I am an undergraduate in Applied Mathematics and Computer Science at Universidad del Rosario in Bogota, Colombia, supported by a national merit scholarship. I am preparing for a research career in computational neuroscience and NeuroAI, with interests in learning, adaptive behavior, reinforcement learning, network science, and dynamical systems.
I build computational models and research software for studying how biological and artificial agents learn, coordinate, and make decisions under uncertainty. My priority is not just predictive performance: I care about reproducibility, identifiability, meaningful controls, and claims that remain honest under audit.
- Computational neuroscience: Neuromatch Academy 2026, Computational Neuroscience track. I am investigating connectivity and behavior in motor recurrent neural networks using paired held-out evaluation.
- Supply-chain resilience with Prof. Alexander Garrido at Universidad del Rosario. I develop discrete-event simulations and decision-policy evaluations while auditing the information and intervention rights required for adaptive policies to improve over strong baselines.
- Collective behavior and coordination with Prof. Edgar Andrade-Lotero at Universidad del Rosario / UC Davis. I use behavioral experiments, network science, and symmetry-aware spectral methods to study coordinated specialization.
| Repository | Focus |
|---|---|
| nma-motor-rnn-connectivity | Active Neuromatch project on motor-RNN connectivity, with reproducible notebooks and paired held-out evaluation. |
| scres-ia | Claim-audited research framework for supply-chain resilience using discrete-event simulation, optimization, and reinforcement learning. |
| spectral-cognitive-labor | Symmetry-aware spectral graph analysis of self-organized division of cognitive labor. |
| HeliOS | Educational RISC-V 64 kernel with scheduling, interrupts, synchronization, a shell, and CI smoke tests. |
| secop-risk-alerts-co | Explainable data pipeline and deployed decision-support prototype for Colombian public-procurement data. |
| chaoslab-double-pendulum | Numerical study and interactive visualization of classical chaos in a double pendulum. |
- Reproducible environments, automated tests, and machine-readable artifacts.
- Held-out evaluation, preregistered gates, strong baselines, and explicit stopping rules.
- Clear separation between observations, inferences, and proposed mechanisms.
- Null results and failed gates treated as informative scientific outcomes.
Research: Python, PyTorch, NumPy, SciPy, pandas, NetworkX, SimPy, Gymnasium, Stable-Baselines3, Jupyter, LaTeX, Quarto
Engineering: C, SQL, FastAPI, Docker, Git, pytest, GitHub Actions, Linux
- Bogota, Colombia
- thomas.chisica@urosario.edu.co
I am open to research collaborations, internships, and conversations about computational neuroscience, NeuroAI, and rigorous computational modeling.