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Colloquium

  • Colloquium
  • STAT 5924, CRN 92847
  • Fridays
  •  2:30 pm - 3:30 pm
  • 300 Seitz Hall

Colloquium Schedule Fall 2026

Join us for snacks, drinks, and conversation as we get to know each other better. We’ve reserved the bar area, so we’ll be comfortably inside if it’s too warm/wet outside. We’ll go around and introduce ourselves. Graduate students and faculty, please be ready to share: Your 5-year goal, your current research interests, and a hobby you enjoy

The Maroon Door

418 N Main St, Blacksburg, VA 24060

Wilson Wright

Bio: Dr. Wilson Wright is an Assistant Professor in the Department of Statistics and Data Science at the University of Missouri. Prior to joining MU, he received a PhD in Statistics from Colorado State University. His research focuses on developing spatial models for analyzing ecological and environmental data. In particular, his work considers new ways to model the distributions of plant and animal species while accounting for spatial dependencies and various forms of observation errors. His paper on “Continuous-space occupancy models” received the 2025 Best Paper in Biometrics by an International Biometric Society member Award.

Title: Continuous-space occupancy models

Abstract: Occupancy models are commonly used to infer species distributions over large spatial extents while accounting for imperfect detection. Current approaches, however, are unable to model species occurrence over continuous spatial domains while accounting for the discrete spatial domain of the observed data. We develop a new class of spatial occupancy models that embeds a change of spatial support between the observed data and occurrence process. We use a clipped Gaussian process to represent species occurrence in continuous space which can provide inferences at a finer resolution than the observed occupancy data. Our approach is beneficial because it allows for more realistic models of species occurrence, can account for species occurring in only a portion of a surveyed site, and can relate detection probabilities to these within-site occurrence proportions. We show how our model can be fit using Bayesian methods and develop a computationally efficient MCMC algorithm. We also show how our framework can be extended to handle multiple years of data to learn about species occupancy dynamics in continuous space. We demonstrate our model using simulated data and compare our approach to alternative spatial occupancy models. We also use our model to analyze ovenbird occurrence data collected in New Hampshire, USA.

Location: 300 Seitz 

Dr. Hongtu Zhu

Bio: Dr. Hongtu Zhu is the Kenan Distinguished Professor of Biostatistics, Statistics, Radiology, Computer Science and Genetics at the University of North Carolina at Chapel Hill. He was a DiDi Fellow and Chief Scientist of Statistics at DiDi Chuxing between 2018 and 2020 and held the Endowed Bao-Shan Jing Professorship in Diagnostic Imaging at MD Anderson Cancer Center between 2016 and 2018. He is an internationally recognized expert in statistical learning, medical image analysis, precision medicine, biostatistics, artificial intelligence, and big data analytics. He received an established investigator award from the Cancer Prevention Research Institute of Texas in 2016, the INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice in 2019, the IMS 2027 Medallion award and Lecture, and the COPSS 2025 Snedecor Award. He has published more than 365 papers in top journals, including Nature, Science, Cell, Nature Genetics, Nature Communication, PNAS, AOS, JASA, Biometrika, and JRSSB, as well as presenting 65+ conference papers at top conferences, including meetings for Neurips, ICLR, ICML, AAAI, STAI-X,  and KDD. He is the coordinating editor of JASA and the editor of JASA ACS.

Title: Understanding CNN Efficiency: Statistical Generative Models for Unstructured Image Data

Abstract: Convolutional Neural Networks (CNNs) are foundational in modern image analysis due to their ability to efficiently learn feature representations. However, theoretical understanding of their efficiency remains limited, largely due to inadequate modeling of image structures and their interaction with CNNs. To address this, we introduce novel statistical generative models (SGMs) that decompose images into task-relevant signals and noise, capturing the complexities of natural image data. Based on these SGMs, we propose a feature mapping approach (FMA) to characterize the transformation from raw image data to feature vectors. We analyze CNNs' approximation capabilities, their adaptation to low-dimensional structures, and their efficiency in vision tasks, ultimately developing statistical learning theories for CNN-based image analysis. Our findings reveal the challenges inherent in vision tasks and highlight CNNs' remarkable efficiency in addressing them, providing new insights into their theoretical and practical capabilities. This is based on the joint work with Dr. Guohao Shen.

Location: 300 Seitz 

Location: 300 Seitz 

Location: 300 Seitz 

Location: 300 Seitz 

Location: 300 Seitz 

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Contact Information

Department of Statistics (MC0439)
Hutcheson Hall, RM 406-A, Virginia Tech
250 Drillfield Drive
Blacksburg, VA 24061

Phone: 540-231-5657

Department Head:
Robert B. Gramacy