Leandro F. Maia
(Incoming) Assistant Professor | Oregon State University
(Incoming) Assistant Professor I work on theoretical and computational analysis of algorithms for large-scale optimization problems, with a particular focus on nonsmooth and nonconvex problems. These challenges frequently arise in applications across machine learning, artificial intelligence, and data science. I focus on inexact block decomposable and trust-region methods. |
Short Bio
I am an incoming Assistant Professor in the School of Mechanical, Industrial, and Manufacturing Engineering at Oregon State University. I received my Ph.D. in Industrial and Systems Engineering from Texas A&M University, where I was advised by Dr. David Gutman. Over the past two years, I had the privilege of working closely with Renato Monteiro from Georgia Tech. I also spent a summer at Sandia National Laboratories collaborating with Drew Kouri and Robert (Bobby) Baraldi. Prior to my doctoral studies, I earned a Master’s degree in Mathematics from the Federal University of Pará (UFPA) and a Bachelor’s degree in Computer Engineering from the Military Institute of Engineering (IME) in Brazil.
Research Interest
Lately, I’ve been particularly interested in the development and analysis of algorithms for large-scale optimization. My current focus includes:
Augmented Lagrangian methods.
Alternating Direction Method of Multipliers (ADMM) and its variants for large-scale and distributed optimization.
Trust Region methods, especially in the context of nonsmooth and inexact proximals.
Randomized Block Coordinate Descent (RBCD) techniques for decomposable problems.