REU participants will be immersed in stimulating interdisciplinary research projects and will work alongside their mentor, other undergraduate and graduate students, postdoctoral associates, and other laboratory personnel daily. Each project will be well-defined and appropriate to the general theme of computational bioengineering. The goal of the program will be to provide students training in basic research that intersects the use of computation and human health, application of the scientific method, and effective communication of scientific findings.
Don Anderson, Professor of Orthopedics & Rehabilitation
Pathomechanical Origins of Posttraumatic Osteoarthritis
The UI Orthopedic Biomechanics Laboratory (UIOBL) endeavors to answer scientific questions in a manner that not only adds to our understanding of musculoskeletal disorders, but that can also be used to improve treatment. We employ the tools of image analysis, computer modeling, and computational stress analysis to objectively quantify phenomena hitherto only assessed subjectively. An area of emphasis has been understanding the abnormal mechanics (pathomechanics) involved in the development of osteoarthritis (OA), one of the most common causes of disability in adults, especially as it develops following articular joint trauma. The REU student will assist in ongoing mechanical analysis of articular fracture severity by participating in image segmentation, model generation, and computational analysis. The student will also assist in ongoing mechanical analysis of articular fractures of the distal tibia aimed at integrating a custom orthotic device into treatment to prevent post-traumatic OA. Students who work on this project will develop skills in computer modeling, MATLAB programming, and data visualization using open source software. For those who work on the custom orthotic devices, skills in video motion analysis will also be acquired.
Terry Braun, Professor of Biomedical Engineering
Disease Variations and Phenotypes
The Coordinated Laboratory for Computational Genomics (CLCG) develops bioinformatics and genomics software systems in efforts to provide clinical decision support for clinical scientists that study inherited and de novo diseases. Specific disease applications are inherited eye diseases, deafness and cancer. Computational biology approaches include the use of local and public heterogeneous, high-throughput "'omics" data, data integration, molecular data (DNA, RNA, single-cell RNA, epigenomics, network and systems biology), and phenotypic data. Data integration uses machine-learning approaches to build models and predict deleterious variants cosegregating with disease phenotypes. Given large, heterogeneous data, high-performance computational resources are also used for model building, model evaluation and analysis. REU students will be involved with the utilization of genomic analysis tools, high-performance computational resources, development of custom software to manage data workflow for analysis and data visualization, and investigation of new tools and resources.
Guadalupe Canahuate, Associate Professor of Electrical and Computer Engineering
Dr. Canahuate's research lab focuses on big-data analysis, machine learning, and high-performance computing. Her research interests are in the area of machine learning, high-dimensional data analysis, and risk modeling. Machine Learning (ML) algorithms are increasingly used in clinical care to improve diagnosis, treatment selection, and prognosis of numerous diseases including cancer. The Surveillance, Epidemiology, and End Results (SEER) Program of the National Cancer Institute (NCI) is a comprehensive source of information on cancer incidence and survival in the United States that includes demographics, cancer staging, and patient survival data. The goal is to extract useful information from this vast amount of data. The REU project will focus on applying machine learning algorithms to SEER data and/or evaluating differences between ethnic/racial groups that lead to different outcomes (health disparities). The REU student will apply both supervised and unsupervised machine learning methods for feature selection, model creation, and clustering using R and/or Python libraries and evaluate performance between different methods.
Deep Learning in Pulmonary Imaging
The research mission of the Pulmonary Imaging and Computer Vision Lab is to develop innovative artificial intelligence techniques for multimodal medical image processing to characterize physiological structure-function relationships. Our lab specializes in studying pulmonary diseases using computed tomography (CT) imaging. A typical REU student will learn how to automatically extract quantitative information from high-dimensional medical images and utilize these features to characterize lung disease. The student will work with 3D, 4D, and dual-energy CT images of patients with chronic obstructive pulmonary disease, lung cancer, COVID-19, and/or acute lung injury. The REU student will learn valuable engineering and computing skills including programming in python, high-performance computing, utilizing graphic processing units, and deep learning. These tools will be applied towards medical image processing, visualization of medical images, training convolutional neural networks, and performing statistical analysis.
Jessica Goetz, Associate Professor of Orthopedics & Rehabilitation
Multiscale Computational Modeling of Articular Cartilage
The Orthopedic Biomechanics Laboratory utilizes a combination of experimental testing, computational modeling, and image analysis techniques to investigate mechanical contributors to orthopedic conditions, musculoskeletal injuries, and clinical treatment approaches. Degenerative joint disease, particularly osteoarthritic joint degeneration after mechanical injury or in association with joint deformities is of particular interest to the investigators in the lab. Analysis of factors contributing to disease development requires consideration of the joint as a whole, as well as consideration of the local mechanics experienced by the different tissues inside the joint. The REU student will extract physical geometry to be modeled from medical images or surface scan data, develop a computational model appropriate for the size scale of the tissue of interest, and use finite element modeling or discrete element analysis to evaluate the acute or chronic mechanical stress applied to the cartilage during an injurious loading regimen.
