My research seeks to exploit graph-based modeling theory and the tools of machine learning for efficient control of physical dynamical systems and control co-design in these systems. I am particularly interested in the design of graph-based machine/deep learning model structures that are compatible with basic physics, and using those model structures for real-time actions. Application … Read more
Alex’s research interests include machine learning, time series, multi-agent systems, uncertainty quantification, and scientific modeling. His recent focus is on developing trustworthy AI systems that can offer insightful guidance for critical decisions, especially in applications involving complex spatiotemporal dynamics. His work is primarily motivated by real-world problems in public health, environmental health and community resilience.
My goal as an ecologist is to apply ecological theory to help solve real-world conservation issues. Specifically, I seek to identify the mechanisms by which behavioral, population, and community dynamics mediate nutrient and energy pathways. The objective is to improve our ability to predict ecological outcomes, and enhance conservation efficacy such as the sustainability of … Read more
Dr. Gaynanova’s research focuses on the development of statistical methods for analysis of modern high-dimensional biomedical data. Her methodological interests are in data integration, machine learning and high-dimensional statistics, motivated by challenges arising in analyses of multi-omics data (e.g., RNASeq, metabolomics, micribiome) and data from wearable devices (continuous glucose monitors, ambulatory blood pressure monitors, activity … Read more
Analysis of policing technology and police data, including impact assessment of surveillance technology, media sentiment analysis, and fatal police violence. Methods include topological data analysis, natural language processing, multivariate time series analysis, difference-in-differences, and complex networks.
Research on computational modeling of energy materials design and optimization 1) I led this large research project on developing machine-learning guided materials discovery demonstrating speed-up of over 80% over traditional methods. 2) My research group runs a popular Scientific Machine Learning webinar series: https://micde.umich.edu/news-events/sciml-webinar-series/
Research in the Lindsey Lab focuses on using simulation to enable on-demand design, discovery, and synthesis of bespoke materials. These efforts are made possible by Dr. Lindsey’s ChIMES framework, which comprises a unique physics-informed machine-learned (ML) interatomic potential (IAP) and artificial intelligence-automated development tool that enables “quantum accurate” simulation of complex systems on scales overlapping … Read more
My research program at the University of Michigan (UM) integrates the fields of biophysics, quantitative systems biology, and bottom-up synthetic biology to understand complex stochastic cellular and developmental processes in early embryos. We have developed innovative computational and experimental techniques in microfluidics and imaging to allow high-throughput quantitative manipulation and single-cell lineage tracking of cellular … Read more
My research focuses on understanding and quantifying climate change impacts on hydroclimatic extremes. From heavy storms and floods to extreme heatwaves and droughts, I study the changing characteristics of these events and their impacts on our daily lives. I use a wide range of data-driven methods such as causal inference, information theory, nonlinear dynamics and … Read more