Research Fellow · Trinity College Dublin

Dr. Gabriel
Rodrigues Palma

I develop statistical machine learning methods — combining computer vision, statistical modelling and time series analysis — for problems in biology and medicine.

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Gabriel Rodrigues Palma
Trinity College Dublin, The University of Dublin, Dublin, Ireland
17
Peer-reviewed articles
36
Conference papers
18k+
Package downloads
4
H-Index

Get to know

About me

Gabriel Rodrigues Palma presenting from a lectern at a conference

  • Current role

    Research Fellow, Trinity College Dublin

  • Focus

    Statistical machine learning applied to electrophysiology

  • Doctorate

    PhD in Statistics, Maynooth University

  • Service

    Chair, Young Statisticians' Section of the Irish Statistical Association

I am a Research Fellow at Trinity College Dublin, based at the Trinity Biomedical Sciences Institute, where my research centres on statistical machine learning applied to electrophysiology. I work with EEG, event-related potentials and motor-evoked potentials to build models that characterise how neural signals change in amyotrophic lateral sclerosis (ALS) and other neurological conditions — from identifying network-based disease subtypes to testing whether electrophysiological markers replicate across independent clinical cohorts.

I completed my PhD in Statistics at Maynooth University, funded by Science Foundation Ireland's Centre for Research Training in Foundations of Data Science. My thesis, Statistical modelling and machine vision applied to automating animal monitoring systems, developed pattern-based prediction and time series embedding methods that are now used to forecast population outbreaks. Before that I read Biological Sciences at the University of São Paulo.

Alongside my fellowship I am a Visiting Researcher at the BT Ireland Innovation Centre, coordinate modules in data science and linear models at Trinity, and serve on the executive committees of the Irish Statistical Association and the British & Irish Region of the International Biometric Society.

More about me

What I work on

Research & expertise

Methods development at the interface of statistics and machine learning, applied to problems where the data are messy, longitudinal and biologically meaningful.

  • Statistical ML for electrophysiology

    Models for EEG, event-related potentials and motor-evoked potentials — network-based ALS subtyping, mismatch negativity signatures and cross-cohort replication of neural markers.

    • EEG
    • ERP
    • ALS
    • Hidden Markov models

  • Time series analysis & forecasting

    Pattern-based prediction and time series embedding for detecting and forecasting population outbreaks from noisy, irregularly sampled monitoring data.

    • Embedding
    • Forecasting
    • Outbreak detection

  • Bayesian & mixed modelling

    Hierarchical joint models, multi-state models for double transitions, and residual-based diagnostics for categorical and polytomous responses.

    • Bayesian
    • Multi-state models
    • Diagnostics

  • Machine vision

    Convolutional architectures for automating species identification, avian song classification and predation-interaction detection from images and audio.

    • CNNs
    • Classification
    • Feature engineering

  • Financial machine learning

    Empirical mode decomposition combined with Gaussian mixture models to forecast asset price movements and characterise trader decision-making.

    • EMD
    • Mixture models
    • Explainable AI

  • Ecological informatics

    Statistical and mathematical modelling for integrated pest management, biological control and biodiversity estimation.

    • IPM
    • Biological control
    • Diversity indices

Languages

  • R
  • Python
  • MATLAB
  • C++
  • SQL
  • Fortran

Modelling & ML

  • tidyverse
  • scikit-learn
  • TensorFlow
  • Keras
  • NumPy
  • Matplotlib

Web & delivery

  • Next.js
  • React
  • JavaScript
  • Django
  • Laravel
  • Git

How I teach

Teaching & lectures

Full modules, short courses and invited lectures across statistics, machine learning and data science — in Ireland, Brazil and France.

Overall, in my career, I have had the opportunity to teach at the 3rd and Diploma levels as a contract lecturer at Maynooth University, module coordinator at Trinity College Dublin and in online courses at PRStats. Also, I had the opportunity to tutor in multiple modules in Ireland and Brazil. In 2024, I was invited to lecture a short course on Applications of Deep Learning and Machine Vision at the 68th annual meeting of the Brazilian Region of the International Biometric Society, the biggest statistical conference in Brazil. Also, in 2025 I received funding to travel to Brazil and deliver a 3-day short course on Machine Learning Applied to Agronomy.

