Academic Presentations

Comprehensive Data Science & Machine Learning Course Materials

Diogo Ribeiro — Faculty of Media Arts and Design, Technical University of Porto

A collection of professional academic presentations covering advanced topics in statistics, machine learning, deep learning, and data science — built for graduate courses, research seminars, professional training, and self-study.

📊 Browse slide previews · Course catalog · Getting started · Contributing · Changelog

Sections below are collapsible. Click any ▸ heading to expand just the part you need.

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🧭 Contents

Section	What's inside
📚 Course catalog	All modules, learning objectives, topics, prerequisites
📁 Repository structure	Directory layout and conventions
🚀 Getting started	Install LaTeX/Python/R, compile slides, run exercises
🎨 Theme & styling	ESMAD Beamer theme and usage template
🎯 Pick your path	Guides for students, educators, and researchers
🤖 Automation & contributing	CI/CD workflows and how to contribute
📄 License, citation & contact	Licensing, BibTeX, and how to reach out

At a glance

📚 Presentations	15+ comprehensive decks, 100+ hours of content
💻 Code	27,000+ lines of production-ready Python & R
📖 References	140+ curated papers with DOIs
🎨 Theme	One professional LaTeX theme, fully documented
📝 Assessments	Exercises, quizzes, exams, and grading rubrics
🤖 Build	Automated PDF compilation via GitHub Actions

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📚 Course catalog

Every module lives in its own directory with a presentation/, and most also ship code/ and exercises/. Expand a domain below for learning objectives and topic lists.

Module	Domain	Level	Duration
R Programming	Programming	Beginner	2–3 weeks
Statistical Learning Theory	ML theory	Intermediate	4–5 weeks
Feature Engineering	ML practice	Beginner–Intermediate	2–3 weeks
Principal Component Analysis	Foundations	Intermediate	1–2 weeks
Optimization for Data Science	Optimization	Intermediate–Advanced	3–4 weeks
Deep Learning Fundamentals	Deep learning	Intermediate–Advanced	3–4 weeks
Reinforcement Learning	Deep learning	Advanced	4–5 weeks
Advanced MCMC Methods	Bayesian	Advanced	3–4 weeks
Bayesian Machine Learning	Bayesian	Advanced	3–4 weeks
Causal Inference	Causal	Advanced	4–5 weeks
A/B Testing & Experimentation	Applied	Intermediate	1–2 weeks
Time Series Analysis	Forecasting	Intermediate–Advanced	3–4 weeks
Explainable AI & Interpretability	Advanced topics	Intermediate–Advanced	2–3 weeks
Building AI Agents	Advanced topics	Advanced	90-minute deck
OOP & Streaming Pipelines	Computer science	Intermediate	2 weeks
Capstone Projects	Projects	Advanced	Course-length
Data Science Applications	Applied course	Intermediate	Full course
Testing Suites Guide	Engineering	Intermediate	1 week

🔷 Deep Learning & Neural Networks — deep learning fundamentals, reinforcement learning

Deep Learning Fundamentals

📂 02-deep-learning/deep-learning-fundamentals/

Learning Objectives:

• Understand the mathematical foundations of neural networks
• Implement backpropagation and gradient descent from scratch
• Master modern optimization techniques (SGD, Adam, AdamW)
• Design and train CNN architectures for computer vision
• Build RNN/LSTM models for sequential data
• Understand Transformer architecture and attention mechanisms
• Apply regularization techniques (dropout, batch normalization)

Topics Covered:

• Perceptron and multilayer networks
• Activation functions (ReLU, sigmoid, tanh, Swish)
• Loss functions and optimization
• Convolutional Neural Networks (LeNet, AlexNet, VGG, ResNet)
• Recurrent Neural Networks and LSTM
• Transformers and self-attention
• Training best practices

Prerequisites: Linear algebra, calculus, Python programming
Level: Intermediate to Advanced
Duration: 3-4 weeks (graduate course)

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Reinforcement Learning

📂 02-deep-learning/reinforcement-learning/

Learning Objectives:

• Formulate problems as Markov Decision Processes
• Derive and apply Bellman equations
• Implement value iteration and policy iteration
• Understand Monte Carlo and TD learning methods
• Build Q-learning and SARSA agents
• Apply function approximation with neural networks
• Implement modern deep RL algorithms (DQN, PPO, A3C)
• Design multi-agent systems

