Potential Mentors

 



Barbara L. Brush, PhD, ANP-BC, FAAN

Professor

Carol J. and F. Edward Lake Term Clinical Professor

Department of Systems, Populations and Leadership

INTERESTS

  • Participatory research
  • Health disparity
  • Family homelessness
  • International nurse migration
  • Nurse workforce policy

The author of two books and over seventy journal articles, Dr. Brush uses a community based participatory research (CBPR) approach to promote health and reduce health inequality among vulnerable community-based populations. She is currently leading a 5-year study of longstanding CBPR partnerships and developing an instrument to measure factors contributing to partnership success. In addition, she is conducting ongoing research with homeless families in Detroit to test the use of narrative interventions in improving trauma disclosure and help seeking and reduce homelessness recidivism. An advanced practice nurse (APN) and historian, Dr. Brush has also been part of a longstanding team designing APN care delivery models and measuring their outcomes in nursing home settings. She examines important issues in nurse workforce development and capacity building and is a leading expert on international nurse migration. Her participation on national and international committees and boards has informed policies on the ethical recruitment of nurses, practice regulations to ensure nursing practice safety and quality, and improvements in health delivery.

CURRENT RESEARCH GRANTS AND PROGRAMS

  • Measurement Approaches to Partnership Success (MAPS): An Innovative Tool for Assessing Long-Standing CBPR Partnerships. NIH, NINR. (PI). 2016-2021. RO1NR016123.
  • CBPR: Enhancing Capacity to Use Innovative Methodologies in the Behavioral and Social Sciences. NIH. (Co-I). PI: C. Coombe and B. Israel. 2016-2018. 1R25GM111837-01.
  • Breaking the Cycle: Piloting a Trauma Intervention in Mothers Experiencing Homelessness. Michigan Institute for Clinical and Health Research. (Co-I). PI: L. Gultekin. 2016-2017. UL1TR000433.
  • Foreign Nurses in Nursing Homes: Recruitment, Education, and Scope of Practice. University of California SF Intramural Grant. (Co-I). PI: L.M. Wagner. 2016-2017.

Ivo D. Dinov, PhD

Professor

Computational Medicine and Bioinformatics, Medical School

Associate Director for Education and Training, Michigan Institute for Data Science

Department of Health Behavior and Biological Sciences Vice Chair

Department of Health Behavior and Biological Sciences

INTERESTS

  • Spacekime and Predictive healthcare analytics
  • Biomedical data science
  • Health and neuroscience informatics
  • Teaching with technology and blended instruction
  • Mathematical modeling and statistical computing

Dr. Dinov is the Director of the Statistics Online Computational Resource (SOCR) and is an expert in mathematical modeling, statistical analysis, high-throughput computational processing and scientific visualization of large datasets (Big Data). His applied research is focused on neuroscience, nursing informatics, multimodal biomedical image analysis, and distributed genomics computing. Examples of specific brain research projects Dr. Dinov is involved in include longitudinal morphometric studies of development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s disease, Parkinson’s disease). He also studies the intricate relations between genetic traits (e.g., SNPs), clinical phenotypes (e.g., disease, behavioral and psychological test) and subject demographics (e.g., race, gender, age) in variety of brain and heart related disorders. Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for science education and active learning.