Jacob Herrmann, Assistant Professor of Biomedical Engineering
Computational Modeling of Respiratory Dynamics
The Herrmann Respiratory Dynamics Lab applies multiscale dynamic imaging and biomechanics to learn how lung structures move, stretch, and transport gas during breathing and mechanical ventilation. Through a combination of experimental techniques and computational modeling, we seek to understand the complex pathophysiologic mechanisms that underlie disease or injury progression (e.g., emphysema, fibrosis, ventilator-induced lung injury), and then evaluate strategies for safe and effective medical interventions and respiratory therapy. A typical REU student project will involve numerical simulation of respiratory structural dynamics and/or gas transport, code documentation, and model verification & validation. Students will develop skills in computational programming (Python/Matlab/C++), numerical methods, high-performance computing, statistics, and scientific communication.
Mathews Jacob, Professor of Electrical and Computer Engineering
Machine learning algorithms for Biomedical Imaging
The Computational Biomedical Imaging Group (CBIG) pursues research on the development of novel machine learning algorithms for the reconstruction and post-processing of medical images. Active research areas include image reconstruction, image analysis, and quantification. Two application areas that are of special relevance are (a) motion robust ultra-high resolution imaging of the brain and (b) comprehensive cardio-pulmonary MRI.
The REU student will develop a physics inspired machine learning algorithm for the recovery of high spatial and temporal resolution MR images. The student will collaborate with graduate students, post-docs, and industrial partners to acquire MRI data at the Magnetic Resonance Imaging Facility at the University of Iowa. The project is an opportunity to learn cutting edge machine learning theory and algorithms, while applying them to solve practical problems of high impact.
Hans Johnson, Associate Professor of Electrical and Computer Engineering
Accelerating Brain Research Through High Performance Computing and Deep Learning Applications
Dr. Johnson’s Scalable Informatics for Neuroscience, Processing and Software Engineering (SINAPSE) laboratory is an interdisciplinary team of computer scientists, software engineers, and medical investigators who develop computational tools for the analysis and visualization of clinical and medical image data. The purpose of the group is to provide the infrastructure and environment for the development of computational algorithms and open-source technologies, and then oversee the training and dissemination of these tools to the medical research community. The REU student will assist in the application of high-performance computational infrastructures towards the understanding brain health and function. The REU student will learn how rigorous software engineering techniques are applied to develop machine learning models that expose novel understandings of how the brain works. The REU student will develop the skills for integrating tools from C++, Python, R, and shell commands in the service of data analysis.
Thomas L. Casavant, Professor of Electrical and Computer Engineering and Biomedical Engineering
Applications of Machine Learning to Personalized Genomic Medicine
The Coordinated Laboratory for Computational Genomics, within the CBCB has been involved in the mapping of human disease traits to genomic loci for nearly 3 decades. The lab develops novel computational methods (algorithms and machine learning models) to reveal complex cause-and-effect relationships between genotype and phenotype. The goal is to develop decision support tools in the form of machine learning models to guide diagnosis and treatment selection for a variety of human disease including cancer, deafness, mental illness, and blindness. The REU student will assist in the use of machine learning approaches to identify informative data resources, develop methods for selecting and improving features, and building models that will predict outcomes with and without alternative candidate treatments regimens. The REU student will learn about DNA/RNA sequencing, protein and metabolic assays, microbiome assays, appropriate and ethical access to clinical and demographic patient information, and machine learning.
Sajan Goud Lingala, Assistant Professor of Biomedical Engineering
Under Sampled Reconstruction of Dynamic Magnetic Resonance Imaging Data
The Laboratory of Quantitative and Dynamic Magnetic Resonance Imaging (MRI) develops new acquisition, reconstruction, and analysis methods for multidimensional MRI applications. In this project, the REU student will develop an accelerated MRI algorithm that recovers dynamic time varying image series (e.g., of a moving vocal tract) from highly under-sampled measurements. This is an ill-posed mathematical problem. The REU student therefore will learn concepts based on linear algebra, signal/image processing, machine learning to design regularizers to make the problem well-posed. These regularizes will be either hand-crafted (e.g., based on prior knowledge) or data-driven (e.g., machine learning based). The REU student will interact daily with the mentor and graduate students, and with a bigger team of personnel spanning this laboratory, the Iowa Institute of Biomedical Imaging, and the Magnetic Resonance Research Facility.
Yang Liu, Associate Professor of Electrical and Computer Engineering
Development & Application of Cancer Imaging Systems
The Integrated Imaging and Cyberphysical System Laboratory at the University of Iowa conducts basic and applied research in intraoperative imaging, augmented reality, computer vision, and cyber-physical systems. A current focus of our effort is the development and application of imaging systems to study cancer and guide interventions. The REU student will collaborate with a graduate student in the development and application of an imaging system to identify key functional and anatomical structures. The REU student will learn about medical imaging, image analysis, cancer biology, and machine learning as part of this project.
