The Department of Statistics & Data Science at UCLA is delighted to welcome Drago Plecko as a new tenure-track Assistant Professor. His appointment strengthens the department’s research at the intersection of causal inference, data science, and artificial intelligence.

Drago’s research develops methods for causal inference, emphasizing both statistical foundations and computational scalability. He is particularly interested in using causal models to make AI systems more trustworthy — advancing fairness, explainability, and algorithmic recourse. His applied work focuses on medical data, especially causal questions in epidemiology and the use of AI in intensive care medicine. Before joining UCLA, Drago was a Postdoctoral Scholar in Computer Science at Columbia University, earned his Ph.D. in Statistics from ETH Zürich, and completed his undergraduate and master’s studies in Mathematics at the University of Cambridge.

With expertise in machine learning methods and theory, UCLA Department of Statistics and Data Science assistant professors, Dr. Xiaowu Dai and Dr. Yuhua Zhu, are poised to engage in further cutting-edge research with support from the Hellman fellowships.

The UCLA Hellman Fellows Program was established through the generosity of the Hellman Fellows Fund to support the research of promising assistant professors who show promise for great distinction in their research and creative endeavors across all fields of study. Professor Dai and Professor Zhu are two of 24 rising professors who were selected from a wide pool of talented applicants.

Professor Dai’s research focuses on developing machine learning methods for data-driven inference and decision-making, leveraging tools from game theory and dynamical systems. He also leads the SCALE Lab, where his team addresses a broad range of emerging challenges at the intersection of AI and society.

Professor Zhu’s research sits at the intersection of partial differential equations (PDEs) and machine learning, with a focus on using PDE frameworks to understand and design efficient algorithms for reinforcement learning and nonconvex optimization. Her work includes model-free approaches to continuous-time RL using discrete-time data, consensus-based global optimization via interacting particle system, and theoretical analysis of machine learning algorithms. By leveraging tools from PDEs, she develops provably efficient algorithms that are robust, scalable, and grounded in mathematical theory.

Congratulations to both of them!

We are delighted to share that Dr. Judea Pearl, Chancellor’s Professor of Computer Science and Statistics, has been elected a Fellow of the Royal Society—one of the world’s most prestigious scientific academies.

The honor recognizes Dr. Pearl’s foundational contributions to artificial intelligence, particularly his pioneering work on causal reasoning and Bayesian networks. His groundbreaking research has transformed the way scientists and engineers understand cause-and-effect relationships under uncertainty, influencing fields ranging from statistics and medicine to law and public policy.

Established in 1660, the Royal Society is the oldest scientific academy in continuous existence. Dr. Pearl joins a distinguished lineage of fellows that includes Isaac Newton, Charles Darwin, and Alan Turing.

We congratulate Dr. Pearl on this remarkable achievement and well-deserved honor!

https://royalsociety.org/news/2025/05/new-fellows/