CURRENT RESEARCH GRANTS AND PROGRAMS

  • NS091856 Biostatistics and Data Management Core, Cholinergic Mechanisms of Gait Dysfunction in Parkinson’s Disease. This research examines the role of cholinergic lesions in gait and balance abnormalities in Parkinson’s Disease and develops novel treatment strategies targeted at cholinergic neurotransmission.
  • DK089503 Integrative Biostatistics and Informatics Core. The Michigan Nutrition Obesity Research Center conducts research to encourage and enable researchers to integrate advanced phenotyping and computational tools to more fully define individual and population characteristics that arise in response to dietary nutrient composition or amount.
  • NR015331 Center for Complexity and Self-management of Chronic Disease investigates health promotion, illness prevention and the burden of chronic illness burgeons using advanced methods, complexity theory, and data analytics.
  • NSF DUE 1023115 The Distributome Project (http://distributome.org/) is an open-source, open content-development project for exploring, discovering, learning, and computational utilization of diverse probability distributions. Role: Site-Principal Investigator.
  • EB020406 Big Data for Discovery Center aims to create a user-focused graphical system to dynamically create, modify, manage and manipulate multiple collections of big datasets and enrich next generation “Big Data” workflow technologies as well as to develop an interface to enable modeling, visualization, and the interactive exploration of Big Data.
  • NSF 1916425: This project builds the Midwest Big Data Hub, a consortium of partners and working groups working in Big Data and including stakeholders in the twelve states of the Midwest Census region (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) and six leading universities that support hundreds of researchers, technologists, and students. This hub provides a basis for collaboration and outreach that increases the potential for benefiting society.
  • NIH 1R01CA233487: Optimal Decision Making in Radiotherapy Using Panomics Analytics. The long-term goal of this project is to overcome barriers related to prediction uncertainties and human-computer interactions, which are currently limiting the ability to make personalized clinical decisions for real-time response-based adaptation in radiotherapy from available data. To meet this need and overcome current challenges, we have assembled a multidisciplinary team including: clinicians, medical physicists, data scientists, and human factor experts.

Yun Jiang, PhD, MS, RN

Assistant Professor

Department of Systems, Populations and Leadership

INTERESTS

Consumer health informatics

  • Data mining
  • Chronic condition self-management
  • Technology acceptance and use
  • Cancer survivorship

Dr. Jiang’s research focuses on informatics- and data-driven solutions for chronic condition self-management, with emphasis on cancer medication adherence and symptom self-management. She is interested in discovering consumer health self-management behavior patterns from data, and developing information technology-based support to empower and engage patients and families in health self-management. Her current research projects include (1) identifying factors associated with patients’ acceptance and use of mobile technology for health self-monitoring and decision support, (2) exploring the complex relationships among patient adherence to oral oncolytics, experience with side effects and self-management activities and (3) understanding cancer patients’ toxicity self-reporting behaviors using natural language processing and machine learning approaches. Dr. Jiang has received trainings in both Nursing and Health Informatics. She is also holding certificates in Gerontology (Gerontechnology track) and Clinical & Translational Science.

CURRENT RESEARCH GRANTS AND PROGRAMS

  • Self-management of Oral Oncolytic Agents and Side Effects among Patients with Cancer. Pilot funding of the Center for Complexity and Self-Management of Chronic Disease (NIH/NINR P20NR015331-03), 2016-2018. Role: Pilot PI.

Kevin Joiner, PhD, APRN, ANP-BC

Assistant Professor

Department of Health Behavior and Biological Sciences

INTERESTS

  • Diabetes
  • Vulnerable populations
  • Type 2 diabetes prevention
  • Risk perception among Latino immigrants and U.S.-born Latinos
  • Telehealth and mobile health

Dr. Joiner’s research interests center on prevention of type 2 diabetes and improvement of health outcomes of type 1 and type 2 diabetes, and the development of self-management interventions for populations that do not have adequate access to preventive and primary care health services. Projects have included describing Latino immigrants’ perceptions of risks of developing type 2 diabetes, developing and testing of a type 2 diabetes prevention intervention delivered via mobile app, describing the experiences during the transition from adolescence to young adulthood in Latino youth with type 1 diabetes, piloting an exercise and coping skills training program for adolescents with type 1 diabetes. He is currently working to develop and pilot test a Spanish-language type 2 diabetes prevention self-management support intervention that is sensitive to unique Latino cultural preferences and accessible to individuals who are limited English proficiency.


Clayton J. Shuman, Ph.D., MSN, RN

Assistant Professor

Department of Systems, Populations and Leadership

INTERESTS

  • Implementation and Dissemination Science
  • Evidence-based Practice
  • Health Services Research
  • Nursing Leadership
  • Neonatal and women’s health nursing

Dr. Shuman’s expertise is in implementation and translation science with a specific focus on the effect of context on implementation success/failure; outcomes (patient, unit, and organization level); and sustainability of intervention effects following implementation.  His research advances the science of implementation by examining the process of transferring interventions into local practice settings and developing and testing implementation interventions that expedite and sustain evidence-based practices (EBP), thus, ultimately improving patient care delivery and outcomes. His current work investigates the role of nurse managers in creating and fostering climates conducive for implementation and use of evidence-based practices. Dr. Shuman’s clinical background is in neonatal intensive care nursing and maternal health, with specific interests in high-risk pregnancy and delivery, improving care of high-risk neonates, and supporting families caring for newborns.

