Erik Erhardt

Dr. Erik Erhardt specializes in the fields of Bayesian and Frequentist statistical methods, with a particular focus on applications in brain imaging and stable isotope sourcing. As a tenured faculty member in the Department of Mathematics & Statistics at the University of New Mexico, Dr. Erhardt has made significant contributions to both academia and applied research. His role as the Director of the Statistics Consulting Clinic and the Biostatistics and NeuroInformatics (BNI) Core for the Center for Biomedical Research Excellence (COBRE) highlights his leadership in advancing statistical methodologies in biomedical research. Dr. Erhardt's research is characterized by its interdisciplinary nature, bridging the gap between statistical theory and practical applications in neuroscience and environmental science. His work in brain imaging involves developing and applying sophisticated statistical models to better understand neural processes and structures. In the realm of stable isotope sourcing, he employs statistical techniques to trace and analyze isotopic compositions, contributing to advancements in ecological and environmental studies. As a Howard Hughes Medical Institute Interfaces Scholar, Dr. Erhardt is actively involved in collaborative research projects that integrate statistical analysis with other scientific disciplines. This role underscores his commitment to fostering innovation through interdisciplinary partnerships and his dedication to providing statistical consultation to researchers across various fields. In addition to his research endeavors, Dr. Erhardt is deeply committed to education. He teaches Advanced Data Analysis courses, where he imparts his extensive knowledge and experience to students, preparing them for careers in statistics and related fields. His teaching philosophy emphasizes the importance of practical application and critical thinking, equipping students with the skills necessary to tackle complex data-driven challenges. Dr. Erhardt's contributions to the academic community are further exemplified by his upcoming sabbatical during the 2025 academic year. This period will allow him to focus on advancing his research projects and exploring new avenues of inquiry, ultimately enriching his teaching and research activities upon his return. His dedication to both his students and his research ensures that he remains at the forefront of statistical innovation and application.