Data MASTER Seminar

Data MASTER colloquium


Event: Q and A on the Data MASTER Scholarship (this is the reschedule of the event, which can cancelled due to the GW closure)

Time: Monday, March 26, 1-2pm

Place:  Phillips 730

Host: Yongwu Rong and other participating faculty

Description.  This event for undergraduates interested in the Data MASTER scholarship that we informed you earlier. The scholarship provides support for students to do research projects related to data-driven computation.  We will provide basic information, show you examples of past projects as well as possible projects offered by our faculty, and we will answer questions you may have.


Speaker:  Robert E. Kass, Carnegie Mellon University

Date and Time:  Monday, April 06, 2015, 10 - 11am.

Place: Monroe 110, 2115 G Street NW Washington, DC 20052 

Title: Statistical Thinking in Neuroscience

Experimenters are typically adept at applying standard statistical techniques, while computational neuroscientists are capable of formulating mathematically sophisticated data analytic methods to attack novel problems in data analysis. Yet, in many situations, statisticians proceed differently than those without formal training in statistics. What is different about the the way statisticians approach problems? I will give you my thoughts on this subject, and will illustrate with  analyses drawn from my own work, involving neural spike trains and neuroimaging. I will also touch on the notion of scientific reproducibility, and will comment on potential roles for Bayesian inference.

Robert E. (Rob) Kass received his Ph.D. in Statistics from the University of Chicago in 1980. His early work formed the basis for his book Geometrical Foundations of Asymptotic Inference, co-authored with Paul Vos. His subsequent research has been in Bayesian inference and, beginning in 2000, in the application of statistics to neuroscience. Kass is known not only for his methodological contributions, but also for several major review articles, including one with Adrian Raftery on Bayes factors (Journal of American Statistical Association, 1995) one with Larry Wasserman on prior distributions (Journal of American Statistical Association, 1996), and a pair with Emery Brown on statistics in neuroscience (Nature Neuroscience, 2004, also with Partha Mitra; Journal of Neurophysiology, 2005, also with Valerie Ventura). His book Analysis of Neural Data, with Emery Brown and Uri Eden, was published in 2014.

Kass has served as Chair of the Section for Bayesian Statistical Science of the American Statistical Association, Chair of the Statistics Section of the American Association for the Advancement of Science, founding Editor-in-Chief of the journal Bayesian Analysis, and Executive Editor (editor-in-chief) of the international review journal Statistical Science. He is an elected Fellow of the American Statistical Association, the Institute of Mathematical Statistics, and the American Association for the Advancement of Science. He has been recognized by the Institute for Scientific Information as one of the 10 most highly cited researchers, 1995-2005, in the category of mathematics (ranked #4). In 2013 he received the Outstanding Statistical Application Award from the American Statistical Association for his 2011 paper in the Annals of Applied Statistics with Ryan Kelly and Wei-Liem Loh. In 1991 he began the series of eight international workshops Case Studies in Bayesian Statistics, which were held every two years at Carnegie Mellon, and was co-editor of the six proceedings volumes that were published by Springer. He also founded and has co-organized the international workshop series Statistical Analysis of Neuronal Data, which began in 2002; the seventh iteration will occur in May, 2015.

Kass has been been on the faculty of the Department of Statistics at Carnegie Mellon since 1981 and served as Department Head from 1995 to 2004; he joined the Center for the Neural Basis of Cognition in 1997, and the Machine Learning Department (in the School of Computer Science) in 2007.

Data MASTER Seminar and Q & A on Data MASTER Program

Speakers: Data MASTER team and Students of Math 4981, Topics on Complex Networks.

Date and Time: Wednesday, December 3, 2014 11:30 - 1pm.

Place: Monroe 263

Abstract: Data MASTER (Data-driven Mathematics and Statistics Training, Education, & Research) is an NSF-funded program that we launched in Fall 2014. It aims at enhancing data-driven computational skills for GW undergraduates. As part of the program we offer selected courses that emphasize on Quantitative Exploration of Data (QED courses). This event will feature the following

(1) We will present QED courses for Spring 2015 and academic year 2015-2016. Questions concerning those courses and Data MASTER program will be answered.

(2) During Fall 2014, we offered Math 4891, Topics on Complex Networks, as a QED course. This event will feature presentations from students on their research projects.

All GW undergraduates interested in this program are welcome.

For more information on the Data MASTER program, check our website at

Title: Multiplexity and Multilevel Networks: Opportunities and Challenges

Speaker: Jonathon Mote, GW

Date and Time: Thursday, November 13, 2014 11:10am-12:25pm

Place: Monroe 114

Abstract: The tremendous increase in research on social networks has increased our understanding of these social structures, but the overwhelming majority of this research typically continues to be limited to either one type of social relationship or one network.  However, people are engaged in a wide range of social relationships and networks, whether in organizations or larger social environments.  In this regard, two areas in need of further research are multiplexity and multi-level network analysis.  Multiplexity is the phenomenon of multiple network ties, that is, the overlap of roles, exchanges, or affiliations in social relationships.  Multi-level network analysis investigates the interaction (and perhaps interdependence) between two or more networks, such as cross-linkages between two organizational networks.  This talk explores these two areas and discusses two case studies to highlight the opportunities and challenges with these approaches

Bio: Jonathon Mote is an Assistant Professor of Organizational Science at The George Washington University.  His research interests are primarily focused on the interrelationship between organizational environments and networks of science and innovation. His research has received funding from the National Science Foundation, the Department of Energy, the National Oceanic and Atmospheric Administration and Industry Canada.  Prior to joining GWU, Jonathon was an assistant professor of Management at Southern Illinois University.  From 2003 to 2009, Jonathon was an assistant research scientist at the University of Maryland.

Title: Complex Network Analysis using Mathematica

Speaker: Chenghang Du, GW

Date and Time: Tuesday, October 14, 2014 11:10am-12:25pm

Place: Monroe 114


This is a hands-on workshop aimed at undergraduate students and other interested audience. I will walk you through some examples of using Mathematica for complex network analysis. I will also outline a few potential projects, including collaboration network, Boolean dynamics, social network, and time series analysis. Students are welcomed to bring laptop to the workshop.

Title:Mathematics and Science of Complex Networks
Speaker: Yongwu Rong (GWU)
Time: September 18, 2014 Thursday, 11:10 - 12:25pm.
Location: Monroe 114.
Abstract: Complex networks arise naturally in many disciplines, from computer networks to interactions of biological cells, from air traffic design to social networks. Over the past decade, there has been a great deal of new interests in this field, largely due to the explosive amount of data on various networks. An urgent need now is to extract useful information to understand the structure of these networks.
This talk is a special lecture for Math 4981, Topics on Complex Networks. Part of the talk will be an overview of the topics, beginning with basic graph theory starting from Euler, to random graph theory due to Erdős and Rényi, followed by scale-free networks due to Barabasi. Time permitting, we will discuss our own work on biological networks and some of the computational issues.