Topics Covered:

• Markov Decision Processes and dynamic programming
• Monte Carlo methods
• Temporal Difference learning (SARSA, Q-learning)
• Function approximation and deep Q-networks
• Policy gradient methods (REINFORCE, Actor-Critic, PPO)
• Multi-agent reinforcement learning
• Applications (games, robotics, resource allocation)

Prerequisites: Probability, linear algebra, Python
Level: Advanced
Duration: 4-5 weeks (graduate course)

🔷 Machine Learning Theory & Practice — statistical learning, feature engineering, explainable AI

Statistical Learning Theory

📂 01-foundations/statistical-modeling/

Learning Objectives:

• Understand bias-variance tradeoff
• Master regularization techniques (Ridge, Lasso, Elastic Net)
• Apply cross-validation and model selection
• Implement ensemble methods (bagging, boosting, stacking)
• Understand kernel methods and SVMs
• Perform dimensionality reduction (PCA, t-SNE, UMAP)
• Evaluate models using appropriate metrics

Topics Covered:

• Supervised learning fundamentals
• Linear and logistic regression
• Regularization and model selection
• Tree-based methods (CART, Random Forests, XGBoost)
• Support Vector Machines
• Gaussian Processes
• Model evaluation and validation

Prerequisites: Statistics, linear algebra, programming
Level: Intermediate
Duration: 4-5 weeks

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Feature Engineering

📂 01-foundations/feature-engineering/

Learning Objectives:

• Design effective feature engineering pipelines
• Handle missing data with advanced imputation techniques
• Encode categorical variables appropriately
• Create polynomial and interaction features
• Apply feature scaling and normalization
• Perform feature selection using multiple methods
• Build end-to-end ML pipelines with scikit-learn

Topics Covered:

• Missing value imputation (mean, median, KNN, MICE)
• Categorical encoding (one-hot, ordinal, target, entity embeddings)
• Feature scaling (standard, min-max, robust)
• Polynomial features and interactions
• Feature selection (filter, wrapper, embedded methods)
• Dimensionality reduction
• Pipeline construction

Prerequisites: Basic Python, pandas, scikit-learn
Level: Beginner to Intermediate
Duration: 2-3 weeks

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Explainable AI & Model Interpretability

📂 06-advanced-topics/explainable-ai/

Learning Objectives:

• Understand the interpretability-accuracy tradeoff
• Explain model predictions using SHAP values
• Apply LIME for local explanations
• Compute and interpret permutation importance
• Visualize partial dependence and ICE plots
• Detect and mitigate algorithmic bias
• Implement fairness metrics and constraints
• Use modern XAI tools (SHAP, LIME, InterpretML)

Topics Covered:

• Global vs local explanations
• Model-agnostic methods (SHAP, LIME, permutation importance)
• Model-specific interpretability (linear models, trees, neural networks)
• Attention mechanisms and gradient-based explanations
• Algorithmic fairness and bias detection
• Fairness definitions and impossibility results
• Practical implementation with Python tools

Prerequisites: Machine learning basics, Python
Level: Intermediate to Advanced
Duration: 2-3 weeks

🔷 Bayesian Methods & MCMC — advanced MCMC, Bayesian machine learning

Advanced MCMC Methods

📂 03-bayesian-methods/mcmc/

Learning Objectives:

• Understand Bayesian inference and posterior distributions
• Derive Metropolis-Hastings acceptance probability
• Implement MCMC algorithms from scratch
• Apply Hamiltonian Monte Carlo for efficient sampling
• Use No-U-Turn Sampler (NUTS) for automatic tuning
• Diagnose convergence using R-hat and ESS
• Apply MCMC to real Bayesian models

Topics Covered:

• Bayesian inference fundamentals
• Metropolis-Hastings algorithm
• Hamiltonian Monte Carlo and leapfrog integration
• No-U-Turn Sampler (NUTS)
• Convergence diagnostics (trace plots, R-hat, ESS)
• Applications (Bayesian regression, hierarchical models)

Prerequisites: Probability theory, calculus, Python
Level: Advanced
Duration: 3-4 weeks
Code: Complete Python implementations (8,000+ lines)

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Bayesian Machine Learning

📂 03-bayesian-methods/bayesian-machine-learning/

Learning Objectives:

