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Workshop The Mathematical Knowledge for Teaching (K-8): Why, What and How?
Show Schedules
- David Eisenbud, Jim Lewis: Welcome and Introduction
- Richard Schaar, Lee Shulman: Must Knowing How to Teach Be Limited by Teaching What One Knows?
- Deborah Ball: Teacher's Knowledge of Mathematics
- Dan Fallon, James Hiebert, Heather Hill, David Monk: What Evidence Exists About the Relationship Between Teachers' Knowledge of Mathematics and Student Achievement?
- Deborah Ball: What Mathematical Knowledge, Skill, and Habits Do Teachers Need in Order to Teach Effectively?
- Hyman Bass, Pam Grossman, Roger Howe, Liping Ma, Randolph Philipp: What Mathematical Knowledge, Skill, and Habits Do Teachers Need in Order to Teach Effectively?
- Deborah Ball: What Mathematical Knowledge, Skill, and Habits Do Teachers Need in Order to Teach Effectively?
- Zalman Usiskin: What Mathematical Knowledge, Skill, and Habits Do Teachers Need in Order to Teach Effectively?
- Robert Moses: What Mathematical Knowledge, Skill, and Habits Do Teachers Need in Order to Teach Effectively?
- Deborah Ball, Dan Fallon, Roger Howe, Richard (James) Milgram: What Can Mathematics Departments and Schools of Education Do to Help Teachers Develop Such Knowledge?
- Teachers' Knowledge of Mathematics, and Ways to Develop It.
- Ruth Cossey, William Velez: What Mathematical Knowledge, Skill, and Habits Do Teachers Need in Order to Teach Effectively?
- Jill Adler: What Mathematical Knowledge, Skill, and Habits do Teachers Need in Order to Teach Effectively?
- Carlos Cabana, Marta Civil, Lena Licon Khisty: What Mathematical Knowledge, Skill, and Habits Do Teachers Need in Order to Teach Effectively?
- Linda Curtis-Bey, Dan Fallon, Tom Fortmann, Jim Lewis, Cathy Seeley, Diane Spresser: What Are the Critical Next Steps with Respect to Policy? What are Directions for Research? Come Back to Evidence.
- Deborah Ball, Ruth Cossey, David Eisenbud, Jeremy Kilpatrick: What Are the Critical Next Steps with Respect to Policy? What are Directions for Research? Come Back to Evidence.
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Workshop Models of Real-World Random Networks
Show Schedules
- Walter Willinger: The Many Facets of Internet Topology.
- George Varghese: Streaming Algorithms for Traffic Analysis at High Speeds.
- Aaron Clauset: On the Bias of Traceroute Sampling.
- Volker Schmidt: Fitting and Simulation of Models for Telecommunication Access Networks.
- Susan Holmes: Multivariate Techniques for using Graph Structure and Covariates.
- Fan Chung Graham: A Duplication-Deletion Model for Random Power Law Graphs.
- Balaji Prabhakar: Some Engineering Uses of Randomization and Power Laws.
- Anthony Bonato: Infinite Limits and Models of the Web Graph.
- Michael Mitzenmacher: New Directions for Power Law Research.
- Kevin Lang: Cuts and Balance in Power Law Graphs.
- Milena Mihail: Algorithmic Performance in Complex Networks.
- P. Kumar: Scaling Laws in Information Theory for Wireless Networks.
- Dmitry Znamenskiy: Connectivity, Component Sizes and Distances in the Power Law Random Graphs.
- Marko Puljic: Synchrony in the Probabilistic Cellular Networks.
- Noam Berger: Spread of Viruses in Networks.
- Don Towsley: Epidemics on Networks, with Internet Applications.
- Ashish Goel: Sharp Thresholds in Geometric Random Graphs, with Algorithmic Implications.
- Kevin McCurley: Hierarchical Structure in Real World Networks.
- Raissa D'Souza: Competition-Induced Preferential Attachment.
- Mark Newman: Spatial Networks.
- Mike Steel: Random Autocatalytic Networks.
- Shweta Bansal: The Spread of Infectious Disease through Contact Networks.
- Chris Wiggins: Predicting Evolution from Topology: A Machine Learning Approach.
