Marco Alvarez

  • Associate Professor
  • Computer Science
  • Phone: 401.874.5009
  • Email:
  • Office Location: Tyler 255
  • Website


I joined URI in 2015 after working in the Department of Computer and Information Sciences, University of Delaware as a visiting scholar. I graduated with a Ph.D. in CS from the Computer Science Department, Utah State University, a M.S. in CS from the Instituto de Ciências Matemáticas e de Computação, Universidade de São Paulo, and a B.S. in CS from Faculdade de Computação, Universidade Federal de Mato Groso do Sul.

Note to prospective students: I am always looking for self-motivated students who are interested in pursuing Master’s or Ph.D. research. If you are interested in joining our graduate program, please look carefully at Graduate Admissions for further information. Teaching Assistantships may be available for strong candidates. While I welcome inquiries by e-mail, please be advised that I am generally unable to reply to all inquiries.


My research interests are in the broad area of Machine Learning with a focus on deep learning and machine learning techniques for structured data (e.g. graph kernels). My work has been applied to challenging problems arising in a variety of domains, such as computer vision, computer program analysis, and computational biology.

Selected Publications

Conference Publications

  • Rondeau, J. and M. Alvarez (2018). Deep Modeling of Human Age Guesses for Apparent Age Estimation. In: International Joint Conference on Neural Networks (IJCNN). Rio de Janeiro, Brazil.
  • Xu, L., D. Zhang, M. Alvarez, J. Morales, X. Ma, and J. Cavazos (2016). Dynamic Android Malware Classification Using Graph-Based Representations. In: International Conference on Cyber Security and Cloud Computing (CSCloud). Beijing, China, pp. 220–231.
  • Xu, L., W. Wang, M. Alvarez, J. Cavazos, and D. Zhang (2014). Parallelization of Shortest Path Graph Kernels on Multi-Core CPUs and GPUs. In: International Workshop on Programmability Issues for Heterogeneous Multicores (MULTIPROG). Best Paper Award. Vienna, Austria.
  • Pistori, H., M. Pereira, M. Alvarez, and X. Qi (2013). Open Source Tools and Project-Based Teaching as Enablers of Research Experience in Computer Vision Students. In: Congresso Brasileiro de Educacao em Engenharia (COBENGE). Gramado, RS, Brasil.
  • Park, E., J. Cavazos, and M. Alvarez (2012). Using Graph-Based Program Characterization for Predictive Modeling. In: International Symposium on Code Generation and Optimization (CGO). San Jose, CA, USA, pp. 196–206.
  • Alvarez, M. and C. Yan (2010). Exploring Structural Modeling of Proteins for Kernel-Based Enzyme Discrimination. In: Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). Montreal, Canada, pp. 1–5.


  • Alvarez, M. and C. Yan (2012). A New Protein Graph Model for Function Prediction. Computational Biology and Chemistry 37, 6–10.
  • Alvarez, M., X. Qi, and C. Yan (2011b). A Shortest-Path Graph Kernel for Estimating Gene Product Semantic Similarity. Journal of Biomedical Semantics 2(1), 3.
  • Alvarez, M. and C. Yan (2011). A Graph-Based Semantic Similarity Measure for the Gene Ontology. Journal of Bioinformatics and Computational Biology.
  • Shelton, B., J. Scoresby, T. Stowell, M. Capell, M. Alvarez, and C. Coats (2010). A Frankenstein Approach to Open Source: The Construction of a 3D Game Engine as Meaningful Educational Process. IEEE Transactions on Learning Technologies 3 (2), 85–90.

Book Chapters

  • Alvarez, M., X. Qi, and C. Yan (2011a). “GO-Based Term Semantic Similarity”. In: Ontology Learning and Knowledge Discovery Using the Web: Challenges and Recent Advances. IGI Publishing. Chap. IX.
  • Alvarez, M. and S. Lim (2008). “A Machine Learning Approach for One-Stop Learning”. In: Data Mining and Knowledge Discovery Technologies. IGI Publishing. Chap. XIV.

Course Information

Neural Networks and Deep Learning
Data Structures and Abstractions
Machine Learning
Parallel Computing
Artificial Intelligence
Competitive Programming
Object Oriented Programming