research

Multi-Armed Bandits and Uncertainty Quantification

Question

How should a learner quantify uncertainty well enough to explore efficiently?

I am interested in bandit settings where the quality of uncertainty estimates directly affects the quality of exploration. This includes questions about how to construct priors, how to share information across related tasks, and how structural assumptions can reduce the amount of data needed for learning.

Related projects

• Multi-Task Bandits and Shared Priors
• Priors from Language Models

Related publications

• Co-Exploration and Co-Exploitation via Shared Structure in Multi-Task Bandits
• Had enough of experts? Elicitation and evaluation of Bayesian priors from large language models

Causal Inference for Efficient Learning

Question

When does causal information actually change what can be learned from limited feedback?

I work on questions at the intersection of causal inference and decision-making, especially in settings where interventions, confounding, or uncertainty about graph structure affect learning. I am interested in when causal information genuinely improves what can be learned, either by reducing sample complexity, improving identifiability, or supporting better downstream decisions.

Related projects

• Causal Decision-Making and Bayesian Optimization
• CLAM and Local Effect Estimation

Related publications

• Graph Agnostic Causal Bayesian Optimisation
• CLAM: Causal Spatial Disaggregation to Infer Local Effects From Coarse Data

Spatio-Temporal Event Modeling

Question

How do we build event models that are expressive enough for real data and still interpretable enough to trust?

This is an earlier line of work of mine, focused on point processes and related models for events evolving over time and space. I have been particularly interested in interpretable model structure, benchmark design, counterfactual event generation, and applications where the model needs to be understandable as well as predictive.

Related projects

• Interpretable Spatio-Temporal Point Processes

Related publications

• Neural Spatio Temporal Point Processes: Trends and Challenges
• HawkesNest: A Multi-Axis Benchmark for Spatio-Temporal Pattern Complexity
• SQUID: A Bayesian Approach for Physics-Informed Event Modeling
• Peculiarities of Counterfactual Point Process Generation

Adjacent Interests

I also have an ongoing interest in fairness-aware learning and human-in-the-loop machine learning, especially where these questions interact with uncertainty quantification or decision support. Earlier work in this direction includes Flexible Group Fairness Metrics for Survival Analysis and engineering work during Julia Summer of Code on fairness algorithms for the MLJ ecosystem.