معرفی
David Janz is a Florence Nightingale Bicentenary Fellow in Statistics at the University of Oxford's Department of Statistics, specializing in theoretical machine learning and sequential decision-making systems.
His educational pathway includes:
- Undergraduate studies in Engineering, Economics, and Management at Oxford
- PhD at Cambridge supervised by José Miguel Hernández-Lobato and Zoubin Ghahramani
- Postdoctoral work with Csaba Szepesvári at the University of Alberta
Dr. Janz's research centers on bandit algorithms and reinforcement learning theory, with applications in adaptive medical trials and sequential experimentation. His work bridges statistical theory with practical decision-making frameworks, emphasizing mathematical rigor in exploration-exploitation tradeoffs and optimization under uncertainty. Key contributions include novel analysis of randomized exploration mechanisms and efficient sampling techniques for complex probabilistic models.
Recent publications reveal a concentrated research trajectory in theoretical machine learning, particularly advancing linear bandit theory (2025), perturbation-based exploration methods (2024), and scalable Bayesian inference (2023). These works collectively strengthen foundations for adaptive systems across medical, industrial, and AI domains.
Scientific recognition includes:
- Florence Nightingale Bicentenary Fellowship
- Outstanding Paper Award at International Conference on Algorithmic Learning Theory (2025)
Dr. Janz actively mentors PhD candidates in machine learning theory at Oxford and contributes to the Statistical Theory and Methodology and Computational Statistics and Machine Learning research groups. His collaborative network spans institutions including Cambridge, Alberta, and Oxford's medical research divisions, with ongoing work in theoretical guarantees for adaptive experimentation.