Department of Mathematics

some math pictures


FONG SYMPOSIUM

The 2013 Fong Symposium will take place on April 9th and 10th at San Francisco State University. The featured speaker this year is Bin Yu , Chancellor's Professor in the Departments of Statistics and Electrical Engineering and Computer Science at UC Berkeley.
Professor Yu will give a public lecture on Tuesday April 9th, and a more specialized talk on Wednesday April 10th. Both talks will take place at San Francisco State's main campus. The public lecture (Science SCI210 at 4pm, preceded by coffee at 3:30) is open to a wide audience. The second talk (Thornton TH404 at 3pm) will be accessible to math and statistics undergraduates and graduate students interested in research, and will be followed by a reception (TH404, 4pm). Find the abstracts below, as well as in the attached flyers ( Day 1, and Day 2). We will appreciate your promoting the talks among your colleagues and students, and posting the flyers in your institution. We will be taking the speaker out to dinner on Tuesday evening. Please contact Javier Arsuaga at jarsuaga@sfsu.edu if you would like to schedule a meeting with the speaker, or to attend the dinner.

PUBLIC LECTURE
Tuesday April 9th, 2013
Coffee and Tea: 3:30-4:00pm, Room Science SCI 210
Public Lecture: 4:00 – 5:00pm, Room Science SCI210

Movie Reconstruction from Brain Signals: "Mind-Reading"
Least Squares, a time-tested method for fitting a linear model to data, attained prominence as far back as 1801 when it was used by Johan Carl Friedrich Gauss to fit astronomical data and predict the trajectory of the newly-discovered asteroid Ceres. Attempts have recently been made to modify the Least Squares method and answer one of the important questions in computational neuroscience: Can the vast quantities of high-dimensional neuroscience data available today be used to decode brain activities?
In a thrilling breakthrough at the intersection of neuroscience and statistics, penalized Least Squares methods have been used to construct a "mind-reading" algorithm that reconstructs movies from fMRI brain signals. The story of this algorithm is a fascinating tale of the interdisciplinary research that led to the development of the system which was selected as one of Time Magazine's 50 Best Inventions of 2011.

TECHNICAL LECTURE
Wednesday April 10th, 2013
Talk: 3:00-4:00pm, Room Thornton TH404
Reception: 4:00-5:00pm, Room TH404

Spectral clustering and high-dimensional stochastic block model for undirected and directed graphs
Network analyses have become the focus of research in recent years across many fields including biology, communication studies, economics, information science, organizational studies, and social psychology. Spectral clustering is a popular and computationally feasible method to discover communities or clusters of highly-connected actors which are an essential feature of the structure of many empirical networks.
Focusing on the High-dimensional Stochastic Block Model, a social network model with well-defined communities, this presentation will reveal the conditions for spectral clustering to correctly estimate the community membership of nearly all nodes. The asymptotic clustering results presented here are the first that allow the number of clusters in the model to grow with the number of nodes, hence the name "high-dimensional". We will discuss ongoing work on directed spectral clustering for networks whose edges are directed, and provide illustrative examples using data from the Enron and Caenorhabditis elegans networks.

BIOGRAPHY Bin Yu, Chancellor's Professor in the Departments of Statistics and Electrical Engineering & Computer Science at UC Berkeley, chaired the Statistics Department at Berkeley from 2009 to 2012. She has published more than 100 scientific papers in leading journals and conference proceedings on statistics, EECS, remote sensing and neuroscience. Her publications cover a wide range of research on empirical process theory, information theory (MDL), MCMC methods, signal processing, machine learning, high dimensional data inference (boosting and Lasso and sparse modeling in general), and interdisciplinary data problems. She has served on editorial boards including Annals of Statistics, Journal of American Statistical Association, and Journal of Machine Learning Research. Prof. Yu was a Guggenheim Fellow and co-recipient of the Best Paper Award of IEEE Signal Processing Society in 2006, and was the Tukey Memorial Lecturer for the Bernoulli Society in 2012. She is President-elect of the Institute of Mathematical Statistics (IMS) and an elected Fellow of IMS, AAAS, IEEE, and the American Statistical Association. She serves on the Scientific Advisory Board of the Institute for Pure and Applied Mathematics and on the Board of Mathematical Sciences and Applications of the National Academy of Sciences. She previously served as co-chair of the National Scientific Committee of the Statistical and Applied Mathematical Sciences Institute and on the Board of Governors of IEEE-IT Society.

Sponsors
The Fong symposium is made possible thanks to the generosity of Pamela Fong through a gift to the SFSU Mathematics Department. Additional sponsorship comes from the National Science Foundation. Lunch on Wednesday is sponsored by the SF State Women in Science and Engineering (WISE) Program.
Fong Symposium Organizing Committee
Javier Arsuaga
Alexandra Piryatinska
Mariel Vazquez
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