Anna Kratz, PhD, is Assistant Professor of Physical Medicine and Rehabilitation and the Center for Clinical Outcomes Development and Application (CODA) at the University of Michigan, Ann Arbor.
Dr. Kratz’s clinical research is focused on the characteristics and mechanisms of common symptoms (e.g. pain, fatigue, cognitive dysfunction) and functional outcomes in those with chronic clinical conditions. Using a combination of ambulatory measurement methods of physical activity (actigraphy), heart rate variability, galvanic skin response, and self-reported experiences, her research aims to overlay the patient’s day-to-day experience with physiological markers of stress, sleep quality, and physical activity. She utilizes a number of computational approaches, including multilevel statistical modeling, signal processing, and machine learning to analyze these data. The ultimate goal is to use insights from these data to design better clinical interventions to help patients better manage symptoms and optimize functioning and quality of life.
Dr. Schnell works at the interface between biophysical chemistry, mathematical and computational biology, and pathophysiology. As an independent scientist, his primary research interest is to use mathematical, computational and statistical methods to design or select optimal procedures and experiments, and to provide maximum information by analyzing biochemical data. His laboratory deals with the following topics:
(i) Development and implementation of mathematical, computational, and statistical methods to identify and characterize reaction mechanisms.
(ii) Investigate and test performance design of experiments or standards to quantify, interpret and analyze biochemical data.
(iii) Development of new algorithms and software to analyze biochemical data.
The key objective of my research is to create suitable standards and appropriate support of standards leading to reproducible results in the biochemical sciences. Reproducibility is central to scientific credibility. Meta-research has repeatedly shown that accurate reporting and sound peer-review do not by themselves guarantee the reproducibility of scientific results. One of the leading causes of poor reproducibility is limited research efforts in quantitative biology and chemometrics. In my laboratory, we are developing new ways to assess the reproducibility of quantitative findings in the biochemical sciences.
As a team scientist, Dr. Schnell’s research interest is to investigate complex biomedical systems comprising many interacting components, where modeling and theory may aid in the identification of the key mechanisms underlying the behavior of the system as a whole. His collaborators are primarily basic scientists who focus on the identification of molecular, biochemical or developmental mechanisms associated with diseases. To this end, Dr. Schnell’s expertise plays a central role in the identification of these mechanisms. Using mathematical and computational models, Dr. Schnell can formulate several hypothetical model mechanisms in parallel, which are compared with independent experimental data used to construct the models. The resulting comparisons are then independent between models, and any models that satisfy statistical measures of similarity will be used to make predictions, which will be tested experimentally by his collaborators. The model validated by the experiments will be considered the mechanism capable of explaining the behavior of the systems.
Dr. Currie develops ecosystem process models to simulate biogeochemical cycling and plant community ecology in wetlands, forests, mixed-use and human-dominated landscapes. He models social and ecological interactions that relate to sustainability in the environment and society’s use of natural resources. He uses statistical modeling, including structural equation modeling, to study landscape-scale patterns in forest fragmentation and tree cover. He is interested in transdisciplinary research, model ontologies and model linkages across fields.
Research interests include quantile regression modeling for associations related to possibly unusual or extreme events, subgroup analysis, and uncertainty quantification after model selection.