Statistical Methods in Imaging Conference 2023

The annual meeting of the ASA Statistics in Imaging Section

May 22-24, 2023

The event will take place at the Graduate Hotel on the University of Minnesota Campus in Minneapolis.

SMI aims to bring together investigators from students to established researchers who are working on projects related to statistical methods and their applications in the broad area of imaging science, including, but not limited to, biomedical imaging, immunofluorescence imaging, and geospatial imaging.

View the conference schedule

Dr. Mingyao Li
Dr. Mingyao Li

Our keynote speaker is Dr. Mingyao Li. Dr. Li received her PhD in Biostatistics from the University of Michigan in 2005. She was trained as a statistical geneticist, but since she joined the faculty at the University of Pennsylvania in 2006, she has gradually transitioned her research from traditional statistical genetics to statistical genomics with the goal of having a deeper understanding of the molecular mechanism of human disease. The central theme of her current research is to use statistical and machine learning methods to understand cellular heterogeneity in human-disease-relevant tissues, to characterize gene expression diversity across cell types, to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell and spatial transcriptomics studies, and to translate these findings into clinics. More recently, she expanded her expertise into computational pathology, which is critical when processing and analyzing spatial transcriptomics data. In addition to methods development, she is also interested in collaborating with researchers seeking to identify complex disease susceptibility genes and acting cell types. At UPenn, she serves as the Director of the Statistical Center for Single-Cell and Spatial Genomics. She also chairs the Graduate Program in Biostatistics. She is an elected member of the International Statistical Institute, a Fellow of the American Statistical Association, and a Fellow of the American Association for the Advancement of Science.

Title: Integrating spatial transcriptomics with histology to infer super-resolution tissue architecture

Abstract:  The rapid development of spatial transcriptomics (ST) technologies has made it possible to measure gene expression within the original tissue contexts. The applications of ST have enabled researchers to characterize spatial gene expression patterns, study cell-cell communications, and resolve the spatiotemporal order of cellular development, which have transformed our understanding of the functional organization of tissues. Previous studies have shown that gene expression patterns are correlated with histological features, suggesting that gene expression can be predicted from histology images. However, these existing methods do not fully utilize the rich cellular information provided by high-resolution histology images. In this talk, I will present methods that we recently developed that aim to integrate gene expression with histology to computationally reconstruct ST data that cover the entire transcriptome with near-single-cell resolution. Through comprehensive analysis of diverse datasets generated from both diseased and normal tissues, we show that our super-resolution gene prediction is accurate and useful for different applications in tissue architecture inference.

Kelvin Lim
Kelvin Lim

Our second keynote speaker is Dr. Kelvin Lim, who is a Professor of Psychiatry, Director of Adult Mental Health Research, and Drs. T. J. and Ella M. Arneson Land-Grant Chair in Human Behavior at the University of Minnesota. Dr. Lim’s main research areas are in investigating how disruptions in brain connectivity influence pathophysiology, prognosis, and treatment of neuropsychiatric disorders.

Ranjan Maitra
Ranjan Maitra

Our Founder’s Talk will be given by Dr. Ranjan Maitra, who is a Professor in Statistics at Iowa State University. Dr. Maitra’s main research areas include the analysis of massive data, statistical computing, and statistical learning.

Introduction to the tidyverse and Quarto

Presented by: David Schneck

Tidyverse is a collection of open-source R packages designed with a similar philosophy and structure that aim to make data import, tidying, manipulation, and visualization straightforward and easily-reproducible. Tidyverse contains some of the most well-known and useful R packages for any application of data science, and is a must-have for any aspiring or veteran statistician or data scientist. The following packages will be explored in this workshop with time designated for hands-on coding examples:

  • dplyr: Data manipulation and pipes

  • ggplot2: data visualization and graphic creation

  • tidyr: tools to create clean and tidy working data

  • tibble: a re-imagining of the typical dataframe structure

  • stringr: tools for working with strings

  • Other packages that will be touched upon: readr, purrr, forcats

The second portion of this session will focus on the new technical publishing system: Quarto. Quarto builds upon the utility of Rmarkdown but adds several new features for utility, interactability, readability, and dissemination of analyses. Though Quarto can be used with other languages, we will focus on creating and exploring documents in R. Quarto is a cutting edge resource that will surely help in creating beautiful publications and other documents.

