Dr. Avestruz is a computational cosmologist. She uses simulations to model, predict, and interpret observed large-scale cosmic structures. Her primary focus is to understand the evolution of galaxy clusters. These are the most massive gravitationally collapsed structures in our universe, comprised of hundreds to thousands of galaxies. Other aspects of her work prepare for the next decade of observations, which will produce unprecedented volumes of data. In particular, she is leading software development efforts within the clusters working group of the Large Synoptic Survey Telescope to calibrate galaxy cluster masses from simulation data. Dr. Avestruz also incorporates big data methods, including machine learning, to extract gravitational lensing signatures that probe the mass distribution of massive galaxies and galaxy clusters.
Laura Balzano is an Associate Professor in Electrical Engineering and Computer Science at the University of Michigan. She is an Intel Early Career Faculty Honor Fellow and received an NSF BRIGE award. She received all her degrees in Electrical Engineering: BS from Rice University, MS from the University of California in Los Angeles, and PhD from the University of Wisconsin. She received the Outstanding MS Degree of the year award from the UCLA EE Department, and the Best Dissertation award from the University of Wisconsin ECE Department. Her main research focus is on modeling with highly incomplete or corrupted data, and its applications in networks, environmental monitoring, and computer vision. Her expertise is in statistical signal processing, matrix factorization, and optimization.
Dr. Ivo Dinov directs the Statistics Online Computational Resource (SOCR), co-directs the multi-institutional Probability Distributome Project, and is an associate director for education of the Michigan Institute for Data Science (MIDAS).
Dr. Dinov is an expert in mathematical modeling, statistical analysis, computational processing and visualization of Big Data. He is involved in longitudinal morphometric studies of human development (e.g., Autism, Schizophrenia), maturation (e.g., depression, pain) and aging (e.g., Alzheimer’s and Parkinson’s diseases). Dr. Dinov is developing, validating and disseminating novel technology-enhanced pedagogical approaches for scientific education and active learning.
Current research in the Laboratory for Interactive Visualization in Engineering (LIVE) is focused on perceptual robotics, and its applications in the construction, operation, and maintenance of civil infrastructure systems. We are also conducting research in real-time visualization and its applications in engineering process monitoring and control. Distributed interactive simulation of coupled civil infrastructure processes is also an active research area pursued in LIVE.
Prof. El-Tawil’s general research interest lies in computational modeling, analysis, and testing of structural materials and systems. He is especially interested in how buildings and bridges behave under the extreme loading conditions generated by manmade and natural hazards such as seismic excitation, collision by heavy objects, and blast. The focus of his research effort is to investigate how to utilize new materials, concepts and technologies to create innovative structural systems that mitigate the potentially catastrophic effects of extreme loading.
- Much of his research is directed towards the computational and theoretical aspects of structural engineering, with particular emphasis on computational simulation, constitutive modeling, multiscale techniques, macro-plasticity formulations, nonlinear solution strategies and visualization methods. Prof. El-Tawil also has a strong and long-sustained interest in multi-disciplinary research. He has conducted research in human decision making and social interactions during extreme events and the use of agent based models for egress simulations. He is also interested in visualization and has developed new techniques for applying virtual reality in the field of finite element simulations and the use of augmented reality for rapid assessment of infrastructure damage in the wake of disasters.