- Lea Popovic: Stochastic Models for Intra-cellular Networks.
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Workshop Visual Recognition
Show Schedules
- David Lowe: Features for Recognition
- Svetlana Lazebnik: Features for Visual Recognition
- Cordelia Schmid: Features of Visual Recognition
- Pierre Moreels: Features of Visual Recognition
- Andras Ferencz: Features of Visual Rcognition-Student Talks
- Stuart Andrews: Features for Visual Recognition- Student Talks
- Pietro Perona: BAFLE: Boosted Ambiguous Feature Learning
- Yali Amit: Features of Visual Recognition
- Dan Huttenlocher: Stochastic Models & Learning I
- Michael Jordan: Dirichlet Processes, Chinese Restaurant Processes, and All That.
- Andrew Zisserman: Stochastic Models & Learning I
- William Freeman: Stochastic Models & Learning I
- Alexei Efros: Stochastic Models & Learning II
- William Freeman: Stochastic Models & Learning II
- Alexei Efros: Images, Context of Visual Recognition
- Erik Sudderth: Learning Hierarchical Models of Scens, Objects, and Parts
- Song Chun Zhu: Images, Context of Visual Recognition
- Jitendra Malik: Shape Recognition
- Yann LeCun: Invariant Object Recognition with Energy-Based Models.
- Laurent Younes: Shape and Correspondence
- Shimon Ullman: Hierarchical Approaches and Language
- Kalanit Grill-Spector: Recognition and the Brain.
- Chris Manning: Recognizing Object Types, Attributes and Relations Through Language.
- Donald Geman: Hierarchical Approaches.
- Matthew Harrison: Unsupervised Learning of Invariances Using Video.
- Donald Geman: Hierarchical Approaches.
- Junmei Zhu: Dynamic Link Matching for Correspondence-based Visual Recognition.
- Eran Borenstein: Top-down Figure-ground Segmentation.
- Björn Ommer: Compositional Graphical Models for Categorization.
- Elliot Bernstein: Statistical Models for Visual Object Detection and Classification.
- Deva Ramanan: Tracking People and Recognizing Their Activities.
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Workshop Phase Transitions in Computation and Reconstruction
Show Schedules
- Yuval Peres: Phase Transitions in Reconstruction
- Jennifer Chayes: Prehistoric Spin Glasses
- Svante Janson: Robust Reconstruction on Trees
- James Martin: Reconstruction on Regular Trees and the Hard-Core Model
- Elchanan Mossel: Phase Transition in Phylogeny
- Alistair Sinclair: Recontruction Problems on Trees: A Simple Criterion for Impossibility
- David Levin: Phase Transition in Reconstructing Bias of Bit Sequences
- Mike Molloy: Sharp Thresholds for Random Constraint Satisfaction Problems
- Ryan O'Donnell: Correlation Distillation On Trees
- Krzysztof Oleszkiewicz: An Invariance Principle, with Some Applications to Boolean Functions
- Sourav Chatterjee: Universality results: A General Approach
- Christian Borgs: Proof of the Local REM-Conjecture for Number Partitioning.
- Random Voronoi Percolation in the Plane
- Riccardo Zecchina: "1-RSB" Clustering and Algorithms for Random Constraint Satisfaction Problems
- Elitza Maneva: An Alternative View of Survey Propagation for Satisfiability
- Balaji Prabhakar: The Asymptotic Behavior of Minimal Matchings in the Random Assignment Problem
- Belief Propagation for finding Max Weight Matching
- David Aldous: Local Weak Convergence and the Cavity Method
- David Gamarnik: Applications of the Local Weak Convergence Method to Random Graph Problems
- Gregory Sorkin: A Linear-Expected-Time Algorithm for Max Cut on Sparse Random Graphs
- Richard Kenyon: Simple Random Surfaces
- David Revelle: Mixing Times for Random Walks on Finite Lamplighter
- Van Vu: (Sharp) Thresholds for Random Regular Graphs
- Raissa D'Souza: Novel Behavior in a Simple Cellular Automaton Model of Traffic
- Marc Mezard: Hard Constraints on Random Graphs: From Lattice Glasses to Matching Problems
- Dimitris Achlioptas: Random Formulas Have Frozen Variables
- Andrea Montanari: Phase Transitions in Iterative Coding Systems
- Roman Kotecky: Phase Coexistence and Collapse of Supersaturation
- vladas sidoravicius: A Few Problems Related to Percolation of Binary Sequences
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Workshop Emphasis Week on Learning and Inference in Low and Mid Level Vision
Show Schedules
- Eero Simoncelli: Image Statistics, Efficent Coding and Visual Perception.
