Richard Gonzalez
Center Director, Research Center for Group Dynamics, Institute for Social Research
Co-Director, BioSocial Methods Collaborative
Amos N Tversky Collegiate Professor, Psychology and Statistics, LSA
Professor of Integrative Systems and Design, College of Engineering
| E-mail: | Email Richard Gonzalez |
| Address: | Research Center for Group Dynamics Institute for Social Research University of Michigan 426 Thompson Street Ann Arbor, Michigan 48106 |
| Phone: | 734-647-6785 |
Articles in Announcements
The runner’s bounce and their performance
Extended our model of capturing heterogeneity in ground reaction forces while running to identify bouncing behavior in elite runners.
Developing a new approach to modeling the ground reaction forces in elite human runners
Runners are commonly modeled as spring–mass systems, but the traditional calculations of these models rely on discrete observations during the gait cycle (e.g. maximal vertical force) and simplifying assumptions (e.g. leg length), challenging the predicative capacity and generalizability of observations. We present a method to model runners as spring–mass systems using nonlinear regression (NLR) and the full vertical ground reaction force (vGRF) time series without additional inputs and fewer traditional parameter assumptions.
Using the transition to siblinghood to understand the development of emotion understanding
We test models of emotion understanding on a data set involving the transition to siblinghood. When families have a second child, the first born only child becomes a sibling and the family dynamics are likely to change.
Cardiovascular health and daily stress among White and Black Americans
Daily stress and cardiovascular reactivity may be important mechanisms linking cumulative life event stress with cardiovascular health and may help to explain racial health disparities. This study assessed links between trajectories of life event stress exposure, daily stressors, and cardiovascular reactivity among Black and White individuals.
Estimating a typical path from GPS data
Finding insights from sensor data such as GPS can be tricky. Commuting between home and work may not always follow the same path as some days there are additional stops for errands or alternate routes taken. We propose an algorithm for extracting a typical path from a collection of trips coded by GPS coordinates.
Predicting satisfaction in romantic relationships
We used machine learning to understand which constructs have greater predictive importance for perceived changes in satisfaction since the pandemic began and satisfaction over the prior week.
Early cognitive decline and its impact on spouse’s loneliness
One of the first papers to study the trajectory of loneliness among spouses of adults with mild cognitive impairment.
Honor: Collegiate Professorship
I became the Amos N. Tversky Collegiate Professor of Psychology and Statistics.
