Mary McGlohon Graduate Assistant Machine Learning Department Carnegie Mellon University 5000 Forbes Ave., Pittsburgh, PA 15213 Office: Gates-Hillman Center 8221 - 412-268-3569 Email: mmcgloho+www@cs.cmu.edu

Job application materials

CV
Research Statement
Teaching Statement

I am a Ph.D. student in the Machine Learning Department at CMU's School of Computer Science, expecting to graduate in the second half of 2010. My advisors are Christos Faloutsos and Alan Montgomery. My thesis research involves finding patterns of network formation, evolution, and diffusion in real networks and applying these patterns to anomaly detection and marketing. I have enjoyed internships with Google, Microsoft, and PricewaterhouseCoopers. I have received support in my graduate studies from an NSF Graduate Research Fellowship and a Yahoo! Key Technical Challenges Grant.

I have also enjoyed internships with Google, Microsoft Live Labs and PricewaterhouseCoopers. I graduated from the University of Tulsa with Bachelor's degrees in Computer Science and Mathematics. I worked on AI research in Sandip Sen's lab, and also did research in applied math with Christian Constanda.

News, Fall 2009

I am TAing 15-381, Intro to AI.

I proposed my thesis in April 2009, with a proposed completion date of August 2010. A draft of my proposal is available here.

CV

Last updated July 2009: [pdf].

Research Interests

My field-related interests include the following:

• Knowledge discovery for the Web
• Link analysis
• Evolving networks
• Social networks
• Data mining applications to epidemiology and public health
• Data mining applications to marketing
• Anomaly detection in graphs

Conference Papers

• M. McGlohon, S. Bay, M. Anderle, D. Steier, and C. Faloutsos. SNARE: A Link Analytic System for Graph Labeling and Risk Detection SIG-KDD Paris, France. June 2009. [pdf]
• M. McGlohon and M. Hurst. Community Structure and Information Flow in Usenet: Improving analysis with a thread ownership model. International Conference on Weblogs and Social Media (ICWSM09). San Jose, Cali. May 2009. [pdf]
• M. McGlohon and M. Hurst. Considering the Sources: Comparing linking patterns in Usenet and blogs. International Conference on Weblogs and Social Media (ICWSM09). San Jose, Cali. May 2009. [pdf]
• M. Goetz, J. Leskovec, M. McGlohon, and C. Faloutsos. Modeling Blog Dynamics. International Conference on Weblogs and Social Media (ICWSM09). San Jose, Cali. May 2009. [pdf]
• L. Akoglu. M. McGlohon, and C. Faloutsos. RTM: Laws and a Recursive Generator for Weighted Time-Evolving Graphs. ICDM IEEE Int'l Conference on Data Mining Pisa, Italy, Dec. 2008. [pdf]
• M. McGlohon, L. Akoglu, and C. Faloutsos. Weighted Graphs and Disconnected Components: Patterns and a Generator. SIG-KDD Las Vegas, Nev., August 2008. [pdf][video (videolectures.net)]
• M. McGlohon, J. Leskovec, C. Faloutsos, N. Glance, and M. Hurst. Finding patterns in blog shapes and blog evolution. International Conference on Weblogs and Social Media. Boulder, Colo., March 2007. January 2007. [pdf] Tech Report (identical): CMU-ML-07-100.

• J. Leskovec, J, M. McGlohon, C. Faloutsos, N. Glance, and M. Hurst. Patterns of Cascading Behavior in Large Blog Graphs. Society of Industrial and Applied Mathematics- Data Mining. Minneapolis, Minn., April 2007. [pdf] Tech report (12 pgs): CMU-ML-06-113 [local]

• McGlohon, M. and S. Sen. Learning to cooperate in multi-agent systems by combining Q- learning and evolutionary strategy. World Conference on Lateral Computing, December 2004. [pdf] Best Student Paper Award.

Other Publications

• M. McGlohon, J. Leskovec,C. Faloutsos, N. Glance, M. Hurst. Information Propagation and Network Evolution on the Web. DA Project, Machine Learning Dept. Carnegie Mellon University. [pdf]
• M. McGlohon and C. Faloutsos. ADAGE: A Software Package for Analyzing Graph Evolution. Tech Report. CMU-ML-07-112. [pdf][software]

Talks

Feel free to re-use parts of these presentations. My policies for re-using them are 1) please credit me, and 2) don't change my fonts to Comic Sans.