• Apply Bayesian inference to machine learning problems
• Build Bayesian linear and logistic regression models
• Implement Gaussian Processes for regression
• Understand Bayesian neural networks
• Perform approximate inference (VI, EP)
• Apply Bayesian optimization for hyperparameter tuning
• Quantify predictive uncertainty

Topics Covered:

• Bayesian linear regression
• Gaussian Processes
• Bayesian neural networks
• Variational inference
• Bayesian optimization
• Uncertainty quantification

Prerequisites: Bayesian statistics, machine learning, Python
Level: Advanced
Duration: 3-4 weeks

🔷 Causal Inference & Experimentation — causal inference, A/B testing

Causal Inference

📂 04-causal-inference/causal-inference-fundamentals/

Learning Objectives:

• Understand potential outcomes framework
• Draw and interpret causal DAGs
• Implement Instrumental Variables (IV/2SLS)
• Apply Regression Discontinuity Design
• Use Difference-in-Differences methods
• Estimate propensity scores and perform matching
• Apply synthetic control methods
• Identify and address confounding

Topics Covered:

• Potential outcomes and causal graphs
• Instrumental Variables and weak instruments
• Regression Discontinuity (sharp and fuzzy)
• Difference-in-Differences and event studies
• Propensity score methods
• Synthetic controls
• Modern methods (Callaway-Sant'Anna, Sun-Abraham)

Prerequisites: Statistics, econometrics, R or Python
Level: Advanced
Duration: 4-5 weeks
Code: Python & R implementations (11,000+ lines)

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A/B Testing & Experimentation

📂 04-causal-inference/ab-testing/

Learning Objectives:

• Design statistically rigorous A/B tests
• Calculate required sample sizes
• Perform hypothesis testing correctly
• Control for multiple comparisons
• Understand statistical power and effect sizes
• Apply sequential testing methods
• Analyze experimental results
• Avoid common pitfalls (peeking, p-hacking)

Topics Covered:

• Experimental design
• Hypothesis testing and p-values
• Sample size calculations
• Multiple testing corrections
• Bayesian A/B testing
• Sequential analysis
• Common pitfalls and best practices

Prerequisites: Statistics, probability
Level: Intermediate
Duration: 1-2 weeks

🔷 Time Series & Forecasting — classical and deep forecasting methods

Time Series Analysis

📂 05-time-series/time-series-forecasting/

Learning Objectives:

• Analyze time series components (trend, seasonality)
• Test for and achieve stationarity
• Build ARIMA and SARIMA models
• Implement VAR models for multivariate series
• Apply state space models and Kalman filter
• Use LSTM and Transformers for forecasting
• Evaluate forecasting accuracy
• Apply hybrid methods (Prophet, N-BEATS)

Topics Covered:

• Stationarity and unit root tests
• ARMA, ARIMA, SARIMA models
• Vector Autoregression (VAR)
• State space models and Kalman filter
• Forecasting and evaluation
• Deep learning for time series (LSTM, GRU)
• Transformer models (TFT, Autoformer, Informer)
• Hybrid approaches (ES-RNN, N-BEATS, Prophet)

Prerequisites: Statistics, linear algebra, Python
Level: Intermediate to Advanced
Duration: 3-4 weeks

🔷 Optimization & Computational Methods — convex optimization through evolutionary search

Optimization for Data Science

📂 01-foundations/optimization/

Learning Objectives:

• Formulate optimization problems
• Understand convexity and its implications
• Derive and apply KKT conditions
• Implement gradient descent variants
• Apply momentum and adaptive methods (Adam, AdamW)
• Solve constrained optimization problems
• Use evolutionary algorithms for black-box optimization
• Apply Bayesian optimization for hyperparameter tuning
• Optimize neural network training

Topics Covered:

• Convex optimization fundamentals
• Gradient descent (batch, SGD, mini-batch)
• Momentum methods and Nesterov acceleration
• Adaptive learning rates (AdaGrad, RMSProp, Adam)
• Constrained optimization (Lagrangian, KKT, penalties)
• Evolutionary algorithms (GA, ES, PSO, CMA-ES)
• Bayesian optimization
• Multi-objective optimization

Prerequisites: Calculus, linear algebra, Python
Level: Intermediate to Advanced
Duration: 3-4 weeks

🔷 Programming, Engineering & Applied Modules — R, PCA, OOP & streaming, AI agents, capstones

