Daniel Montoya is an AI research engineer with 9 years of multidisciplinary experience applying machine learning, statistics, and computer vision to safety-critical systems. Currently based in France and working on evidential deep learning for collaborative perception in autonomous driving, he has also led research on OOD and anomaly detection for classification, detection, and segmentation. Trained in physics and AI (Université Paris-Saclay), he pairs strong mathematical foundations with practical skills in Python, C/C++, R, and production-oriented research. His background spans drug-design graph neural networks, generative models with symmetry, and FPGA-accelerated neural inference—showing both theoretical depth and systems-level know-how. Daniel’s early work in psychometrics and applied statistics gives him a nuanced view of experimental design and evaluation that improves model validation in real-world deployments. He combines curiosity-driven research with hands-on engineering to push safer autonomy from lab prototypes toward robust field-ready systems.
9 years of coding experience
7 years of employment as a software developer
Diplomatura Prevención de Problemáticas Psicosociales en Ámbito Universitario, enfasis en adicciones, Diplomatura Prevención de Problemáticas Psicosociales en Ámbito Universitario, enfasis en adicciones at Universidad Luis Amigó
Physics, Exact sciences, Physics, Exact sciences at Universidad de Antioquía
Contributions:11 commits, 6 PRs, 8 pushes in 1 year 10 months
nodejsexpressnodeexpress-server
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