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pereirarodrigo/README.md

πŸ‘‹ Hey, I’m Rodrigo


I’m Rodrigo Pereira Cruz, a cognitive scientist and AI researcher from πŸ‡§πŸ‡· Brazil, currently pursuing an MSc in Artificial Intelligence (AI) at the University of Essex.

🧠 My work lives at the intersection of AI, cognitive science, and computational neuroscience - where I explore how intelligence emerges, adapts, and self-organises.

While I started within traditional AI methods, my trajectory naturally led me toward neuro-inspired systems, probabilistic reasoning, and intrinsic learning mechanisms. I believe that building truly intelligent systems requires more than brute force - it demands understanding how learning itself unfolds.


πŸ” Research focus

  • Cognitive modelling - using RL and probabilistic models to simulate cognitive processes.
  • Intrinsic curiosity and meta-learning - exploring how agents self-motivate and adapt over time.
  • Spiking neural networks and neuron models - bridging bio-realism and machine learning.
  • Multi-agent reinforcement learning (MARL) - modelling distributed, emergent behaviours.
  • Imitation and inverse RL - capturing expert-like behavior and learning from it.
  • Gaussian processes and uncertainty-aware systems - making learning robust, explainable, and grounded.

πŸ’‘ Current initiatives

🧬 Exploring embodied cognition and perceptual emergence through RL and neuro-inspired architectures.

🧠 Independent research projects (including my first preprint on Zenodo) focused on integrating RL into cognitive science frameworks.

🌍 Building bridges with European labs working in cognitive systems, neuro-symbolic AI, and adaptive learning.


🌐 Let's connect

πŸ“« I'm open to research collaborations, academic exchanges, or just a good conversation about intelligence (natural or artificial).


β€œThe mind is not a vessel to be filled, but a fire to be kindled.”
– Plutarch

Pinned Loading

  1. doominator doominator Public

    Doom-based reinforcement learning agent.

    Python

  2. fuzzyl fuzzyl Public

    Analysing fuzzy logic and inference in approximate reasoning.

    Jupyter Notebook

  3. gail_control gail_control Public

    Applying Generative Adversarial Imitation Learning to a control environment.

    Python 1

  4. hyper_tuning hyper_tuning Public

    Demonstrating the use, and impact of, hyperparameter tuning and pipelines in machine learning models.

    Python

  5. q_learning q_learning Public

    Reinforcement learning with Python and OpenAI Gym.

    Jupyter Notebook

  6. spark_ml spark_ml Public

    Creating a machine learning model in a distributed computing environment with PySpark.

    Jupyter Notebook