MUGIZI ROBERT RWEBANGIRA Address: Comp Sci Dept, Phone: 412 965 6778 2300 6^th St. Washington, DC 20059 Email: rweba@cs.cmu.edu
OBJECTIVE	To obtain a position in academic or industrial research that will challenge me and allow me to explore new areas.
EDUCATION	Carnegie Mellon University Pittsburgh, PA PhD Computer Science, December 2008 Carnegie Mellon University Pittsburgh, PA MSc. Computer Science, May 2006 Howard University Washington, DC BSc. Systems and Computer Science, May 2002 Major GPA: 4.0/4.0 Summa Cum Laude Cumulative GPA: 3.96/4.0
EXPERIENCE 04/09 - Present	Post-doctoral Researcher and Adjunct Professor, Howard University Supervised all aspects of the required algorithms class for Computer Science undergraduates by designing the syllabus, delivering lectures, and setting homeworks and exams. Conducted independent research in the area of applied machine learning, considering topics such as ranking legislators and low-dimensional data visualization.
08/02 – 12/08	Graduate Student at Carnegie Mellon University Working with Avrim Blum and John Lafferty, I investigated various ways of using unlabeled data to help solve learning problems. I studied transductive classification using graph min-cut algorithms, transductive regression and augmenting features with unlabelled data.
08/07 – 12/07 08/04 – 12/04	Teaching Assistant for Computational Discrete Math Twice assisted Klaus Sutner in teaching this upper level undergraduate course in Discrete Math by grading home works, holding office hours, giving supporting lectures and delivering guest lectures as needed.
05/03 – 08/03	Summer Intern at IBM T.J. Watson Research Center Worked with Vittorio Castelli, Tessa Lau, Lawrence Bergman and Daniel Oblinger on learning from procedure traces. This project involved recording traces of several different users performing the same task and then automatically combining these traces into one “super trace” that could be run by a non-expert. I designed and implemented code to test the effectiveness of the system.
05/02 – 08/02	Summer Intern at IBM T.J. Watson Research Center Worked with John S. Davis, Marion S. Blount, April W. Savoy and Maria Ebling. Our research focused on finding ways to use information about the users’ Context to learn their habits. I helped design the system and implemented code to do data mining on the context information.
08/01 – 12/01	Tutorial Assistant for Operating Systems, Howard University Assisted Prof. Legand Burge in running a required senior course in Operating Systems. Graded homework, quizzes and exams.
05/01 – 08/01	Summer Intern at IBM T.J. Watson Research Center Worked with Daby Sow and Chatschik Bisdikian on mechanisms for Content Delivery. Our research focused on analyzing the effectiveness of prefetching in reducing latency to the end user. I wrote code to test our model, conducted a series of tests and co-wrote a paper.
01/01 - 05/01	Tutorial Assistant for Structure of Programming Languages, Howard University Assisted Legand Burge in running a required senior course on programming languages, compilers and theoretical issues in computing. Graded student homework, exams and projects.
03/99 – 05/02	Assistant to the Principle Investigator, Howard University Future Aerospace Science and Technology (HUFAST) · Performed research into distributed computing systems. · Researched issues pertaining to fault tolerance. · Worked extensively with CORBA and Java. · Co wrote and presented a paper at an international conference.
01/00 – 09/00	System Administrator, Computer Learning and Design Center (CLDC), Howard University Lead a team of four people in maintaining and upgrading a PC lab with 18 Linux machines. Upgraded software packages and replaced failed hardware components. Fixed problems with Network File System (NFS), printer configuration, and generally assisted lab users. Assisted junior co-workers in completion of their tasks and taught a class on Perl.
PUBLICATIONS	M.R. Rwebangira. Learning by Combining Native Features with Similarity Functions. Extended Abstract, WEHYS 08 M.R. Rwebangira, A. Blum. Learning by Combining Native Features with Similarity Functions. Technical Report M.R. Rwebangira, J. Lafferty. Local Linear Semi-supervised Regression. Technical Report M.R. Rwebangira. Techniques for Exploiting Unlabeled Data. Doctoral Thesis A. Blum, T-H. H. Chan, M. R. Rwebangira. A Random-Surfer Web-Graph Model. ANALCO 2006. A. Blum, P. Choi, J. Lafferty, B. Pantano, M.R. Rwebangira, X. Zhu. Person Identification in Webcam Images: An Application of Semi-Supervised Learning. ICML 2005 Workshop on Learning with Partially Classified Training Data. A. Blum, J. Lafferty, M.R. Rwebangira, R. Reddy. Semi-supervised Learning Using Randomized Mincuts. International Conference on Machine Learning 2004. D. Sow, G. Banavar, J.S. Davis II, J Sussman, M.R. Rwebangira Preparing the Edge of the Network for Pervasive Content Delivery, Advanced Topic Workshop onMiddleware for Mobile Computing with IFIP/ACM middleware conference L. Burge, M.R. Rwebangira. “Constructing Reliable Software Across the ORB”. International Conference of Minorities in Computing. ADMI 2000.
AWARDS & HONORS	Graduate Fellowship at Carnegie Mellon University -2002-2008 Highest GPA among graduating Seniors in Engineering - 2002 Golden Key International Honour Society -2001 Elected Cataloger for Local Chapter of Tau Beta Pi – 2001 Elected to Tau Beta Pi Honor Society –2000 Howard University Presidential Scholarship - Full four-year scholarship.(1998-2002) Dean's Honor Roll - 1998-2002 National Dean’s List – 99/00 Top Junior in Engineering – 2001
REFERENCES	Available upon request