis. We discuss several approaches and heuristics for quantifying and elucidating treelike structure in networks, including various treedecompositions and Gromov's deltahyperbolicity. These approaches were developed with very different "treelike" applications in mind, and thus we discuss the strengths and shortcomings of each in the context of complex networks and how each might aid in identifying intermediatescale structure in these graphs.  
Neel Sundaresan (eBay)  Graphs in Network Commerce and Their Applications 
Abstract: A networked marketplace like eBay where multiple buyers and sellers participate at the scale of hundreds of millions, interesting network structures exist. These these structures in buyingselling relationship between buyers and sellers influence any typical algorithm on site like search, recommender systems, and reputation computation. Graph structures also exists in query relationship space. We discuss these structures and use of these structures in design of algorithms in search, recommender systems, and trust systems.  
Daniel L Sussman (Johns Hopkins University)  Dot Product Embedding in Large (Errorfully Observed) Graphs with Applications in Statistical Connectomics 
Abstract: A stochastic block model is a kind of heterogeneous ErdosRenyi random graph model where the probability of an edge between any pair of vertices is only a function of the cluster to which those vertices belong. This is equivalent to a conditionally independent edge random graph model, where each vertex has associated with it one of finitely many latent vectors (one per cluster). We consider a special case of the conditionally independent edge random graph model called the random dot product graph, in which the dot products of the latent vectors associated with each vertex are the edge probabilities. We prove that a dot product embedding (DPE) is sufficient to correctly assign all but a neglible faction of the vertices to their respective clusters. We apply DPE in both an unsupervised and a semisupervised numerical experiments, demonstrating efficacy in both domains. Moreover, we consider that perhaps, graphs are only observed errorfully. Specifically, there is a quantity/quality tradeoff: given a fixed amount of time, one could more accurately sample fewer edges, or less accurately sample more edges. We demonstrate that we can numerically estimate the optimal tradeoff for a few specific exploitation tasks.  
Caroline Uhler (University of Minnesota)  Geometry of maximum likelihood estimation in Gaussian graphical models 
Abstract: We study maximum likelihood estimation in Gaussian graphical models from a geometric point of view. In current applications of statistics, we are often faced with problems involving a large number of random variables, but only a small number of observations. An algebraic elimination criterion allows us to ﬁnd exact lower bounds on the number of observations needed to ensure that the maximum likelihood estimator exists with probability one. This is applied to bipartite graphs and grids, leading to the ﬁrst instance of a graph for which the maximum likelihood estimator exists with probability one even when the number of observations equals the treewidth of the graph.  
Divyanshu Vats (University of Minnesota)  Necessary Conditions for SetBased Graphical Model Selection 
Abstract: Graphical model selection, where the goal is to estimate the graph underlying a distribution, is known to be computationally intractable in general. An important issue is to study theoretical limits on the performance of graphical model selection algorithms. In particular, given parameters of the underlying distribution, we want to find a lower bound on the number of samples required for accurate graph estimation. When deriving these theoretical bounds, it is common to treat the learning problem as a communication problem where the observations correspond to noisy messages and the decoding problem infers the graph from the observations. Current analysis of graphical model selection algorithms is limited to studying graph estimators that output a unique graph. In this paper, we consider graph estimators that output a set of graphs, leading to setbased graphical model selection (SBGMS). This has connections to listdecoding where a decoder outputs a list of possible codewords instead of a single codeword. Our main contribution is to derive necessary conditions for accurate SBGMS for various classes of graphical models and show reduction in the number of samples required for consistent estimation. Further, we derive necessary conditions on the cardinality of the setbased estimates given graph parameters.  
Joshua Tzvi Vogelstein (Johns Hopkins University)  Large Graph Classification: Theory and Statistical Connectomics Applications 
Abstract: The field of pattern recognition (classification) is dominated by vectorvalued data, in terms of both theory and applications. Here, we develop novel classifiers that natively operate in graphvalued domains; specifically, we develop both parametric and nonparametric classifiers. The parameter classifier, which we dub the “Signal Subgraph Classifier”, has the advantage of consistently yielding an a selection of vertices/edges which are deemed informative with regard to the classification task at hand, under and independent edge model (and more generally). The nonparametric classifier, dubbed the “GraphMatched Frobenius Norm kNearestNeighbor Classifier”, is proven to be universally consistent, both when vertices are labeled and when they are not. This proof, however, relies on a perfect graph matching algorithm, and graph matching is known to be NPhard. So, we developed a novel fast inexact graph matching algorithm, based on relaxing the constraint set to its convex hull. We prove that the optimal solution to this relaxation is identical to the original problem’s optimal solution whenever the graphs are undirected and isomorphic, even though our algorithm runs in O(n^3) time. All three algorithms outperform the previous stateoftheart in their respective domains. For future work, we discuss the possibility of using “Dot Product Embedding” to classifier even larger graphs.  