CURRENT RESEARCH GRANTS AND PROGRAMS

  • “Mom is medicine: Exploring factors affecting implementation of maternal-delivered care for opioid-exposed infants.” (PI). UMSN, 7/11/2019-7/10/2021
  • “Neonatal abstinence syndrome: maternal attitudes and pharmacogenetic predictors.” (co-I). M-Cubed, 2018-2020
  • “Nurse perceptions of engaging mothers in the care of their substance-exposed infant” (PI). UMSN.
  • “Preparing for takeoff: Using stakeholder data to inform Re-Implementation of the ‘My Flight Plan for Home’ discharge tool in the neonatal ICU.” (PI). UMSN.

Sarah A. Stoddard, PhD, RN, CNP, FSAHM

Associate Professor

Assistant Professor, Health Behavior and Health Education, School of Public Health

Co-Director, Training and Education Core, University of Michigan Injury Prevention Center

Research Affiliate, Population Studies Center, Institute for Social Research

Research Affiliate, Center for Human Growth and Development

Research Affiliate, Institute on Women and Gender

Department of Systems, Populations and Leadership

INTERESTS

  • Adolescent Substance Use
  • Future orientation
  • Adolescent health
  • Risk and resilience
  • Prevention Science

Dr. Stoddard is recognized nationally for her leadership in adolescent health and her interdisciplinary research to prevent substance use and violence among vulnerable populations of youth. Through research, leadership in community-based partnerships, and linkages with schools, she has advanced the frontier of substance use prevention through interventions that enhance future expectations and school connectedness among vulnerable youth. Her research publications have significantly broadened the knowledge base and importance of future expectations as a key internal asset for the preventing substance use and violence.

Dr. Stoddard’s career has focused on promoting the health and well-being of youth living in communities characterized by substantial health and social disparities, and includes professional experience as a local public health nurse focused on maternal-child health, a nurse practitioner in community- and school-based clinics, and the State Adolescent Health Coordinator for the Minnesota Department of Health.

CURRENT RESEARCH GRANTS AND PROGRAMS

  • National Institute on Minority Health and Health Disparities. The Flint Center for Health Equity Solutions. Project 2: Strengthen Flint Families. Role: Academic Co-PI (Multiple PI: D. Furr-Holden, S. Stoddard) (5U54MD011227)
  • University of Michigan Office of Research, Youth Empowerment Solutions for Positive Futures: A pilot study. Role: Principal Investigator
  • National Institute of Justice. Evaluating the Effectiveness of the Say Something Anonymous Reporting System to Improve School Safety. Role: Co-Investigator (MPI: J. Heinze, H. Hsieh) (2017-CK-BX-0002)
  • National Center for Injury Prevention and Control NIH. University of Michigan Injury Center, Role: Co-I/Director, Training and Education Core (PI: R. Cunningham) (5R49CE002099-05S1)
  • National Institute on Drug Abuse. Intergenerational Transmission of Drug Use in an Urban Sample. Role: Co-Investigator (PI: M. Zimmerman) (R01DA035811)
  • Centers for Disease Control. Michigan Youth Violence Prevention Center Community Engagement and Revitalization. Role: Co-Investigator (PI: M. Zimmerman). (U01 CE002698)
  • Department of Health and Human Services, National Institutes of Health. Reducing HIV Vulnerability Thru Multilevel Life Skills Intervention for Adolescent Men. Role: Co-Investigator (PI: J. Bauermeister, R. Stephenson). (U01 MD011274)

Xingyu (Mark) Zhang, Ph.D.

Research Assistant Professor

Applied Biostatistics Laboratory

Department of Systems, Populations and Leadership

INTERESTS

  • Health outcomes with an emphasis on study design and statistical analysis
  • Electronic health record data mining
  • Predictive and diagnostic modeling in healthcare
  • Emergency heath care and health service
  • Public health surveillance modeling

Dr. Zhang’s research focuses on healthcare outcomes with an emphasis on study design and statistical analysis. The methods he applies include multi-level analysis, time series analysis, infection early warning modeling, medical imaging analysis, feature extraction, pattern classification, neural networks, support vector machine, natural language processing, deep learning, survival analysis, meta-analysis, etc.