- David Mumford: Is There Simple Statistical Model of Generic Natural Images?
- Andrew Fitzgibbon: Applied Natural Image Statistics
- Alexei Efros: Data-driven Vision: Learning by Lookup
- Brendan Frey: Using Data-based Parameterizations to Efficient Learn Hierarchical Models.
- Song Chun Zhu: From Primal Sketch to 2 1/2 Sketch -- Shape from Shading, Stereo, and Motion.
- Michael Black: Image Satistics and Low Level Vision.
- William Freeman: Learning to Separate Shading From Paint .
- Andrew Blake: Fusion of Colour, Contrast and Stereo for Bi-layer Segmentation.
- Michael Isard: Estimating Stereo and Optic Flow Using Loopy Belief Propagation.
- Yair Weiss: Linear Programming, Belief Propagation and Low-level Vision.
- Dan Huttenlocher: Speeding up Belief Propagation for Low and Mid Level Vision
- Alan Yuille: Beyond BP? Approximate Inference and the DLR Equations.
- Ramin Zabih: Graph Cut Energy Minimization Algorithms for Computer Vision.
- Julian Besag, Raphael Gottardo: Microarray Imaging with MRFs and MCMC .
- David Donoho: Scaling Properties of Higher-order Image Statistics: Implications for Edge/Object Detection.
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Workshop Emphasis Week on Neurobiological Vision
Show Schedules
- Jonathan Touryan: Analysis of V1 Complex Cell Receptive Fields with Complex Stimuli"
- Matteo Carandini: Receptive Fields and supressive Fields in the Early Visual System
- Charles Gray: Multi-Neuron response Dynamics in Cat V1 to the Presentation of Time-Varying Natural Scenes.
- Gregory DeAngelis: Roles of Area MT in Stereo Vision.
- David Mumford: Open Discussion on the Role of Inhibitory Neurons and Methods of Recording from Large Numbers of Neurons.
- Jeff Hawkins: How the Cortex Works.
- Mike Lewicki: Density Component Models for Learning Heirarchical structure in Natural Images.
- Stuart Geman: Invariance and Selectivity in the Ventral Visual Pathway
- Tai Sing Lee: Cortical Mechanisms for Visual Interference.
- Antonio Torralba: How Scene Context Guides Attention.
- Bin Yu: A Better Staistical Model for Spike Trains
- Charles Anderson: Spatial-Frequency Tiling of V1 Simple Cells is Predicted by Signal-to-Noise Considerations.
- Bruno Olshausen: Sparse Coding amd Inference in Visual Cortex.
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Workshop Markov Chains in Algorithms and Statistical Physics
Show Schedules
- Persi Diaconis: Importance Sampling vs Markov chain Monte Carlo
- Thomas Hayes: General lower bounds for mixing of single-site dynamics on graphs
- Catherine Greenhill: Sampling regular graphs and a peer-to-peer network
- Pierto Caputo: Relaxation times in random shuffles and related Markov chains
- Ben Morris: The mixing time of the Thorp shuffle
- Elchanan Mossel: Shuffling by semi-random transpositions
- Sharad Goel: Mixing times for top to bottom shuffles
- Phillip Geissler: Finding transition pathways by Monte Carlo methods
- Glauber dynamics on trees: Boundary conditions and mixing time
- Fabio Martinelli: Phase ordering after a deep quench: the stochastic Ising and hard core gas models on a tree
- Mary Cryan: Approximately counting integral flows and cell-bounded contingency tables
- Russell Martin: Strong spatial mixing for lattice graphs with fewer colours
- Xia-Li Meng: Computing normalizing constants: A bridge between Statistical Physics and Statistical Computing
- Horng-Tzer Yau: Logarithmic Sobolev inequality for some models of random walks
- Andrea Montanari: Equilibration time for Glauber dynamics on random 3-XORSAT instances
- James Fill: Perfect simulation of perpetuities using Coupling Into And From The Past
- László Lovász: Volume computation: a status report
- Edward Adelson: Random sub-matrices of a given matrix
- Ravi Montenegro: Modified conductance and new Cheeger inequalities
- Christian Borgs: Slow mixing for Swendsen-Wang dynamics on the torus
- David Galvin: Slow mixing of local dynamics for proper 3-colourings on regular bipartite graphs
- Marcus Sammer: Bounds on fastest chains using transportation
- David Wilson: How many queries does it take to decide if there's a percolating cluster?
