Find Academic Supervisors

• Scientific Machine Learning
• Simulation-Based Inference
• Hierarchical Bayesian Inference
• +3 more

Jonas Arruda is a Ph.D. student and Researcher at the Life and Medical Sciences Institute (LIMES) of the University of Bonn, affiliated with the Hasenauer Lab. His work bridges Scientific Machine Learning , Simulation-Based Inference , and Computational Biology , with a focus on Hierarchical Bayesian Inference and Neural Posterior Estimation . Education: BSc and MSc in Mathematics at the University of Bonn (with a year at the University of Barcelona), MSc in Economics (2024) Experience: Research visitor at Rensselaer Polytechnic Institute (2025) His research explores amortized inference for large-scale models and neural posterior estimation in non-linear mixed-effects modeling, contributing to both computational statistics and secure distributed learning frameworks. He has presented at the BioInference Conference 2023, where he received the best talk award for early career researchers. Contact: jonas.arruda@uni-bonn.de . Affiliated with the University of Bonn since 2022.

Quan ZhangView profile

Assistant Professor

• Machine Learning
• Statistics
• Interpretable Learning
• +7 more

Quan Zhang is an Assistant Professor in the Department of Accounting and Information Systems at the Broad College of Business, Michigan State University. With a Ph.D. in Information, Risk and Operations Management from the University of Texas at Austin (2020), an M.S. in Biostatistics from the University of Minnesota (2015), and a B.S. in Biological Science and Economics from Peking University (2012), his academic journey bridges diverse disciplines. Education Ph.D., Information, Risk and Operations Management - University of Texas at Austin (2020) M.S., Biostatistics (Ph.D. study) - University of Minnesota at Twin Cities (2015) B.S., Biological Science and Economics - Peking University (2012) His research focuses on interpretable machine learning , particularly balancing model interpretability and capacity using Bayesian and variational inference methods. Applications span telemedicine , quantitative marketing , medical data analysis , and online finance , with recent work on watermarking for copyright protection and robust representation learning. Selected publications include his 2025 paper on Beyond Invisibility: Learning Robust Visible Watermarks , 2023 work on Weibull Racing Survival Analysis , and foundational 2018 contributions to Nonparametric Bayesian Lomax Delegate Racing . His methodological innovations in Bayesian multinomial regression and survival modeling have been published in Advances in Neural Information Processing Systems and the Journal of Machine Learning Research . He contributes to software development with Python and R implementations for Bayesian models, available in his GitHub repository. His teaching includes graduate-level ITM885 Machine Learning and Optimization , emphasizing practical applications in business analytics.

Yizhou ZhangView profile

Assistant Professor

• Programming Languages
• Probabilistic Programming
• Compiler Design
• +4 more

Yizhou Zhang is an Assistant Professor at the Cheriton School of Computer Science, University of Waterloo. He specializes in programming language design and implementation, focusing on balancing expressive power with strong guarantees through language abstractions and compiler verification. Designing modular, type-safe language systems Probabilistic programming and inference optimization Algebraic effects and control flow management His recent publications explore lexical effect handlers, certified compiler frameworks, and nested polymorphism models. Awards include the ACM SIGPLAN Distinguished Paper Award (2023) for his work on probabilistic program compilation. Current advisees include PhD candidates Cong Ma, Zhaoyi Ge, Jianlin Li, and Ende Jin. He has served on program committees for POPL, PLDI, and OOPSLA conferences.