Diego Marcondes
profile
MSI-Google Fellow
email diego.marcondes@anu.edu.au
call +61 2 6125 1007

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About

I earned a BS in Statistics and a PhD in Applied Mathematics from the University of São Paulo, Brazil. Previously, I was a postdoc at the Computer Science Department, Institute of Mathematics and Statistics, University of São Paulo (2022-2024), and a visiting postdoctoral scholar at the Department of Electrical and Computer Engineering, Texas A&M University (2023-2024). 

My research interests in data science are at the intersection of probability theory, statistics, applied mathematics and computer science. In particular, I am interested in developing learning methods with a strong mathematical basis which do not only have a high performance, but are controllable and interpretable, with applications to scientific problems, and signal and image processing. I am currently working on operator learning based on mathematical morphology and physics-informed neural networks for solving forward and inverse problems. I also have interests in epidemiological modelling and the metastability of Markov processes.

Affiliations

Research interests

My research interests are mainly:

  • Representation, design, and machine learning of lattice operators by mathematical morphology
  • Numerically solving forward and inverse problems associated to partial differential equations with scientific machine learning methods
  • Statistical learning
  • Operator learning by neural networks
  • Epidemiological modelling considering human mobility and population dynamics with calibration methods based on machine learning methods
  • Metastability of Markov processes, in particular interacting particle systems

Projects

Teaching information

  • 2025/1: Special Topics in Mathematics - Scientific Machine Learning

Location

Room 1.48, Hanna Neumann Building 145

Publications

Peixoto, C.; Marcondes, D.; Melo, M. P.; Maia, A. C.; Correia, L. A. Prediction of healthcare costs on consumer direct health plan in the Brazilian context. To appear in Revista Brasileira de Economia. 2025. 

Marcondes, D.; Simonis, A.; Barrera, J. Back to basics to open the black box. Nature Machine Intelligence. 2024

Marcondes, D.; Barrera, J. Discrete Morphological Neural Networks. SIAM Journal on Image Sciences. 2024

Marcondes, D., Barrera, J. The Lattice Overparametrization Paradigm for the Machine Learning of Lattice Operators. In: Brunetti, S., Frosini, A., Rinaldi, S. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2024. Lecture Notes in Computer Science, vol 14605. Springer, Cham, 2024

Marcondes, D., Feldman, M., Barrera, J. An Algorithm to Train Unrestricted Sequential Discrete Morphological Neural Networks. In: Brunetti, S., Frosini, A., Rinaldi, S. (eds) Discrete Geometry and Mathematical Morphology. DGMM 2024. Lecture Notes in Computer Science, vol 14605. Springer, Cham, 2024

Landim, C.; Marcondes, D.; Seo, I. A resolvent approach to metastability. Journal of the European Mathematical Society, pp.1-56. 2023

Landim, C.; Marcondes, D.; Seo, I. Metastable behavior of weakly mixing Markov chains: The case of reversible, critical zero-range processes. Annals of Probability, 51 (1) 157 - 227. 2023

Ferreira, C. P.; Marcondes, D.; Melo, M. P.; Oliva, S. M.; Peixoto, C.; Peixoto, P. S. A snapshot of a pandemic: the interplay between social isolation and COVID-19 dynamics in Brazil. Patterns, 2(10).

Peixoto P. S.; Marcondes, D.; Peixoto, C.; Oliva S. M. Modeling future spread of infections via mobile geolocation data and population dynamics. An application to COVID-19 in Brazil. PLoS ONE 15(7): e0235732. 2020

Marcondes, D.; Peixoto, C.; Stern, J. M. Assessing randomness in case assignment: the case study of the Brazilian Supreme Court, Law, Probability and Risk, Volume 18, Issue 2-3, Pages 97–114. 2019

Marcondes, D.; Peixoto, C.; Maia, A. C. A Survey of a Hurdle Model for Heavy-Tailed Data Based on the Generalized Lambda Distribution. Communications in Statistics - Theory and Methods 49, no. 4, 781–808. 2020

Marcondes, D.; Simonis, A.; Barrera, J. Feature Selection based on the Local Lift Dependence Scale. Entropy, 20, 97. 2018

Marcondes, D.; Marcondes, N.R. A Nonparametric Statistical Approach to Content Analysis of Items. Stats, 1, 1-13. 2018

Marcondes, D.; Simonis A.; Barrera, J.. Feature selection from local lift dependence‑based partitions. In Bayesian Inference and Maximum Entropy Methods in Science and Engineering: MaxEnt 37, Jarinu, Brazil, July 09–14, 2017 37, pages 43– 53. Springer, 2018. 

Marcondes, D.; Peixoto, C.; Souza, K.; Wechsler, S. Entrance Fees and a Bayesian Approach to the St. Petersburg Paradox. Philosophies, 2, 11, 2017