- Senya Shlosman: Markov chains of queueing networks and their infinite volume limits
- Jason Schweinsberg: Family size distributions for multitype Yule processes
- Rob van den Berg: Conditional correlation inequalities for percolation and contact processes
- Peter Winkler: Mixing among the reals
- Alan Frieze: Random walks on random graphs
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Workshop Introductory Workshop in Mathematical, Computational and Statistical Aspects of Image Analysis
Show Schedules
- David Mumford: Pattern Theory: Grenander's Ideas and Examples
- Trevor Hastie: Modern Classifier Design
- Olivier Faugeras: Variational Principles and PDE's of Computer Vision
- Pietro Perona: An Invitation to Visual Recognition
- Donald Geman: Strategies for Visual Recognition
- Edward Adelson: Image Statistics and Surface Perception
- Richard Baraniuk: Multiscale Geometric Analysis for Images
- David Mumford: Modeling Shape
- Olivier Faugeras: Variational Methods for Multimodal Image Matching: Theory and Applications
- David Donoho: Appearance Manifolds 2
- Luminita Vese: Energy Minimization for Cartoon & Texture Separation :U+V Models
- Eero Simoncelli: Statistical Image Models
- Bruno Olshausen: What We Know and Don't Know About Biological Vision
- Song Chun Zhu: Seeing as Statistical Inference
- Jitendra Malik: Ecological Statistics of Grouping and Figure-Ground Cues
- Jan Koenderink: Ecological Optics
- Olivier Faugeras: Variations on Image and Shape Warping, Statistics and Segmentation
- Joachim Buhmann: Learning and Image Segmentation
- David Donoho: More Interactions
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Workshop MSRI Workshop for Women in Mathematics: Introduction to Image Analysis
Show Schedules
- Ruzena Bajcsy: Image Analysis: History and Relation to Other Disciplines
- Jana Kosecka, Kathryn Leonard: Image Processing (Transform, Multiscale and Harmonic Analysis, Wavelet Transform)
- Kathryn Leonard: Image Contours, PDE's and Anisotropic Diffusion
- Ill-Posed Problems in Image Analysis: Statistical And Numerical Techniques.
- Chen Sagiv: Integrated Active Contours for Texture Segmentation.
- Jana Kosecka: Feature Detection, Feature Descriptions, Matching, Warping
- Jamylle Carter: Variational Methods for Image Denoising
- Kathryn Leonard: Shape Representations, Metrics and Matching
- Jana Kosecka: Motivating Problems and Applications
- Ruzena Bajcsy: Image Segmentation in Medical Imaging.
- Stella Yu: Segmentation of Natural Scenes-Graph-Cut Approaches.
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Workshop MSRI Program on Probability, Algorithms and Statistical Physics, Spring 2005 --- OPENING DAY, Thursday 13 January, 2005
Show Schedules
- David Aldous: Real -world Random Networks- Overview
- Jennifer Chayes: Random Networks; Observed Properties, Models, and Open Questions
- Alistair Sinclair: Markov Chain Monte Carlo Algorithims- Overview
- Fabio Martinelli: Glauber Dynamics with Equilibrium States: Recent Results and Open Problems
- Yuval Peres: Phase Transitions-Overview
- Marc Mezard: What Do We Know About Glass Phases? An Introduction, Illustrated by Some Examples from Computer Science
- Peter Winkler: Rapid Mixing: Some Old Problems and New Ideas