Rebecca Willett (Duke University)  Regularized Online Optimization: Tracking Regret, Risk Bounds, and Applications to Dynamic Networks 
Abstract: Online optimization methods are useful in a variety of applications with sequential observations of a dynamic environment. Often such methods are designed to minimize an accumulated loss metric, and the analysis techniques are appealing because of their applicability in settings where observations cannot be considered independent or identically distributed, and accurate knowledge of the environmental dynamics cannot be assumed. However, such analyses may mask the role of regularization and adaptivity to environmental changes. This work explores regularized online optimization methods and presents several novel performance bounds. Tracking regret bounds relate the accumulated loss of such an algorithm with that of the best possible dynamic estimate that could be chosen in a batch setting, and risk bounds quantify the roles of both the regularizer and the variability of the (unknown) dynamic environment. The efficacy of the method is demonstrated in an online Ising model selection context applied to U. S. Senate voting data.  
Hyokun Yun (Purdue University)  Quilting Stochastic Kronecker Product Graphs to Generate Multiplicative Attribute Graphs 
Abstract: We describe the first subquadratic sampling algorithm for the Multiplicative Attribute Graph Model (MAGM) of Kim and Leskovec (2010). We exploit the close connection between MAGM and the Kronecker Product Graph Model (KPGM) of Leskovec et al. (2010), and show that to sample a graph from a MAGM it suffices to sample small number of KPGM graphs and quilt them together. Under mild technical conditions our algorithm runs in O(log_2(n)^3 E) time, where n is the number of nodes and E is the number of edges in the sampled graph. We demonstrate the scalability of our algorithm via extensive empirical evaluation; we can sample a MAGM graph with 8 million nodes and 20 billion edges in under 6 hours. 
Dennis Amelunxen  Universität Paderborn  9/1/2011  10/29/2011 
Brendan P.W. Ames  University of Minnesota  8/31/2011  8/30/2012 
Andrew A. Anda  St. Cloud State University  10/24/2011  10/28/2011 
Ery AriasCastro  University of California, San Diego  9/24/2011  10/29/2011 
Amir Asiaee Taheri  University of Minnesota  10/23/2011  10/28/2011 
Frank Aurzada  TU Berlin  10/4/2011  10/8/2011 
Bubacarr Bah  University of Edinburgh  9/15/2011  12/15/2011 
Laura Balzano  University of WisconsinMadison  10/25/2011  10/28/2011 
Arindam Banerjee  University of Minnesota  9/1/2011  6/30/2012 
Imre Barany  Hungarian Academy of Sciences (MTA)  9/25/2011  10/2/2011 
Peter W. Bates  Michigan State University  10/9/2011  10/10/2011 
Witold Marek Bednorz  University of Warsaw  9/25/2011  10/1/2011 
Andrew John Beveridge  Macalester College  9/1/2011  5/15/2012 
Sergey G Bobkov  University of Minnesota  9/1/2011  6/30/2012 
Daniel Boley  University of Minnesota  10/24/2011  10/28/2011 
Shyam Boriah  University of Minnesota  10/24/2011  10/28/2011 
James Joseph Brannick  Pennsylvania State University  10/23/2011  10/26/2011 
Robert Calderbank  Duke University  10/24/2011  10/27/2011 
Luca Capogna  University of Minnesota  8/15/2011  6/10/2012 
Aycil Cesmelioglu  University of Minnesota  9/30/2010  8/30/2012 
Frédéric Chazal  INRIA Saclay  ÎledeFrance  10/23/2011  10/28/2011 
Guangliang Chen  Duke University  10/23/2011  10/28/2011 
David Chock  University of Michigan  10/8/2011  10/10/2011 
Paolo Codenotti  University of Minnesota  9/1/2011  8/30/2012 
Katherine Cramer  University of Minnesota  10/9/2011  10/10/2011 
Mihai Cucuringu  Princeton University  10/24/2011  10/29/2011 
