This CV was last updated in December 2012.
For an updated CV, please visit my new mobile-friendly site: www.michaelchughes.com/cv.html
RESEARCH INTERESTS
My PhD research applies techniques from statistical machine learning (particularly hierarchical Bayesian models and nonparametric extensions) to create automated methods for extracting meaningful information from multimedia. I study theoretical methods for efficient inference in graphical models (e.g. MCMC sampling, variational methods) as well as practical ways to apply these methods to real-world data such as video clips or text documents.
Michael C. Hughes
RESEARCH INTERESTS
My PhD research applies techniques from statistical machine learning (particularly hierarchical Bayesian models and nonparametric extensions) to create automated methods for extracting meaningful information from multimedia. I study theoretical methods for efficient inference in graphical models (e.g. MCMC sampling, variational methods) as well as practical ways to apply these methods to real-world data such as video clips or text documents.
BROWN CAMPUS * Box 1910, CS Dept. * 115 Waterman St * Providence, RI 02906 mike<AT>michaelchughes<DOT>com |
EDUCATION
Brown University, Providence, RI. August 2010 - present.
PhD candidate in Computer Science. GPA: 4.0/4.0
Master of Science in Computer Science, May 2012.
* Research Area: machine learning, computer vision. Advisor: Erik Sudderth.
* Coursework: probabilistic methods, data-driven computer vision, machine learning, natural language processing.
Franklin W. Olin College of Engineering, Needham, MA Class of 2010
Bachelor of Science in Electrical and Computer Engineering. GPA 3.93/4.0
* Received four-year, full-tuition Olin Scholarship valued at $130,000
* Coursework: discrete math, computational modeling, artificial intelligence, probability and statistics, signals and systems, software systems, nonlinear dynamics and chaos
HONORS AND AWARDS
NSF Graduate Research Fellowship, Spring 2011
* Three year research stipend award.
* Honorable mention in Spring 2010.
National Defense Science and Engineering Graduate Fellowship, Spring 2011
* Three year research stipend award.
* Declined in order to accept NSF GRFP fellowship.
Meritorious Winner, Interdisciplinary Contest in Modeling, Winter 2007
* Simulated and analyzed U.S. Organ Transplant Network in 72-hour team contest [Report]
Presidential Scholar, Dept. of Education, U.S. Federal Government, May 2006
* Honored among 141 students nationwide for academics, leadership, and service
PUBLICATIONS
Hughes, M., Fox, E., and Sudderth, E. "Effective Split-Merge Monte Carlo Methods for Nonparametric Models of Sequential Data." In Advances in Neural Information Processing Systems (NIPS) 2012. South Lake Tahoe, Nevada, USA. [web] [PDF] [code].
Kim, D., Hughes, M., and Sudderth, E. "The Nonparametric Metadata Dependant Relational Model." In International Conference on Machine Learning (ICML), 2012. Edinburgh, Scotland, UK. [web] [PDF].
Hughes, M. and Sudderth, E. "Nonparametric Discovery of Activity Patterns from Video Collections." In
The Eighth IEEE Computer Society Workshop on Perceptual Organization in Computer Vision (POCV), 2012. Providence, Rhode Island, USA. [PDF].
RESEARCH EXPERIENCE
Research Intern, Topology Estimation for Multi-Camera Tracking, Summer 2009
MIT Lincoln Laboratory, Homeland Protection and Tactical Systems Division
* Prototyped multi-camera tracking system for facility-wide surveillance in nine-week project
* Developed statistical methods to estimate camera topology without a priori knowledge
* Recovered most ground-truth topological links in 4 camera network from only 8 min. of footage
* Presented results in hour-long, seminar-style talk to full-time research staff
Project Manager, Study on Network Formation Games, Summer 2008
Research In Industrial Projects for Students (RIPS) REU, Institute for Pure and Applied Mathematics, UCLA.
Project Sponsored by Microsoft Research Asia.
* Led team in two-month theoretical investigation of ad-hoc network formation by sel.sh agents
* Developed proofs and conjectures about connectivity in networks at Nash equilibria
* Found polynomial time approximation for NP-Hard best response calculation
* Presented poster at NetSci 2010 and prepared unpublished manuscript
Project Manager, Trustworthy Architecture for Distributed Systems, Fall 2009
Senior Capstone Program in Engineering, Olin College. Sponsored by IBM Research.
* Led 5 student team in designing trustworthy systems architecture for smart grid application
* Proposed hardware and software requirements and protocols for security and resilience
* Mentors: Dr. Mark Sheldon (Olin), Mr. Alex Morrow (IBM Fellow)
Novice Programmer Behavior Research, Sept 2007 – present
* Conducted independent research on programming behavior of novice CS students
* Authored paper accepted in Journal of Computer Science Education
* Mentor: Dr. Matthew Jadud (Allegheny College)
UNDERGRADUATE RESEARCH PUBLICATIONS
Hughes, M., Jadud, M., and Rodrigo, M. "String formatting considered harmful for novice programmers." Journal of Computer Science Education, Sept 2010. [Journal Link] [PDF of earlier draft]
Bei, X., Chen, W., Ercal-Ozkaya, G., Fu, X., Hughes, M., and McBride, S. "On Pure and Approximate Nash Equilibria in Betweenness Centrality Games." International School and Conference on Network Science (NetSci) 2010. Poster presentation. Cambridge, MA. [PDF]
LEADERSHIP AND SERVICE
Leader, Machine Learning Reading Group, Brown University. Fall 2012-Spring 2013.
* Organized weekly meetings of grad students from many departments
Curriculum Director, Engineering Discovery, Olin College Fall 2008-Spring 2010
* Managed 15 undergrads in developing hands-on lessons for 4th-8th graders
* Hosted workshop for 30 children to design, build, and launch bottle rockets
* Pioneered green energy workshop which earned over $750 in outside funding
English as a Second Language (ESL) Tutor, Olin College, Fall 2008
* Piloted one-on-one English reading and writing intervention program for international students
SKILLS
Languages: Proficient in Java, Python, C, Erlang, Matlab.
Communication: Technical writing for publication, public speaking