Vince Magnotta, Professor of Radiology
Identifying Metabolic Brain Changes with MRI and Machine Learning
The MR Research Facility (MRRF) develops novel imaging techniques to study psychiatric and neurological disorders. A current focus of this effort is the development and application of MR imaging techniques to study metabolic brain changes in bipolar disorder. The REU student under the supervision of a post-doctoral fellow will assist in the use of machine learning approaches to identify differences and changes in brain metabolism and neural circuits thought to underly this psychiatric disorder. The REU student will learn about medical imaging, image analysis, neurobiology, and machine learning for this project.
M.L. Suresh Raghavan, Professor of Biomedical Engineering
Electrochemical Catheter for Blood Flow Measurement
The BioMechanics of Soft Tissues (BioMOST) lab develops and uses experimental and computational methods, based on principles of biomechanics, biomaterials, and medical image processing, to study and repair diseases of the cardiovascular and pulmonary systems. The REU student, under the supervision of a graduate student, will assist on the development of a novel electrochemical catheter for blood flow measurement. The REU student will learn experimental methods involving fluid flow loop studies, tissue mechanical testing, electrochemical methods, fabrication of silicone replicas, etc., as well as computational methods, such as finite element analysis, computational fluid dynamics, computational geometry, and image processing.
Joseph Reinhardt, Professor of Biomedical Engineering
Machine Learning to Better Understand Lung Disease
The Reinhardt Biomedical Imaging Laboratory uses computed tomography (CT) imaging and image processing to better understand the anatomy and physiology of the human lungs and diseases that affect the respiratory system. We develop image processing and machine learning methods to analyze lung CT images gin an effort to better understand diseases like emphysema, asthma, and chronic obstructive pulmonary disease. A typical REU student project will involve developing and/or applying image processing algorithms to identify, measure, and characterize anatomic structures in Lung CT images, or using machine learning to better understand lung anatomy in normal and diseased subjects. The REU student will learn how to use programming tools such as python, R, and C++, as well as learn about image processing, machine learning, and lung physiology and anatomy.
Edward Sander, Associate Professor of Biomedical Engineering
Modeling Cell & Tissue Mechanobiology
The Multi-scale Mechanics, Mechanobiology, and Tissue Engineering Laboratory studies the mechanobiology of soft tissue remodeling using tissue engineering techniques to build in vitro mimics. We combine imaging, mechanical testing, and biological assays with computational models in order to understand which biochemical and mechanical cues are present and responsible for the cellular activities we observe. Our models are composed of systems of linear and non-linear equations solved using various methods, such as finite element analysis. The REU student will meet daily with Dr. Sander and work alongside graduate students on both experiments and modeling. The student will learn Matlab, how to solve systems of linear equations, cell culture, biomaterial fabrication, and microscopy. These skills will be used to quantify differences in cell behaviors, such as migration and matrix synthesis, in response to perturbations in environmental conditions, and to predict how these cues affect tissue remodeling.
Sarah Vigmostad, Associate Professor of Biomedical Engineering
Cardiovascular Surgical Planning
The Vigmostad Computational Biofluid Mechanics Laboratory focuses on developing and employing a userfriendly, cutting-edge, computational modeling package that supports personalized medicine through virtual surgical planning. REU students will gain experience working with cutting-edge computational fluid mechanics software that specializes in image-based fluid-structure interaction modeling. REU students will work with patient-specific medical image data to simulate disease states and mimic various surgical repair options to identify how surgical decision-making impacts hemodynamics and ultimately, patient outcomes so that the best treatment path can be determined.
Rachel Vitali, Assistant Professor of Mechanical Engineering
Driving Dynamic Simulations of Movement with Wearable Sensor Data
The mission of the Human Instrumentation and Robotics (HIR) Lab is to quantify, understand, and distinguish human movement via wearable technologies to better interpret performance, health, and behavior. One of the many challenges associated with this type of work is a comprehensive definition or metric for performance cannot be defined; in fact, most applications require a task-specific definition of performance that has both physical and operational meaning to the stakeholders involved. This project focuses on medical professionals, specifically dentists, who are at significant risk of developing musculoskeletal disorders over a career spent in ergonomically detrimental work conditions. REU students will be involved in how to transform wearable sensor data (i.e., inertial measurement units to measure motion and surface electromyography to measure muscle activity) into inputs for a musculoskeletal model of the neck to better understand the mechanisms by which microinjuries occur and accrue over long periods of time.
Kristan Worthington, Assistant Professor of Biomedical Engineering
Predicting Optimal Scaffold Parameters for Retinal Tissue Engineering
The Worthington Lab focuses on polymeric biomaterials and the ways in which biological systems interact with materials. We also apply this knowledge to the design and creation of materials with structural, mechanical, and chemical properties that meet the needs of specific biomedical applications, especially those involving soft tissue and the nervous system. Our work to date falls into three major categories: 1) precision biomaterials using high-resolution 3D printing; 2) regenerative engineering of the outer retina; and 3) mucoperiosteal wound-healing biomaterials. A student from the Computational Bioengineering REU program would participate in these research projects by helping us to develop mathematical models that can be used to predict and control crosslinking of biopolymers or to create finite element models that enable us to understand the impact of scaffold design and material properties on cell fate in and around biomaterial scaffolds.