Presentations:-
13
Oluwaseun Ademuwagun, Mugizi Rwebangira, Carolyn Rose "Predicting Marked Code Switching in African Languages", December 2011
12
Mugizi Robert Rwebangira Visualizing the Legislature
Talk at UMD Summer Social Webshop - August 26,2011
11
R.Davis, R. Doku, W. Samotshozo, M. Rwebangira, C. Liu, L. Burge De Novo Peptide Sequencing from Mass Spectrometry Data
ADMI 2011, April 16,2011 Greenville,SC
10
Mugizi Robert Rwebangira, “Visualizing the Legislature” (ppt
An invited grad seminar talk at Virginia Tech Northern Campus - October 29,2010
9
Mugizi Robert Rwebangira, “Visualizing the Legislature” (ppt)
Talk at the National Technical Association Conference - September 15,2010
8
Mugizi Robert Rwebangira, “Techniques for Exploiting Unlabeled Data”, Thesis Defense (ppt)
I defended my thesis on September 8, 2008.
7
Mugizi Robert Rwebangira, “Techniques for Exploiting Unlabeled Data”, Thesis proposal (ppt)
I proposed my thesis on May 11, 2007.
6
Avrim Blum, T.-H. Hubert Chan, Mugizi Robert Rwebangira, “A Random-Surfer Web-Graph Model”, ANALCO '06. (ppt)
I gave this presentation on March 27, 2006 at the Theory lunch at CMU. It is essentially an expanded version of the ANALCO ‘06 talk.
5
This was the presentation I gave at the ANALCO ’06 workshop on January 21, 2006 at the Radisson Hotel in Miami.
4
A. Blum, J. Lafferty, M.R. Rwebangira, R. Reddy “Semi-supervised Learning Using Randomized Mincuts”, International Conference on Machine Learning 2004 (ppt)
I presented this work in July 2004 at ICML 04 in Banff, Canada.
3
A. Blum, M.R. Rwebangira, R. Reddy, J. Lafferty “Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples” 2003 (ppt)
I presented my ICML poster (with a few changes) to the Machine Learning lunch at CMU(Pittsburgh!) in September 2003.
2
A. Blum, M.R. Rwebangira, R. Reddy, J. Lafferty “Improving the Graph Mincut Approach to Learning from Labeled and Unlabeled Examples”, International Conference on Machine Learning 2003 (Non refereed Poster) (ppt)
This was a poster I presented about work in progress at ICML in August 2003 in Washington, D.C.
1
L. Burge, M.R. Rwebangira. Constructing Reliable Software across the ORB. Symposium on Computing at Minority Institutions. ADMI 2000. (ppt)
I presented this in Virginia Beach, Virginia in June 2000.
Publications:-
17
Mugizi RwebangiraOn Ranking Senators by Their Votes, Lecture Notes in Electrical Engineering 2012, 1, Volume 124, Recent Advances in Computer Science and Information Engineering, Pages 261-268
16
Hui Li, Lauren Scott, Chunmei Liu, Mugizi Rwebangira and Legand Burge Rapid and Accurate Generation of Peptide Sequence Tags with a Graph Search Approach Lecture Notes in Computer Science, 2011, Volume 6674, Bioinformatics Research and Applications, Pages 253-261
15
Ko KD, Liu C, Rwebangira MR, Burge L, Southerland W. The Development of a Proteomic Analyzing Pipeline for Identifying Proteins with Multiple RRMs and Predicting their Domain Boundaries. Workshop on Computational Structural Bioinformatics, BIBM 2011, 374-381.
14
Li H, Liu C, Rwebangira MR, Burge L, Southerland W. Rapid Identification of multiple Post-translational Modifications with Peptide Sequence Tags. Workshop on Integrative Data Analysis in Systems Biology, BIBM 2011, 251-254.
Wardell Samotshozo, Mugizi Robert Rwebangira, Chunmei Liu, Legand Burge, Rhonda Davis, Ronald Doku, William Southerland. Pairing Algorithm for De Novo Sequencing of Tandem Mass Spectra. National Technical Association Conference, Howard University, 2011
A simple model for peptide sequencing.
H. Sueing, J. Jackson, R. Iziduh, A.N. Washington, M.R. Rwebangira, L. Burge. “The Opportunistic Routing of the Washington Metropolitan Area Bus System as a Wireless Vehicular Node Simulated Network.” WORLDCOMP 10 (pdf)
A realistic simulation of an ad-hoc vehicular network.
M.R. Rwebangira. “On Ranking Senators.” arXiv:0909.1418. Technical Report. 2009 (pdf)
Using the graph laplacian to rank legislators.
Mugizi Robert Rwebangira, “Learning by Combining Native Features with Similarity Functions”, Extended Abstract, WEHYS 08 (pdf)
Extended abstract that was submitted to the WEHYS workshop at NIPS 2008.
Mugizi Robert Rwebangira, Avrim Blum “Learning by Combining Native Features with Similarity Functions”, Technical Report (pdf)
This was the other major unpublished part of my thesis work.
Mugizi Robert Rwebangira, John Lafferty “Local Linear Semi-supervised Regression”, Technical Report (pdf)
This was one large part of my thesis work.
Mugizi Robert Rwebangira, “Techniques for Exploiting Unlabeled Data”, Doctoral Thesis (pdf)
My PhD thesis which I defended in September 2008 and finally delivered in November 2008.
Mugizi Robert Rwebangira, “Techniques for Exploiting Unlabeled Data”, Thesis proposal (pdf)
My thesis proposal document, proposed in May 2007.
Avrim Blum, T.-H. Hubert Chan, Mugizi Robert Rwebangira, “A Random-Surfer Web-Graph Model”, ANALCO '06. (pdf) (ppt1) (ppt2)
This work was done in Fall 2005. We studied a certain natural model for producing a random graph and did some experimental and theoretical analysis. I later presented this at ANALCO in Miami and at the Theory lunch at CMU.
Maria-Florina Balcan, Avrim Blum, Pakyan Choi, John Lafferty, Brian Pantano, Mugizi Robert Rwebangira, Xiaojin Zhu “Person identification in webcam images: An application of semi-supervised learning”, ICML 2005 Workshop on Learning with Partially Classified Training Data (pdf)
This work was done during 2004 and 2005 by a large group of collaborators. It was essentially a large scale application of semi-supervised learning to a “realistic” task. Jerry Zhu was the lead and presented it at a workshop in ICML 2005.
A. Blum, J. Lafferty, M.R. Rwebangira, R. Reddy “Semi-supervised Learning Using Randomized Mincuts”, International Conference on Machine Learning 2004 (ps) (pdf)
This is an extension of the graph mincut approach to learning with labeled and unlabeled data originally proposed by Blum and Chawla. We added randomness to the graph structure and took the average of several mincuts. We also proposed a general method for constructing the graph that seems to have robust performance.
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 on Middleware for Mobile Computing with IFIP/ACM middleware conference 2001 (pdf)
This work was done in the summer of 2001 at IBM research and published in fall of the same year. The title pretty much says it all; it’s all about pervasive computing.
L. Burge, M.R. Rwebangira. Constructing Reliable Software across the ORB. Symposium on Computing at Minority Institutions. ADMI 2000. (pdf)(ppt)
An approach to fault tolerance in distributed systems.