Hi, I'm Minas.
I am a Postdoctoral Fellow at the University of California, Berkeley, where I conduct research at the intersection of artificial intelligence, statistics, and physics. My focus involves developing Bayesian machine learning techniques with applications in the physical sciences. I received my PhD in Astrophysics in 2022 from the University of Edinburgh, where my thesis pioneered novel methods for scalable and gradient-free Bayesian inference with applications in astronomy. Prior to my doctorate, I obtained a Master's degree in Applied Mathematics from the University of Cambridge and a Bachelor's degree in Physics from Aristotle University of Thessaloniki.
My recent research focuses on developing artificial intelligence and machine learning tools to facilitate Bayesian inference in the physical sciences. I am particularly interested in designing algorithms that can efficiently sample from high-dimensional posterior distributions when the gradient is intractable. By combining Bayesian probabilistic methods and AI, I aim to push the boundaries of statistical and computational modeling. My goal is to create generalizable techniques that can provide insight into fundamental questions across astronomy, physics, and chemistry.