Anomaly detection using NFs
I study how to detect and infer anomalous data in the context of likelihood estimation.
Hi,
I'm a M.Sc student at the Department of Computer Science, UFMG.
As a deep learning and data science researcher, my studies encompass a broad range of areas, from ML theory to AI in practice.
I focus on creating solutions to address real-world complex problems through probabilistic machine learning.
Funded by Petrobras, I am a member of GoDeep (Geoscience Oriented Deep Learning), a research group studying deep learning approaches to solve geoscience problems. Moreover, my research extends to generative models across a wide spectrum of contexts, ranging from stochastic forecasting to anomaly detection.
Before moving to Computer Science, as a Physics major funded by CNPq, I worked as an observational astronomer. Using Gaia DR3 data, I applied unsupervised learning algorithms to characterize stellar clusters.
When I'm not doing research, I enjoy playing board games with my friends, watching F1, reading, and playing tennis.
Experience
AI and Statistics Consultant: 2023 - Present
Providing tailored solutions in artificial intelligence and statistical modeling to solve complex business.Designed and deployed models, dashboard and pipeline for multiple small and medium companies.
Data profiling and statistical analysis in projects under Fiocruz, FUNAI and Ministério da Saúde.
Graduate Researcher in Computer Science, 2023 - Present
Development of deep learning models at PetrobrasGenerative modelling, covariate shift and performance degradation through probabilistic machine learning.
Undergraduate Researcher in Astrophysics, 2022-2023
Developed a methodology to quantify uncertainty in identifying stellar populations through unsupervised clustering algorithms.Studied Milky-Way substructures through classical machine learning using Gaia space telescope.
Summer Internship at JPL (NASA), 2021-2021
Fundamental modelling of orbital dynamics in LEO and profiling sensor systems.Research Assistant in Astrophysics, 2020-2022
Developed an automatic isochrone fitting algorithm with Gaia space telescope data.Research
Current research
Seismic fault detection
As part of my Petrobras research, I study and develop automatic seismic interpretation tools.
Past research
Automatic isochrone fitting
I studied how to automatically fit isochrones to a star cluster in a colour-magnitude diagram.
Milky Way substructures
I studied the relationship between open star clusters with MW's substructures, such as stellar streams.
Clustering algorithms applied to stellar populations
I applied unsupervised clustering algorithms to identify and charcterize open star clusters.
Education
2023-Present
M.Sc Computer Science, UFMGResearch on Deep Learning and Data Science.
2019-2022
B.Sc Physics, UFMGConcentration in Astrophysics and Computational Physics
Publications
A systematic review of deep learning for structural geological interpretation
Gustavo Lúcius Fernandes, Flavio Figueiredo, ... , João Pedro Pires
Data Mining and Knowledge Discovery, 2025
DOI / Link
Gustavo Lúcius Fernandes, Flavio Figueiredo, ... , João Pedro Pires
Data Mining and Knowledge Discovery, 2025
DOI / Link
Preliminary study of open clusters associated with Milky Way substructures through automatic isochrone fitting
João Pedro Pires, João Francisco Coelho dos Santos Jr.
XLV Annual Meeting of the Brazilian Astronomical Society, 2022
Contact me
If you have any questions feel free to ask!