Rodrigo Alves

PhD student


Rodrigo Alves is a PhD candidate at the Machine Learning Group (since 2018) under the supervision of Marius Kloft at TU Kaiserslautern, Germany. He is also a Lecturer (since 2014, currently in PhD leave) at CEFET-MG, Brazil. He holds bachelor's (Information Systems - Best student award) and a master's (Computer Science) degrees from the Department of Computer Science at the Federal University of Minas Gerais. He is also vocational educational Teacher certificated by the Häme University of Applied Sciences, Finland. During his career, he has been collaborating in various research groups and management teams.

Research interests

Rodrigo Alves is interested in machine learning and its applications, especially related do data mining. Currently, his research is focused on recommender systems (theory and application). He is also interested in student-centred learning methodology, particularly in relation to how to improve machine-learning teaching.

Appointments and scientific matters
TUK, Building 36, Room 309 - 67653 Kaiserslautern

Curriculum Vitae


Vocational Educational Training, Häme University of Applied Sciences, Finland
Master degree, Federal University of Minas Gerais, Brazil
Bachelor degree in Information Systems, Federal University of Minas Gerais, Brazil

Professional Experience

since 2018
PhD candidate - Researcher, TU Kaiserslautern, Kaiserslautern, Germany
Lecturer (teacher of technical and higher education), CEFET-MG, Belo horizonte, MG, Brazil
Data Processing Coordinator, CEFET-MG, Belo horizonte, MG, Brazil
Technician, CEFET-MG, Belo horizonte, MG, Brazil

Key publications

  • R. Alves*, A. Ledent*, R. Assunção, and M. Kloft. An Empirical Study of the Discreteness Prior in Low-Rank Matrix Completion. Proceedings of Machine Learning Research (PMLR): NeurIPS 2020 Workshop on the Pre-registration Experiment: An Alternative Publication Model For Machine Learning Research, (to appear) 2020.
  • Antoine Ledent*, Rodrigo Alves*, and Marius Kloft. Orthogonal Inductive Matrix Completion. Preprint, 2020
  • F. Jirasek*, R. Alves*, J. Damay, R. Vandermeulen, R. Bamler, M. Bortz, S. Mandt, M. Kloft and H. Hasse. Machine Learning in Thermodynamics: Prediction of Activity Coefficients by Matrix Completion. The Journal of Physical Chemistry Letters, 2020.
  • Rodrigo Augusto da Silva Alves, Renato Martins Assuncao, and Pedro Olmo Stancioli Vaz de Melo. Burstiness Scale: A Parsimonious Model for Characterizing Random Series of Events. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD ’16). ACM, New York, NY, USA, 1405-1414, 2016.
(* denotes equal contribution)