AI Researcher
Emmanuel Muñiz
Selected work across medical AI, autonomous trading, and robotics, with each project framed around the problem I tackled, the system I built, and the technical stack behind it. This portfolio highlights the repositories and media that best represent my current research direction.
Active Projects
Current work in development.
Research
Medical AI research focusing on two primary domains: radiology and cerebral palsy.
Radiology Report Generation Research
What I did: Developed a novel implementation distinct from current SOTA approaches to maximize information retention in radiology report generation. This led to LAnA ↗ (Layerwise Anatomical Attention), an anatomy-guided model collection that mimics a doctor's diagnostic perception. The model is lightweight, runs on a standard CPU in ~10 seconds, and has garnered over 3,000 Hugging Face downloads.
How I did it: Rather than relying on prompt engineering, I explicitly injected segmented clinical regions into the model by modifying GPT-2's internal attention mechanisms layer-by-layer. Trained entirely on GCP under a strict $300 budget, this architectural innovation outperformed vanilla baselines and was published to arXiv and deployed as an interactive Hugging Face Space.
What I used: PyTorch, Hugging Face (Transformers, PEFT, Accelerate), DINOv3, GPT-based Decoders, Layer-Wise Anatomical Attention, LoRA/QLoRA, Vertex AI (GCP), Docker, and NVIDIA A100.
Radiology Report Generation with Layer-Wise Anatomical Attention
First Author & Lead Researcher • arXiv:2512.16841
Cerebral Palsy Research
What I did: Applied advanced clustering and predictive modeling to a dataset of 4,000+ medical records to identify neonatal risk phenotypes and improve early detection of cerebral palsy.
How I did it: Developed a robust analytical pipeline using Random Forest models for classification and K-Means/Hierarchical clustering for phenotype discovery. Validated model performance and stability using Bootstrap ARI and Silhouette metrics, achieving 92% accuracy in high-risk group identification.
What I used: Scikit-learn, Pandas, NumPy, Random Forest, K-Means, Hierarchical Clustering, Bootstrap ARI, Silhouette Metrics, and Matplotlib/Seaborn.
Neonatal Risk Phenotypes for Cerebral Palsy Based on Integrated Perinatal Exposures: A Cohort Study
Co-Author • European Journal of Paediatric Neurology
Trading Projects
What I did: Developed systematic, probability-driven automated trading strategies and portfolio management algorithms.
How I did it: By applying reinforcement learning for dynamic portfolio balancing and training deep sequential models on time-series and sentiment data.
What I used: Reinforcement Learning (Stable Baselines3), OpenAI Gymnasium, LSTMs, Time Series Modeling, VADER (Sentiment Analysis), and Python/Pandas.
Robotics & Vision
What I did: Built resilient humanoid locomotion, autonomous navigation, physical manipulation, and real-time computer vision tracking systems.
How I did it: Trained GPU-accelerated policies in MuJoCo/Newton and integrated software models with physical actuators, simulated environments, and real-time visual feedback loops.
What I used: PyTorch, CUDA, Newton, MuJoCo, MuJoCo Warp, MJX, reinforcement learning, computer vision, ROS, path planning, and embedded control.
Experience timeline
Resume-backed roles and research milestones.