Module	Directory	Focus
R Programming	00-programming-fundamentals/r-programming/	R from basics to data science workflows
Principal Component Analysis	01-foundations/pca/	PCA theory, geometry, and applied dimensionality reduction
OOP & Streaming Pipelines	06-advanced-topics/computer-science/	Object-oriented design principles and streaming pipeline processing
Building AI Agents	06-advanced-topics/ai-agents/	Agent architecture, reliability, and production operations (90-minute deck)
Capstone Projects	07-capstone-projects/	Project guides, prerequisites appendix, and industry-focused briefs
Data Science Applications	08-data-science-applications-course/	Full applied course: "Data Science in Practice — Industry Applications"
Testing Suites Guide	09-unit-tests/	Writing and structuring test suites for data science code
MLOps & Deployment	06-advanced-topics/mlops-deployment/	Planned module — directory scaffolded, slides in progress

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📁 Repository structure

Full directory tree

academic-presentations/
├── README.md                            # This file
├── CONTRIBUTING.md                      # Contribution guidelines
├── CHANGELOG.md                         # Version history
├── ACCESSIBILITY.md                     # Accessibility guidance
├── QUALITY.md                           # Quality standards
├── compile_all.sh                       # Build every presentation
│
├── .github/                             # 🤖 GitHub Actions automation
│   ├── workflows/
│   │   ├── compile-latex.yml            # Auto-compile PDFs
│   │   ├── check-links.yml              # Verify all URLs
│   │   └── generate-previews.yml        # Create PDF previews
│   ├── dependabot.yml                   # Dependency updates
│   └── markdown-link-check-config.json
│
├── shared/                              # 🔄 Shared resources
│   ├── theme/                           # 🎨 Professional LaTeX theme
│   │   ├── esmad_beamer_theme.sty       # Custom Beamer theme
│   │   ├── esmad_beamer_theme_highcontrast.sty
│   │   ├── STYLE_GUIDE.md               # Theme documentation
│   │   └── template_presentation.tex    # Example template
│   ├── bibliographies/                  # 📚 Reference libraries (140+ papers)
│   │   ├── mcmc_references.bib
│   │   ├── causal_inference_references.bib
│   │   ├── statistical_learning_references.bib
│   │   ├── capstone_projects_references.bib
│   │   ├── industry_focus_references.bib
│   │   └── *_enhancements_references.bib
│   └── utilities/                       # Shared LaTeX/helper utilities
│
├── 00-programming-fundamentals/         # 💻 Programming basics
│   └── r-programming/                   # R: A Comprehensive Introduction
│
├── 01-foundations/                      # 📊 Core foundations
│   ├── statistical-modeling/            # Statistical Learning Theory
│   ├── feature-engineering/             # Feature Engineering
│   ├── pca/                             # Principal Component Analysis
│   └── optimization/                    # Optimization for Data Science
│
├── 02-deep-learning/                    # 🧠 Deep learning
│   ├── deep-learning-fundamentals/
│   └── reinforcement-learning/
│
├── 03-bayesian-methods/                 # 🎲 Bayesian statistics
│   ├── mcmc/                            # MCMC methods
│   └── bayesian-machine-learning/       # Bayesian ML
│
├── 04-causal-inference/                 # ⚖️ Causal methods
│   ├── causal-inference-fundamentals/
│   └── ab-testing/                      # A/B Testing & Experimentation
│
├── 05-time-series/                      # ⏱️ Time series
│   └── time-series-forecasting/
│
├── 06-advanced-topics/                  # 🔬 Advanced topics
│   ├── explainable-ai/                  # Explainable AI
│   ├── ai-agents/                       # Building AI Agents
│   ├── computer-science/                # OOP & streaming pipelines
│   └── mlops-deployment/                # Planned module
│
├── 07-capstone-projects/                # 🎓 Projects
│   ├── industry-focus/                  # Industry applications
│   ├── project-guides/                  # Project guidelines
│   └── prerequisites/                   # Prerequisites appendix
│
├── 08-data-science-applications-course/ # 🎯 Applied course
│   ├── presentation/                    # Full course materials
│   ├── exercises/
│   └── assessments/                     # Course assessments
│
├── 09-unit-tests/                       # 🧪 Testing suites guide
│
├── assessments/                         # 📝 Quizzes, exams, rubrics
├── datasets/                            # 📦 Example datasets
├── docs/                                # 📖 Guides and architecture notes
├── scripts/                             # 🔧 Maintenance scripts
└── tests/                               # ✅ Repository test suite

Per-module convention: each module directory contains presentation/ (Beamer slides), and where applicable code/ (Python/R implementations) and exercises/ (problem sets).