Jintao Cui  University of Minnesota  8/31/2010  8/30/2012 
Steven Damelin  Georgia Southern University  10/23/2011  10/27/2011 
Isabel K. Darcy  University of Iowa  9/1/2011  6/30/2012 
Brenda L. Dietrich  IBM  10/9/2011  10/10/2011 
Dainius Dzindzalieta  Vilnius State University  9/1/2011  12/31/2011 
Lars Eldén  Linköping University  10/2/2011  10/30/2011 
Jordan Ellenberg  University of WisconsinMadison  10/26/2011  10/28/2011 
Brian Eriksson  University of WisconsinMadison  10/23/2011  10/28/2011 
Leonardo Espin  NONE  9/1/2011  6/30/2012 
Qiang Fu  University of Minnesota  10/24/2011  10/28/2011 
Zoltan Furedi  Hungarian Academy of Sciences (MTA)  9/22/2011  11/21/2011 
Carlos Andres GaravitoGarzon  University of Minnesota  9/8/2011  6/30/2012 
Donald Geman  Johns Hopkins University  10/25/2011  10/28/2011 
Stuart Geman  Brown University  10/25/2011  10/28/2011 
Subhroshekhar Ghosh  University of California, Berkeley  9/25/2011  10/1/2011 
Robert Ghrist  University of Pennsylvania  10/8/2011  10/11/2011 
Anna Gilbert  University of Michigan  10/9/2011  10/10/2011 
Golshan Golnari  University of Minnesota  10/24/2011  10/28/2011 
Wuming Gong  University of Minnesota  10/24/2011  10/28/2011 
Kara Greenfield  Worcester Polytechnic Institute  10/23/2011  10/28/2011 
David Guarrera  Booz Allen Hamilton Inc. (BAH)  10/23/2011  10/29/2011 
Leonidas Guibas  Stanford University  10/23/2011  10/26/2011 
Sinan Güntürk  Courant Institute of Mathematical Sciences  9/5/2011  10/4/2011 
Xiaoqin Guo  University of Minnesota  10/24/2011  10/28/2011 
J.T. Halbert  TexelTek  10/23/2011  10/29/2011 
Weiqiao Han  University of Minnesota  10/24/2011  10/28/2011 
Matthew T Harrison  Brown University  10/23/2011  10/28/2011 
Mary Ann Horn  Argonne National Laboratory  10/9/2011  10/10/2011 
Thomas Yizhao Hou  California Institute of Technology  10/8/2011  10/10/2011 
Yulia Hristova  University of Minnesota  9/1/2010  8/31/2012 
Daniel Kaslovsky  University of Colorado  10/23/2011  10/28/2011 
Debra Knisley  East Tennessee State University  10/23/2011  10/28/2011 
Tamara G. Kolda  Sandia National Laboratories  10/23/2011  10/28/2011 
Dmitri Krioukov  University of California, San Diego  10/23/2011  10/28/2011 
Akshay Krishnamurthy  Carnegie Mellon University  10/23/2011  10/28/2011 
Anastasios Kyrillidis  École Polytechnique Fédérale de Lausanne (EPFL)  9/24/2011  10/1/2011 
Diane Lambert  Google Inc.  10/6/2011  10/10/2011 
Naomi Ehrich Leonard  Princeton University  10/10/2011  10/12/2011 
Gilad Lerman  University of Minnesota  9/1/2011  6/30/2012 
Wenbo Li  University of Delaware  9/1/2011  5/30/2012 
Xin Liu  University of Minnesota  8/31/2011  8/30/2012 
Simon Lunagomez  Harvard University  10/26/2011  10/27/2011 
Shiqian Ma  University of Minnesota  8/31/2011  8/30/2013 
Mauro Maggioni  Duke University  10/26/2011  10/28/2011 
Michael W. Mahoney  Stanford University  10/23/2011  10/26/2011 
Tara Mallesh  University of Minnesota  10/24/2011  10/28/2011 
Juliane Manitz  GeorgAugustUniversität zu Göttingen  10/22/2011  10/30/2011 
Yi Mao  University of Tennessee  10/23/2011  10/28/2011 
Yu (David) Mao  University of Minnesota  8/31/2010  8/30/2012 
Gabriela Martínez  University of Minnesota  8/31/2011  8/30/2013 
Saurabh Mishra  Eagan High School  8/22/2011  12/31/2011 
Dimitrios Mitsotakis  University of Minnesota  10/27/2010  8/31/2012 
Richard M. Murray  California Institute of Technology  10/8/2011  10/10/2011 
Robert Nowak  University of WisconsinMadison  10/23/2011  10/25/2011 
Douglas W. Nychka  National Center for Atmospheric Research  10/8/2011  10/10/2011 
Luke Olson  University of Illinois at UrbanaChampaign  9/1/2011  12/31/2011 