An Introduction to the ANTsX ecosystem through R 

Presented by Dr. Nick Tustison and Dr. Brian Avants

The Advanced Normalizations Tools ecosystem, known as ANTsX, consists of multiple open-source software libraries which house top-performing algorithms used worldwide by scientific and research communities for processing and analyzing biological and medical imaging data. More recent enhancements include statistical, visualization, and deep learning capabilities through interfacing with both the R statistical project (ANTsR) and TensorFlow/Keras libraries (ANTsRNet).  In this hands-on tutorial, we will showcase much of the available general and application-specific functionality in these R-based libraries which will permit the participant to create their own processing and analysis pipelines.

Poster abstract submission is open until Friday, April 21, 2023. Please submit poster abstracts using this Google Form.

There will be two tracks for the student paper competition: 1) Theory and Methods, and 2) Case Studies and Applications. The selected winners for each track will be invited to present their papers in a session at the Statistical Methods in Imaging Conference held at the University of Minnesota on May 22-24, 2023. The first-place winner and runner-up for each track will also receive a cash prize of $1,000 and $500, respectively.

To be eligible for the student paper competition, the student must satisfy the following criteria:

i) Be a degree candidate at an accredited institution in any term during 2022 and 2023,
ii) Be the first author of the paper. The paper must not be published or accepted for publication at the time of submission,
iii) Be a current member of the Statistics in Imaging Section of the ASA;
iv) Be able to present the paper at SMI 2023.

Submission details
The deadline for submission to the student paper competition is April 3, 2023. Submissions should be sent to with Subject Line either “SMI 2023: Theory and Methods” or “SMI 2023: Case Studies and Applications” depending on the intended track of the submission, and the email should contain the following four attachments in PDF format:

i) The applicant’s Curriculum Vitae;
ii) An unblinded version of the manuscript with no more than 26 pages, including the abstract, figures, tables and references. The manuscripts must be double-spaced with at least 11-point font size;
iii) A blinded version of the manuscript;
iv) A letter from a faculty member familiar with the student’s work. The faculty letter must include a verification of the applicant’s student status and, in the case of joint authorship, should indicate what is the contribution of the applicant to the paper.

A room block from May 21 – May 25 is available at the Graduate Hotel – 615 Washington Ave SE, Minneapolis, MN 55414.

Prices start at $169 per night

American Statistical Association

ASA logo

Institute for research in statistics and its applications logo

NISS logo

Participant Type Cost
Faculty Attendee $390
Student or Postdoc Attendee $170

Registration Policy: No refunds are allowed after May 1, 2023. Substitutions are not allowed. 

Questions? Contact Us

Mark Fiecas,

Megan Schlick,

Monday, 5/22

8:30 – 10 a.m. Tutorial: Introduction to the tidyverse and Quarto
Location: Pinnacle Ballroom
10 – 10:15 a.m. Break
10:15 – 11:45 a.m. Tutorial: An Introduction to the ANTsX ecosystem through R
Location: Pinnacle Ballroom
11:45 a.m. – 1:10 p.m. Lunch
1:10 – 2:40 p.m. Student Paper Competition Winners
Location: Pinnacle Ballroom
2:45 – 2:50 p.m. Break
2:50 – 4:20 p.m. New approaches to analyzing neuroimaging data
Speakers: Meimei Liu, Jun Young Park, Kaizhou Lei, Zhengwu Zhang
Location: Pathways
Collaborative Case Study: Novel application to neurological and neuropsychiatric diseases using different imaging techniques
Speakers: Elizabeth Sweeney, Ceren Tozlu, Seonjoo Lee, Dana Tudorascu
Location: Pinnacle Ballroom
4:20 – 6 p.m. Poster Presentations / Mixer
Location: Pinnacle Foyer and Pinnacle Ballroom

