About me

I am a postdoc with the Manchester Centre for AI Fundamentals, Department of Computer Science at the University of Manchester, supervised by Dr. Samuel Kaski. In August 2026, I will join the Statistics and Data Science Group at the University of Birmingham as an assistant professor. I am a member of the ELLIS Society. Much of my research is done in collaboration with the Finnish Center for Artificial Intelligence.

I received my PhD in Social and Decision Sciences (concentration in Cognitive Decision Sciences) in 2022 from Carnegie Mellon University, where I was advised by Dr. Daniel Oppenheimer. My dissertation investigated the robustness of Bayesian experimental design to misspecification.

During my PhD, I worked on methods for robust cognitive modeling. I have since extended this work to better understand and address the challenges to robust statistical learning more broadly. Most of my work uses the framework of Bayesian inference. I am particularly interested in the following research areas: model misspecification; experimental design; parameter identifiability and correlation; robustness; transfer learning; statistical learning theory; uncertainty quantification. I maintain a strong interest in the applications of my work to scientific theory and practice, particularly in cognitive science.

Research

Statistical learning is the process of resolving uncertainty about which of a set of candidate models best corresponds to a target system, or data-generating process. It is the foundation of scientific discovery, machine learning, and human cognition. If the investigator’s model class accounts for all sources of uncertainty, existing statistical learning frameworks are remarkably powerful in helping scientists, algorithms, and humans better understand and navigate the world.

My work takes a data-centric perspective on learning in the sense that I focus on (i) how the structure of the available data affects whether and how much learning occurs, and (ii) the development of methods that alter the structure of one’s data in ways that promote learning (e.g., active learning methods).

My recent research is driven by questions like:

• How can active learning methods effectively balance the resolution of multiple forms of uncertainty (e.g., between the values of target and nuisance parameters)?
• What are the causes and consequences of negative transfer in applying knowledge gleaned in particular source environments to a similar-but-distinct target environment?
• When and how does increasing the expressivity of a (cognitive or artificial) system improve its ability to learn?

Contact

I am always enthusiastic to discuss research or answer questions! You can reach me at firstname (dot) lastname (at) manchester (dot) ac (dot) uk.

News

• May 2026: Roubing Tang will present our recent paper Representative, Informative, and De-Amplifying: Requirements for Robust Bayesian Active Learning under Model Misspecification at AISTATS 2026.