Hello, thanks for checking out my website.

I’m Leo Duan. I’m a statistician at University of Florida since 2018.

My research aims to build new statistical methods, theory and computational toolboxes for addressing motivating application problems in neuroscience, engineering, and transportation science. The main statistical interest is in modeling the combinatorial objects that are routinely used in science and engineering (e.g., tree graphs, clustering, signal pathways, integer flow networks, etc.). Our research generates interesting solutions at the intersection between combinatorics, optimization, and Bayesian statistics.

My CV can be found here (updated 2025 May)

Some annoucements!

• Lab openning: I’m looking to advise new doctoral students to work on optimization and diffusion models. If you are currently enrolled in UF graduate program & are interested in doing research with me, send an email to li dot duan at ufl dot edu.

• Prof. Jason Xu @ UCLA Biostats and I @ UF Stats are teaching a short course “Harnessing Optimization in Bayesian Inference” at JSM 2025 in Nashville Tennessee. Here’s the course information — please register if interested!

Harnessing Optimization in Bayesian Inference

Sunday, Aug 3: 8:30 AM - 12:30 PM

Bayesian methods offer intuitive frameworks for specifying generative models, quantifying uncertainty, and developing model-based extensions in applications such as multi-population studies. However, traditional Bayesian approaches often face challenges when dealing with high-dimensional data, combinatorial structures, or strict parameter constraints, which can lead to increased costs in modeling, implementation, computation and inference.

This short course introduces and discusses innovative Bayesian methodologies that directly leverage strengths from the optimization literature, significantly broadening the generality and applicability of Bayesian paradigms. These approaches enable researchers to incorporate optimization-based functions or algorithms into various aspects of Bayesian analysis. Examples include accelerated posterior computation via projection-based MCMC, optimization-based reparameterizations that circumvent combinatorial costs, optimization-induced likelihood functions for structured or constrained data, and hybrid models that combine Bayesian and optimization approaches. We will explore different aspects of this framework, including model building, algorithm development, calibration, justification, and statistical inference. The application of these methods will be illustrated through modeling of networks, multivariate time series, and longitudinal or functional data in biomedical and geospatial settings.

• I have been promoted to Associate Professor with tenure at University of Florida.

Research Interests

Selected forthcoming work (please email me for latest manuscript):

• Zitian Wu, Arkaprava Roy and Leo L. Duan. Model-based Inference for Persistent Homology.

Selected recent work (pre-print and accepted):

• Yu Zheng, Malay Ghosh and Leo L. Duan. Statistical Modeling of Combinatorial Response Data. (Under Review at JASA).
• Cheng Zeng, Eleni Dilma, Jason Xu and Leo L. Duan. Bridged Posterior: Optimization, Profile Likelihood and a New Approach for Generalized Bayes. (Revision at JASA)
• Cheng Zeng, Yaozhi Yang, Jason Xu and Leo L. Duan. Gradient-Bridged Posterior: Bayesian Inference for Models with Implicit Function (Under Review at JRSS-B).
• Edric Tam, David B. Dunson and Leo L. Duan. Exact Sampling of Spanning Trees via Fast-forwarded Random Walks. Biometrika 2025.
• Leo L. Duan and Anirban Bhattacharya. Graph-accelerated Markov Chain Monte Carlo. (Accepted by JMLR pending minor revision).
• Yu Zheng and Leo L. Duan. Gibbs Sampling using Anti-correlation Gaussian Data Augmentation, with Applications to L1-ball-type Models. JCGS 2025.
• Yu Zheng, Leo L. Duan and Arkaprava Roy. Consistency of Graphical Model-based Clustering: Robust Clustering using Bayesian Spanning Forest 2025. (Under Review at Bernoulli)
• Leo L. Duan and David B. Dunson. Bayesian Spanning Tree: Estimating the Backbone of the Dependence Graph. JMLR 2024.
• Maoran Xu and Leo L. Duan. Bayesian Inference with the L1-ball Prior: Solving Combinatorial Problems with Exact Zeros. JRSS-B, 2023.
• Maoran Xu, Hua Zhou, Yujie Hu and Leo L. Duan. Bayesian Inference using the Proximal Mapping: Uncertainty Quantification under Varying Dimensionality. JASA, 2023.
• Leo L. Duan and Arkaprava Roy. Spectral Clustering, Spanning Forest, and Bayesian Forest Process. JASA, 2023.
• Cheng Zeng, Jeffrey Miller and Leo L. Duan. Quasi-Bernoulli Stick-breaking: Infinite Mixture with Cluster Consistency. JMLR, 2023
• Leo L. Duan, Zeyu Yuwen, George Michailidis and Zhengwu Zhang. Bayesian Vector Autoregression using the Tree Rank Prior. JMLR, 2023
• Leo L. Duan, George Michailidis and Mingzhou Ding. Spiked Laplacian Graph. JMLR, 2022.
• Leo L. Duan and David B. Dunson. Bayesian Distance Clustering. JMLR, 2021.
• Leo L. Duan. High-Accuracy Posterior Approximation via Random Transport. JASA, 2021.
• Leo L. Duan. Latent Simplex Position Model. JMLR, 2020.
• Leo L. Duan, Alex Young, Akihiko Nishimura, and David B. Dunson. Bayesian Constraint Relaxation. Biometrika 2019.
• Leo L. Duan, James E. Johndrow, and David B. Dunson. Scaling up Data Augmentation MCMC via Calibration. JMLR, 2018.

Selected Fundings and Awards

2024 UF CLAS Fellowship for Doctoral Student Supervised

2023-2026 NSF-ATD: Geospatial Modeling and Risk Mitigation for Human Movement Dynamics under Hurricane Threats (PI)

2022 UF CLAS Faculty Travel Award

2022-2023 UFII SEED Funding Award

2021 UF Statistics Faculty Award for Doctoral Student Supervised

2018 NeurIPS Bayesian Non-parametrics Award

2015 ASA Paper Competition Award in Section on Bayesian Statistical Science

2014 Woodside Foundation Award for Contribution in Biostatistics and Epidemiology Research

Recent and Upcoming Talks:

August 2025, EcoSta, Tokyo Japan

August 2025, Joint Statistical Meetings, Nashville TN

June 2025, BayesComp, Singapore

May 2025, Statistical Methods in Imaging Conference, Houston, TX

Current PhD Students

Zeyu Yuwen (Expected to graduate in Spring 2025)

Yu Zheng (Expected to graduate in Fall 2025)

Past Trainees

Cheng Zeng

Edric Tam (Postdoctoral Fellow, Stanford University)

Eleni Dilma (Biostatistician, Food and Drug Administration)

Maoran Xu (Assistant Professor, Indiana University)