Tuesday, 5/23

8:30 – 10 a.m. New Statistical Methods to Improve the Spatial-omics Analysis Pipeline
Speakers: Gregory Hunt, Mansooreh Ahmadian, Zheng Li, and Lukas Weber
Location: Pathways
Recent Developments on Brain Imaging Analysis
Speakers: Kristin Linn, Zhaoxia Yu, Veera Baladandayuthapani, and Ruben Sanchez-Romero
Location: Pinnacle Ballroom
10 – 10:10 a.m. Break
10:10 – 11:10 a.m. Keynote – Kelvin Lim
Location: Pinnacle Ballroom
11:10 a.m. – 12:40 p.m. Lunch
12:40 – 2:10 p.m. Bayesian Methods
Speakers: Dustin Pluta, Rajarshi Guhaniyogi, Michele Guindani, John Kornak
Location: Pathways
Collaborative Case Study: Statistical Methods and Findings from Large Consortia Studies
Speakers: Aaron Alexander-Bloch, Russell T. Shinohara, and Kaidi Kang
Location: Pinnacle Ballroom
2:10 – 2:20 p.m. Break
2:20 – 3:50 p.m. Modern Statistical Methodology on Spatial, Neuroimaging, and Shape Data Analysis
Speakers: Caitlin Ward, Yaotian Wang, Yuexuan Wu, and Shan Yu
Location: Pathways
Recent advancements in statistical methods for brain connectome analysis
Speakers: Ying Guo, Benjamin Risk, Yize Zhao, and Arkaprava Roy
Location: Pinnacle Ballroom
3:50 – 4:00 p.m. Break
4:00 – 5:30 p.m. Efficient Modeling of Multi-Region High-Dimensional Molecular Data
Speakers: Eric Lock, Xi Jiang, Souvik Seal and Weihua Guan
Location: Pinnacle Ballroom
Recent Advances in Neuroimaging Statistics for Investigating Human Brain Function
Speakers: Dayu Sun, Joshua Lukemire, Panpan Zhang, and Hyunnam Ryu
Location: Pathways

Wednesday, 5/24

8:30 – 10 a.m. Recent Advances in Spatial Analysis of Single-cell Imaging
Speakers: Brooke L. Fridley, Siyuan Ma, Julia Wrobel and Misung Yi
Location: Pathways
Statistical Methods for Brain Connectomes
Speakers: Selena Wang, Yi Zhao, Rongjie Liu, and  Xiao Xu
Location: Pinnacle Ballroom
10 – 10:10 a.m. Break
10:10 – 11:10 a.m. Keynote – Mingyao Li
Location: Pinnacle Ballroom
11:10 a.m. – 12:40 p.m. Lunch
12:40 – 1:40 p.m. Founder’s Talk – Rajan Maitra
Location: Pinnacle Ballroom
1:40 – 1:50 p.m. Break
1:50 – 3:20 p.m. Statistical Methods for Analyzing Multiview and Multi-session Imaging Data
Speakers: Suprateek Kundu, Vince Calhoun, and  Xin Ma
Location: Pathways
Collaborative Case Study: Event-related potential brain-computer interface data present challenges and opportunities for novel statistical methods
Speakers: Jane E Huggins, Lexin Li, and Tianwen Ma
Location: Pinnacle Ballroom
3:20 – 3:30 p.m. Break
3:30 – 5:00 p.m. Penalized Regression and Functional Data Analysis
Speakers: Jeffrey Morris, Zhiling Gu, Todd Ogden and Gang Chen
Location: Pinnacle Ballroom
Advances in Statistical Methods for Transmission Electron Microscopy
Speakers: David S. Matteson, Andrew M. Thomas, and Yuchen Xu
Location: Pathways
5:00 – 5:10 p.m. Closing Remarks
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