Rafael Cabral

About Me

• I have a Ph.D. in statistics and comprehensive expertise in applied and computational statistics. I specialize in developing robust modeling frameworks for spatial and temporal data and stochastic processes. My academic journey was marked by groundbreaking research in latent Gaussian models, which led to significant advancements in the field, papers in prestigious journals, and multiple awards. With a strong foundation in R, Python, and C++, my research has enhanced the precision of predictions in climate science, disease mapping, and econometrics.

Professional Path

• Research Fellow, National University of Singapore: Led the development of innovative Bayesian clustering algorithms, achieving groundbreaking accuracy in numismatics; consulting for the Singapore Institute of Clinical Sciences in the analysis of medical data (functional linear data analysis, mediation/causal studies, etc.)
• Intern in various Industry Roles: From optimization algorithms to deep learning in medical imaging, I have applied my statistical acumen to solve diverse, real-world problems.

Academic Highlights

• Ph.D., Statistics: My dissertation criticized and robustified latent Gaussian models, enhancing their application in real-world scenarios.
• M.Sc., Applied Mathematics: Investigated extreme weather patterns in Germany and new developments in extreme value theory

Recognitions & Engagements:

• Al-Kindi Statistics Research Student Award: For outstanding contributions to statistical research.

Dean’s List Award, KAUST: Recognized for exceptional academic performance and research excellence.

• Asteroid Discovery Award: My interest in astronomy led to the discovery of asteroid 2013 EZ7, confirmed by the Minor Planet Center of the University of Haavard.

Technical Expertise

• I am proficient in a broad array of tools and technologies, including R, Python, C++, MATLAB, and Pytorch/TensorFlow, and have taken specialized courses in financial markets and data science from prestigious institutions.

Objective & Persona:

• Driven by a passion for discovery and efficiency, I seek to collaborate with professionals and organizations that are at the forefront of innovation in statistics and computing. Let's connect and explore how we can drive future advancements together.

I have worked for a variety of AI startups in the past as a Data Science. I have experience in research and development in statistics and machine learning, and can program in R, Python and C++.

PhD in Statistics from KAUST

MSc in Applied Mathematics from University of Lisbon

BSc in Physics from University of Lisbon