---

🚀 Getting started

1. Prerequisites — LaTeX, Python, R

LaTeX distribution:

# Ubuntu/Debian
sudo apt-get install texlive-full

# macOS
brew install --cask mactex

# Windows
# Download and install MiKTeX or TeX Live

Python environment (for code examples):

pip install -r requirements.txt

# Or install the core set directly:
pip install numpy scipy matplotlib seaborn pandas scikit-learn statsmodels
pip install torch tensorflow  # For deep learning examples
pip install shap lime         # For XAI examples

A conda environment is also provided in environment.yml.

R environment (for R examples):

install.packages(c(
  "AER", "rdrobust", "fixest", "did",  # Causal inference
  "caret", "recipes", "mice",           # Feature engineering
  "forecast", "vars", "fable"           # Time series
))

Or run the bundled installer: install_r_packages.R.

2. Compiling presentations — manual, latexmk, or CI

Manual compilation:

cd 02-deep-learning/deep-learning-fundamentals/presentation/
pdflatex deep_learning_beamer.tex
pdflatex deep_learning_beamer.tex  # Run twice for references

Using latexmk (recommended):

cd 02-deep-learning/reinforcement-learning/presentation/
latexmk -pdf rl_beamer.tex

Compile everything:

./compile_all.sh

Automated compilation:

• Push to GitHub → GitHub Actions automatically compiles all PDFs
• Download compiled PDFs from Actions artifacts or Releases

Build artifact policy:

• Compiled PDFs are tracked in git, so any deck can be read straight from GitHub without downloading a release or compiling it yourself.
• LaTeX auxiliary files (.aux, .log, .fls, .fdb_latexmk, .nav, .snm, .out) are generated noise and are ignored.
• CI additionally attaches freshly compiled PDFs to each release, so the release assets always reflect the latest source even if a tracked PDF is a commit behind.

3. Running code and exercises

Python:

# MCMC examples (if code/ directory exists with implementations)
# Example references are embedded in presentation materials

# Exercises and assessments
cd 03-bayesian-methods/mcmc/exercises/
pdflatex mcmc_exercises.tex

Exercises:

# MCMC exercises
cd 03-bayesian-methods/mcmc/exercises/
pdflatex mcmc_exercises.tex

# Causal inference exercises
cd 04-causal-inference/causal-inference-fundamentals/exercises/
pdflatex causal_inference_exercises.tex

---

🎨 Theme & styling

All presentations use the ESMAD Beamer Theme for a consistent, professional appearance.

Theme features and usage template

Features

✅ Professional color palette (ESMAD Blue, accents)
✅ Custom environments (theorems, definitions, examples, alerts)
✅ Mathematical notation helpers (\Normal, \E, \Var, etc.)
✅ Code listing styles with syntax highlighting
✅ Author information with ORCID integration
✅ Slide templates (title, TOC, contact, references)
✅ High-contrast variant for accessibility

Usage

\documentclass[aspectratio=169]{beamer}
\usepackage{../../../shared/theme/esmad_beamer_theme}

% Author info
\authorname{Your Name}
\authoremail{your.email@university.edu}
\authororcid{0000-0000-0000-0000}

\title{Your Presentation}
\date{\today}

\begin{document}
\begin{frame}
  \titlepage
\end{frame}

% Your content...

\contactslide
\end{document}

See shared/theme/STYLE_GUIDE.md for complete documentation, and ACCESSIBILITY.md for accessibility guidance.

---

🎯 Pick your path

📖 For students — learning paths and study tips

Recommended learning paths

Path 1: Machine Learning Fundamentals

1. Statistical Learning (4 weeks)
2. Feature Engineering (2 weeks)
3. Optimization (3 weeks)
4. Explainable AI (2 weeks)

Path 2: Deep Learning Specialization

1. Deep Learning Fundamentals (4 weeks)
2. Optimization (focus on neural networks)
3. Reinforcement Learning (4 weeks)
4. Time Series Analysis (focus on deep methods)

Path 3: Causal & Bayesian Methods

1. Causal Inference (5 weeks)
2. Bayesian ML (4 weeks)
3. MCMC Methods (3 weeks)
4. A/B Testing (2 weeks)

Competency matrices and additional paths live in docs/learning-paths/.