Design and delivery of full modules

  • 2025

    Advanced Linear Models 2Module Coordinator · Delivered

    Trinity College Dublin, Ireland

  • 2025

    Foundations of Data Science 2Module Coordinator · Delivered

    Trinity College Dublin, Ireland

  • 2025

    Tidyverse for EcologistsModule Coordinator · Designed and Delivered

    PRStats

  • 2025

    Machine Vision using PythonModule Coordinator · Designed and Delivered

    PRStats

  • 2025

    Machine Learning using PythonModule Coordinator · Designed and Delivered

    PRStats

  • 2022

    R for Statistics and Data ScienceContract Lecturer · Delivered

    Maynooth University, Ireland

Short courses and invited lectures

  • 2025

    Short course: Machine Learning Applied to Agronomy

    Federal University of Paraíba, Brazil · 14h

  • 2024

    Invited lecture: R for Data Science and Statistics

    Maynooth University, Maynooth, Ireland · module ST203 · 12 December 2024 · 1h

  • 2024

    Invited lecture: Introduction to Statistics

    Maynooth University, Maynooth, Ireland · module ST221 · 11 December 2024 · 1h

  • 2024

    Invited lecture: Introduction to Statistics

    Maynooth University, Maynooth, Ireland · module ST221 · 9 October 2024 · 1h

  • 2024

    Invited lecture: Introduction to Statistics

    Maynooth University, Maynooth, Ireland · module ST221 · 9 October 2024 · 1h

  • 2024

    Short course: Applications of Deep Learning and Machine Vision

    University of São Paulo, Piracicaba, Brazil · 4h

  • 2024

    Short course: Data visualisation

    Institut Pasteur, Paris, France · 25 April 2024 · 3h30m

  • 2024

    Short course: Linear modelling

    Institut Pasteur, Paris, France · 25 April 2024 · 3h30m

  • 2024

    Invited lecture: Topics in Data Analytics

    Maynooth University, Maynooth, Ireland · module DS406 · 27 February 2024 · 2h

  • 2023

    Short course: Introduction to Python Programming Language Applied to Data Science

    University of São Paulo, Brazil · 4 and 11 October 2023 · 4h

  • 2023

    Invited lecture: Topics in Data Analytics

    Maynooth University, Maynooth, Ireland · module DS406 · 27 February 2023 · 2h

  • 2023

    Invited lecture: Introduction to Statistics

    Maynooth University, Maynooth, Ireland · module ST221 · 24 October 2023 · 1h

Teaching assistance

  • 2022

    Topics in Data Analytics

    Maynooth University, Maynooth, Ireland · module DS406 · 27 February – 24 May · 10h

  • 2019

    Statistical Machine Learning for MSc and PhD students of the Statistics programme

    University of São Paulo, Piracicaba, Brazil · 5–27 August · 60h

  • 2018

    Introduction to R and R Markdown: theory and statistical applications

    University of São Paulo, Piracicaba, Brazil · August · 4h

Other teaching experience

  • 2024

    Voluntary Advanced Class teacher for mathematics olympiad students

    Maynooth University, Ireland · February

  • 2023

    Voluntary combinatorics teacher for mathematics olympiad students

    Maynooth University, Ireland · February

  • 2022

    Voluntary combinatorics teacher for mathematics olympiad students

    Maynooth University, Ireland · January

  • 2017

    Voluntary Biology teacher

    Ong Fênix (non-governmental organisation), Brazil · October

My recent work

Publications

17 peer-reviewed journal articles, 36 conference papers, a book chapter and open-source software with more than 18,000 downloads.

What people say

Testimonials

Gabriel is a dedicated and talented individual who worked with us for four years in research projects. He always stood out in all the international scientific initiation symposiums that he participated in, under our guidance, demonstrating exceptional skills and knowledge in his field of expertise. His contribution to our team was invaluable, and we are confident that he will be successful in his scientific career.
Prof. Silvio Sandoval ZocchiAssociate Professor · University of São Paulo
Gabriel took over a complex problem statement of analysing 5G datasets and generating synthetic data by maintaining the integrity and correlation of several features in the dataset. He provided timely updates on the research findings and presented unique ideas to solve a problem. His contribution to the project will help us simulate more data that we can publish and utilize for AI-based research.
Dr. Shubham JainSenior Researcher · Ericsson
Gabriel’s research contribution is of high commercial relevance. His research on this applied industrial use case and real industrial problems and data will be beneficial for his career as it allows him to show thought leadership and gather real life expert knowledge in future telecoms networks (5G and beyond).
Dr. Ashima ChawlaApplied Research Engineer · Ericsson
Gabriel is a young researcher with excellent potential for advanced research in data science and computational modelling. In recent years he brilliantly developed projects emphasizing different problems focused on the interface between data and theory, proposing and testing statistical, mathematical, and computational models applied to different problems centered on biodiversity, trophic interactions, and entomology. Gabriel is also enthusiastic and exceptionally well-organized, and always willing to help co-workers.
Prof. Wesley Augusto Conde GodoyAssociate Professor · University of São Paulo
Gabriel is a very driven and knowledgeable researcher. He is an expert in Python, R, and machine learning. It is very easy to work and to communicate with him.
Dr. Rafael de Andrade MoralAssociate Professor · Maynooth University
Gabriel is an extremely organised, dedicated and competent professional.
Dr. Marcoandre SavarisAssociate Professor · University of São Paulo
I have had the pleasure of working with Gabriel for over 2 years. During this period, he presented himself as an excellent professional both because of his hard skills and his soft skills, which I consider his strong point. Gabriel has the behaviour and the hunger that are essential for any team. He has a great critical sense and a strong leadership spirit.
Rafael SouzaProject Manager · Hype Influency

Get in touch

Contact me

Open to collaborations in statistical machine learning, electrophysiology and applied forecasting — as well as teaching and speaking invitations.