Peter J. Olver  University of Minnesota  10/9/2011  10/10/2011 
Mary Therese Padberg  University of Iowa  8/16/2011  6/1/2012 
Candice Renee Price  University of Iowa  8/1/2011  7/31/2012 
Weifeng (Frederick) Qiu  University of Minnesota  8/31/2010  8/30/2012 
Dana Randall  Georgia Institute of Technology  10/9/2011  10/10/2011 
Gyan Ranjan  University of Minnesota  10/23/2011  10/28/2011 
Reza Rastegar  Iowa State University  10/23/2011  10/28/2011 
Alessandro Rinaldo  Carnegie Mellon University  10/23/2011  10/26/2011 
Firooz Sadjadi  Lockheed Martin  10/24/2011  10/28/2011 
Venkatesh Saligrama  Boston University  10/23/2011  10/26/2011 
Aliaksei Sandryhaila  Carnegie Mellon University  10/23/2011  10/28/2011 
Fadil Santosa  University of Minnesota  10/9/2011  10/10/2011 
Guillermo R. Sapiro  University of Minnesota  9/1/2011  5/31/2012 
Purnamrita Sarkar  University of California, Berkeley  10/26/2011  10/28/2011 
Varun Saxena  University of Minnesota  10/24/2011  10/28/2011 
Chehrzad Shakiban  University of Minnesota  10/9/2011  10/10/2011 
David H. Sharp  Los Alamos National Laboratory  10/8/2011  10/11/2011 
James Sharpnack  Carnegie Mellon University  10/23/2011  10/28/2011 
James Shook  National Institute of Standards and Technology  10/23/2011  10/29/2011 
Aarti Singh  Carnegie Mellon University  10/23/2011  10/26/2011 
Ravishankar Sivalingam  University of Minnesota  10/24/2011  10/28/2011 
Panagiotis E. Souganidis  University of Chicago  10/9/2011  10/10/2011 
Karsten Steinhaeuser  University of Minnesota  10/24/2011  10/28/2011 
Martin Strauss  University of Michigan  10/24/2011  10/27/2011 
Karthik Subbian  University of Minnesota  10/24/2011  10/28/2011 
Blair Dowling Sullivan  Oak Ridge National Laboratory  10/23/2011  10/26/2011 
Neel Sundaresan  eBay  10/26/2011  10/28/2011 
Daniel L Sussman  Johns Hopkins University  10/23/2011  10/28/2011 
Vladimir Sverak  University of Minnesota  10/8/2011  10/10/2011 
Arthur Szlam  University of Minnesota  8/31/2011  8/30/2012 
Minh Tang  Johns Hopkins University  10/23/2011  10/28/2011 
Jared Tanner  University of Edinburgh  9/20/2011  12/15/2011 
Caroline Uhler  University of Minnesota  8/31/2011  10/30/2011 
Panayot S Vassilevski  Lawrence Livermore National Laboratory  10/23/2011  10/28/2011 
Divyanshu Vats  University of Minnesota  8/31/2011  8/30/2012 
Miguel VelezReyes  University of Puerto Rico  10/23/2011  10/26/2011 
J. Tipan Verella  University of Virginia  10/23/2011  10/28/2011 
Joshua Tzvi Vogelstein  Johns Hopkins University  10/23/2011  10/28/2011 
Huahua Wang  University of Minnesota  10/24/2011  10/28/2011 
Jiaping Wang  University of Minnesota  10/9/2011  10/10/2011 
Lan Wang  University of Minnesota  9/1/2011  5/12/2012 
Yi Wang  University of Minnesota  10/24/2011  10/28/2011 
Rachel Ward  University of Texas at Austin  10/23/2011  11/5/2011 
Ali Wehbe  Fort Peck Community College  10/26/2011  10/28/2011 
Ke Wei  University of Edinburgh  10/10/2011  12/10/2011 
Elisabeth Werner  Case Western Reserve University  9/1/2011  12/20/2011 
Jonathan Tyler Whitehouse  Vanderbilt University  9/25/2011  10/2/2011 
Rebecca Willett  Duke University  10/24/2011  10/26/2011 
Steve Wright  Argonne National Laboratory  10/9/2011  10/29/2011 
Hautieng Wu  Princeton University  10/23/2011  10/28/2011 
Lingzhou Xue  University of Minnesota  9/1/2011  6/30/2012 
Feng Yi  University of Minnesota  10/24/2011  10/28/2011 
Wotao Yin  Rice University  9/26/2011  10/1/2011 
Hyokun Yun  Purdue University  10/22/2011  10/28/2011 
Ofer Zeitouni  University of Minnesota  9/1/2011  12/9/2011 
Teng Zhang  University of Minnesota  8/31/2011  8/30/2012 