About Me
I am Carlos, a Postdoctoral Fellow at McGill University, researcher and AI scientist with a Ph.D. in Mathematics & Statistics and over 8 years of experience bridging machine learning, reinforcement learning, and probabilistic modeling with real-world applications in finance, risk management, and healthcare innovation.
My work combines academic rigor and industry experience, creating AI systems that are reliable, explainable, and impactful.
Current Projects
- Risk & Finance: Developing a reinforcement learning framework to manage catastrophic risk in high-dimensional decision-making environments.
- Digital Health: Building end-to-end ML pipelines to improve heart failure screening and biomarker detection, ensuring data quality and advancing clinical research.
Background
- Ph.D. in Mathematics & Statistics (Concordia University); M.Sc. in Mathematical Statistics & Probability; Specialist Degree in Financial Engineering; B.Sc. in Actuarial Science (UNAM).
- Industry roles spanning quantitative finance, actuarial science and applied AI innovation.
- Research fellowships and grants, including MITACS Elevate and Canadian Institute of Actuaries support.
- Publications in top journals such as Finance Research Letters and Mathematical Finance.
- Teaching roles in Machine Learning and AI at Concordia University and Université de Montréal.
My Vision
I aim to push the boundaries of AI for decision-making under uncertainty. Whether advancing reinforcement learning for complex risk management or building machine learning pipelines that turn data into actionable insights. I am committed to creating AI you can trust, powering smarter, more resilient systems for the future.
Let’s connect if you are looking for AI expertise that drives real impact across finance, healthcare, and beyond: