REU Research Mentors

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, University of Iowa

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, University of Iowa

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.

Canahuate Guadalupe, University of Iowa

Guadalupe Canahuate, Associate Professor of Electrical and Computer Engineering

Risk Stratification for Head and Neck Cancer Patients 
Dr. Canahuate's research lab focuses on big-data analysis, machine learning, and high-performance computing. Her research interests are in the area of high-dimensional data, machine learning, similarity and complex searches, and risk modeling for survival analysis. The radiomic features extracted from medical imaging capture spatial information for tumors and organs at risk. Clustering patients based on these features can lead to informative stratifications that can improve survival and toxicity predictions. This REU project will focus on applying feature selection, clustering, and autoencoders to features extracted from head and neck cancer imaging. The goal is to identify patient groups that when combined with other clinical covariates can improve prognosis. The REU student will apply methods to handle right-censored data, data imputation, and both supervised and unsupervised machine learning for feature selection and clustering over high-dimensional data. The REU student will use existing R and/or Python libraries and evaluate performance over real data.

Jessica Goetz, University of Iowa

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.

Hans Johnson, University of Iowa

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.

Tom Casvant, University of Iowa

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, University of iowa

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, University of Iowa

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.

Vincent Magnotta, University of Iowa

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.

Suresh Raghavan, University of Iowa

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, University of Iowa

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, University of Iowa

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, University of Iowa

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.

Kristan Worthington, University of Iowa

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.