Announcing the 2023-2025 Junior Investigator Mentorship Class

Each year, we select a group of early career investigators who show exemplary potential in their careers as researchers, and interest in advancing their knowledge of the dementia field. Each mentee, selects a group of mentors from Michigan Alzheimer’s Disease Center faculty to advance them in these goals. Mentees meet with mentors regularly, and work toward a project of their choosing that connects their current research strengths with an aspect of dementia research.

This program is central to our aims as a center, and supporting the next generation of leaders is pivotal to our mission as a leader in dementia research.

We are pleased to announce the 2023-2025 class of REC mentees:

Andrew Brouwer, PhD, MS, MA, University of Michigan, Assistant Research Scientist develops and uses computational epidemiology to enhance our understanding of the complex patterns and dynamics that underlie disease data, as well as the implications of those patterns for public health practice. Systems thinking is a fundamental theoretical framework in epidemiology, and Dr. Brouwer uses it to bring new insights about the population-level implications of transmission processes, disease etiology and progression, and behavior. Dr. Brouwer’s work is data-driven, focused on developing models for inference in epidemiology and public health. His s work is united by the use of mechanistic frameworks as a lens to interpret real data, a focus on longitudinal patterns and dynamics, and by a quantitative toolset that includes compartmental differential equation models, stochastic multistate transition models, age–period–cohort models, among others. He is an expert in methods that address questions of parameter identifiability, estimation, and uncertainty.

Elizabeth Litkowski, PhD, University of Michigan is a postdoctoral research fellow in the Kaczorowski Lab who returned to graduate school after a diverse background in technology businesses.  She holds a B.S. in Mathematics and an MBA which enabled her to hold roles in businesses as varied as electronic component supply chain distribution to oil/gas drilling before moving to work with theoretical statisticians.  Upon returning to school, she completed an MS degree in Biostatistics evaluating gene networks perturbed by ethanol exposure in a LXS mouse panel to identify potential targets for treating alcohol addiction.  In her PhD in genetic Epidemiology, she studied the genetic risk between diabetes and dementia in the Million Veteran Program (MVP), a large biobank of U.S Veterans with clinical and genotype data. Using a technique called Mendelian randomization, she found evidence of causality between diabetes and dementia in MVP.  In her postdoctoral research, she is pursuing the next step of the diabetes-dementia link through analysis of glucose tolerance with cognitive impairment in a gene by diet study in a mouse panel intended to emulate human diversity.

Norman Scheel, Dr. rer. nat. (Ph.D.), Dipl.-Inf., Michigan State University, Postdoctoral Fellow, Research Associate is on the cutting edge of multi-disciplinary academic training, the prestigious University of Lübeck, Germany allowed me to pursue my education at the cross-sections of computer science, medical informatics, and medicine. Connecting the fields of medical imaging and data science was my core interest during my higher academic education. With great pride, I received a German Diploma in Informatics (Dipl.-Inf., comparable to a combined B.S. and M.S. degree) under the supervision of Prof. Dr.-Ing. Siegfried J. Pöppl († 2019), a visionary and early spearhead in medical image computing in Germany. My doctoral education focused on computational neuroscience at the Graduate School for Computing in Medicine & Life Sciences, University of Lübeck, Germany, for which I received a very prestigious Scholarship from the German Universities Excellence Initiative. Under the supervision of Prof. Dr.rer.nat. Amir Madany Mamlouk, (Institute for Neuro- and Bioinformatics and Center of Brain, Behavior, and Metabolism (CBBM), University of Lübeck, Germany), I evaluated machine learning methods on resting-state functional MRI data, especially in high dimension – low sample size scenarios. As advanced machine-learning approaches proved to be very sensitive towards noise and confounds in functional MRI data, a great amount of my doctoral research focused on artifact removal strategies and their effects on machine-learning-derived biomarkers. In my postdoctoral fellowship at the Department of Neurology, University Medical Center Schleswig-Holstein, Germany, I further investigated a method to measure the dimensional complexity of resting-state functional MRI recordings of the human brain and found significant changes in the complexity of brain function due to healthy aging. Based on this finding I was awarded a developmental grant to test the dimensional complexity of resting-state fMRI data as a biomarker for Alzheimer’s disease, by the Michigan Alzheimer’s disease research center (MADRC). In my postdoctoral time at Michigan State University, I have been able to work closely with Dr. David Zhu, and a multi-disciplinary multi-university team, co-leading the imaging cores of major NIH R01 clinical trials investigating the underpinning relationships of hypertension and the development of Alzheimer’s disease. All throughout my postgraduate career I have been teaching and mentoring students in the many aspects of data- and neuroscience and am proud that I was recently awarded Michigan State University’s 2023 Postdoctoral Excellence in Teaching and Mentoring Award.

Connie Wu, PhD, University of Michigan is a Research Assistant Professor in the Life Sciences Institute and an Assistant Professor in the Departments of Biomedical Engineering and Pharmaceutical Sciences at the University of Michigan. Her lab launched in January 2023 and focuses on (1) the development and translation of ultrasensitive single-molecule detection technologies for diagnostic applications in Alzheimer’s disease and cancer; and (2) multifunctional RNA therapeutics for cancer immunotherapy. Connie obtained her B.S. in chemical engineering from Yale University, where she worked with Dr. Paul Van Tassel in designing porous layer-by-layer polymer films for tissue engineering applications. She pursued her Ph.D. in chemical engineering at MIT in Dr. Paula Hammond’s lab, where she engineered a highly potent small interfering RNA (siRNA) nanoparticle delivery system via nucleic acid engineering and polymer chemistry approaches. She then transitioned to the diagnostics field for her postdoctoral research in the lab of Dr. David Walt at Brigham and Women’s Hospital and the Wyss Institute at Harvard University, where she pioneered ultrasensitive single-molecule detection methods that can measure attomolar (10-18 M) protein concentrations with versatile multiplexing capabilities. In parallel, she developed ultrasensitive digital assays for detecting the long-interspersed element-1 (LINE-1) retrotransposon-encoded protein ORF1p in blood as a highly specific multi-cancer biomarker. Connie is a Biological Sciences Scholar at the University of Michigan and was the recipient of multiple fellowships during her Ph.D. and postdoctoral training, including an NSF Graduate Research Fellowship, MIT Presidential Fellowship, and NIH Ruth L. Kirschstein F32 Postdoctoral Fellowship.

Arthur Zhou, PhD, University of Michigan is a Postdoctoral Research Investigator in the Department of Computational Medicine and Bioinformatics at the University of Michigan. His primary research interest is the discovery and analysis of structural genomic variation within human populations and its impact on health and disease. Structural variations (SVs) have long been known to shape population diversity and lead to many genetic disorders. By combining bioinformatics pipelines and the data from variable genomic technologies, we can explore the SVs’ origins, distribution, and mutational processes. An outstanding problem in the field is that many SVs are potentially overlooked, lying in complex genomic regions that hinder the discovery by traditional methodologies and bioinformatics pipelines. One category of genomic variation, repetitive retrotransposable elements, is of particular interest to my current research. By developing novel analytical tools and leveraging cutting-edge genomic technologies, particularly the emerging long-read sequencing technologies, we can penetrate these intricate regions and resolve these cryptic SVs as we have never done before.

Elisa Torres, PhD, RN, Wayne State University is an Associate Professor at Wayne State University, College of Nursing. Dr. Torres’ program of research involves physical activity and the brain. Her current research aims to determine if biological aging is a mechanism by which physical activity is associated with less risk for Alzheimer’s disease.