• Tutorial at ICWSM, Graph Mining Techniques for Social Media Analysis [video (videolectures.net)] [gzipped pdfs]. (If you would like PPT version, please email me)
• Talk at Microsoft Live Labs over work on blogs: [ppt]
• Presentation in Analysis of Social Media seminar over graph mining techniques applied to blogs: [ppt]
• Talk at Sandia Labs over SDM07 work: [ppt]

Software

• ADAGE, Matlab add-in for analyzing evolving graphs. [tar.gz] See tech report for documentation: [pdf]

Data

I have compiled data from the U.S. Federal Election Commission, representing federal electoral campaign donations from individuals to committees and committees to candidates. A description, and the files, are located here.

Teaching Activities

I was TA for two courses while at CMU:

• 15-381: Intro to Artificial Intelligence, Profs. Manuela Veloso and Luis von Ahn
• 10-601: Machine Learning, Profs. William Cohen and Tom Mitchell

• MATH 1093: Math with Applications (algebra and calculus for non-majors), Fall 2003
• MATH 1083: Contemporary Mathematics (problem solving class for non-majors), Spring 2004, Summer 2004
• MATH 2014: Calculus I (taught weekly quiz section), Fall 2004

Graduate Coursework

In reverse chronological order. * indicates core MLD PhD curriculum.

• 36-462: Topics in Statistics: Chaos, Complexity, and Inference, SP09
• 99-701: College/University Teaching, FA08
• 36-835: Models and Methods for Networks, FA07
• 10-802: Analysis of Social Media, FA07
• 15-853: *Algorithms in the Real World, FA07
• 10-702: *Statistical Machine Learning, SP07
• 10-708: Probabilistic Graphical Models, FA06
• 36-728: Time Series Analysis, FA06Q1
• 36-720: Discrete Multivariate Analysis, FA06Q2
• 15-826: *Multimedia Databases and Data Mining, SP06
• 36-626: Mathematical Statistics II, SP06
• 10-701: *Machine Learning, FA05
• 10-705: *Intermediate Statistics, FA05

Other Activities

I have performed a number of service- and otherwise-related roles for SCS. They include:

• Databases group, lab manager. Page for Machines: (local)
• Dec/5 (TGs, maintenance of the grad lounge coke machine, various other SCS social events)
• Machine Learning Dept. Recruiting (Open House, maintaining contact with prospective students)
• SIGBOVIK 2007, 2008, 2009 program committee

Links

• Tensor Toolbox- From Tamara Kolda and team at Sandia National Labs. A tool for multidimensional arrays in MATLAB. I've found sptensor, the sparse tensor functions, especially useful.

• BlogPulse- Several tools for blog tracking and analysis. Developed by Nielsen Buzzmetrics, some friends in industry.

• Upcoming Conferences and Workshops- a handy reference for upcoming conference deadlines in AI, Data Mining, Machine Learning, and related fields.

Misc.

• M. McGlohon. MapReuse and MapRecycle: Two More Frameworks for Eco-Friendly Data Processing. SIGBOVIK Pittsburgh, Penn., April 2009. Paper: [pdf] "Bypassing the Filters" Award for Paper Most Likely to be Cited by a Real Paper. Which means you should cite it.
• M. McGlohon and Robert J. Simmons. Towards a Frequentist's Approach to Pascal's Wager. SIGBOVIK. Pittsburgh, Penn., April 2008. Paper: [pdf]
• M. McGlohon. Data Mining Disasters: A Report. SIGBOVIK. Pittsburgh, Penn., April 2008. Paper: [pdf] Talk: [ppt] Most-Least-In-Box Award
• (authors redacted) Optimal Censor Placement in Wireless Censor Networks. SIGBOVIK. Pittsburgh, Penn., April 2008. Paper: [pdf]
• M. McGlohon. Methods and Uses of Demoralizing Graphs. SIGBOVIK. Pittsburgh, Penn., April 2007. Paper: [pdf] Talk: [ppt]
• M. McGlohon. Fried Chicken Bucket Processes. SIGBOVIK. Pittsburgh, Penn., April 2007. [pdf] Y.J. Ringard Golden Broccolo Award for Culinary Excellence

Paul Erdos - Noga M. Alon - Phillip B. Gibbons - Christos Faloutsos - Mary McGlohon