Study tips

• 📚 Start with slides to understand concepts
• 💻 Run code examples to see methods in action
• 📝 Complete exercises to test understanding
• 📖 Read references for deeper knowledge
• 🤝 Join discussions (create GitHub issues)

👨‍🏫 For educators — course integration, customization, assessments

Course integration

These materials can be integrated into:

• Graduate courses in Data Science/Statistics/CS
• Professional training programs
• Workshop series
• Seminar courses

Customization

1. Fork this repository
2. Customize presentations for your needs
3. Add your own examples and exercises
4. Maintain attribution (CC BY-SA 4.0)

Assessment resources

Use the materials in assessments/:

• Quizzes for each topic
• Midterm and final exams
• Grading rubrics
• Project ideas

Per-topic enhancement guides are in docs/enhancement-guides/.

🔬 For researchers — citation and bibliographies

Citation

If you use these materials in your research or teaching, please cite:

@misc{ribeiro2025academic,
  author = {Ribeiro, Diogo},
  title = {Academic Presentations: Comprehensive Data Science Course Materials},
  year = {2025},
  publisher = {GitHub},
  url = {https://github.com/diogoribeiro7/academic-presentations},
  note = {Faculty of Media Arts and Design, Technical University of Porto}
}

Using the bibliographies

All presentations reference comprehensive BibTeX files:

\usepackage[backend=bibtex]{biblatex}
\addbibresource{../../../shared/bibliographies/mcmc_references.bib}

% In document
\cite{metropolis1953}
\cite{hoffman2014}

% At end
\printbibliography

Available:

• shared/bibliographies/mcmc_references.bib: 30+ MCMC papers
• shared/bibliographies/causal_inference_references.bib: 50+ causal inference papers
• shared/bibliographies/statistical_learning_references.bib: 60+ ML/stats papers
• Plus capstone, industry-focus, and per-topic enhancement bibliographies

All include DOIs for easy access.

---

🤖 Automation & contributing

CI/CD workflows

• compile-latex.yml: Auto-compiles all LaTeX on push
• check-links.yml: Verifies all URLs and DOIs weekly
• generate-previews.yml: Creates PDF preview gallery
• dependabot.yml: Keeps dependencies updated

Pre-commit hooks (formatting, spell check, LaTeX lint) are configured in .pre-commit-config.yaml.

PDF preview gallery: https://diogoribeiro7.github.io/academic-presentations/

How to contribute

Contributions are welcome — see CONTRIBUTING.md for full guidelines.

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test compilation and code
5. Submit a pull request

Contribution types:

• 🐛 Fix errors in presentations
• 📚 Add new presentations
• 💡 Improve existing content
• 📖 Enhance documentation
• 🧪 Add code examples
• 📝 Create exercises
• 🎨 Improve theme/styling

Quality standards are documented in QUALITY.md.

---

📄 License, citation & contact

License — CC BY-SA 4.0 for content, MIT for code

Content (presentations & exercises)

Licensed under Creative Commons Attribution-ShareAlike 4.0 International

You are free to:

• ✅ Share — copy and redistribute
• ✅ Adapt — remix, transform, and build upon

Under the terms:

• 📝 Attribution required
• 🔄 ShareAlike for derivatives

Code

Code examples licensed under MIT License

Contact & collaboration

Professional inquiries

• Email: dfr@esmad.ipp.pt
• Institution: Faculty of Media Arts and Design, Technical University of Porto
• LinkedIn: diogo-ribeiro-9094604a
• ORCID: 0009-0001-2022-7072

Research interests

• Markov Chain Monte Carlo and Bayesian computation
• Machine learning and deep learning
• Causal inference and econometrics
• Financial risk modeling
• Time series analysis and forecasting

Collaboration opportunities

• 🎓 Guest lectures and workshops
• 🏢 Corporate training programs
• 🔬 Research collaborations
• 📝 Joint publications
• 🌐 Conference presentations

Acknowledgments

• Students and colleagues for valuable feedback
• Open source community for tools and inspiration
• Academic community for rigorous peer review

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Repository maintainer: Diogo Ribeiro · Status: ✅ Actively maintained · History: CHANGELOG.md · View releases