Eric Michielssen

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Eric Michielssen, PhD, is Professor of Electrical Engineering and Computer Science, Director of the Michigan Institute for Computational Discovery and Engineering, and Associate Vice President for Advanced Research Computing. His research interests include all aspects of theoretical, applied, and computational electromagnetics, with emphasis on the development of fast (primarily) integral-equation-based techniques for analyzing electromagnetic phenomena. His group studies fast multipole methods for analyzing static and high frequency electronic and optical devices, fast direct solvers for scattering analysis, and butterfly algorithms for compressing matrices that arise in the integral equation solution of large-scale electromagnetic problems. Furthermore, the group works on plane-wave-time-domain algorithms that extend fast multipole concepts to the time domain, and develop time-domain versions of pre-corrected FFT/adaptive integral methods.  Collectively, these algorithms allow the integral equation analysis of time-harmonic and transient electromagnetic phenomena in large-scale linear and nonlinear surface scatterers, antennas, and circuits.  Recently, the group developed powerful Calderon multiplicative preconditioners for accelerating time domain integral equation solvers applied to the analysis of multiscale phenomena, and used the above analysis techniques to develop new closed-loop and multi-objective optimization tools for synthesizing electromagnetic devices, as well as to assist in uncertainty quantification studies relating to electromagnetic compatibility and bioelectromagnetic problems.

Emanuel Gull

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Professor Gull works in the general area of computational condensed matter physics with a focus on the study of correlated electronic systems in and out of equilibrium. He is an expert on Monte Carlo methods for quantum systems and one of the developers of the diagrammatic ‘continuous-time’ quantum Monte Carlo methods. His recent work includes the study of the Hubbard model using large cluster dynamical mean field methods, the development of vertex function methods for optical (Raman and optical conductivity) probes, and the development of bold line diagrammatic algorithms for quantum impurities out of equilibrium. Professor Gull is involved in the development of open source computer programs for strongly correlated systems.

Quantum impurities are small confined quantum systems coupled to wide leads. An externally applied time-dependent magnetic field induces a change in the population of spins on the impurity, leading to time-dependent switching behavior. The system's equations of motion are determined by a many-body quantum field theory and solved using a diagrammatic Monte Carlo approach. The computations were performed at Columbia University and the University of Michigan.

Quantum impurities are small confined quantum systems coupled to wide leads. An externally applied time-dependent magnetic field induces a change in the population of spins on the impurity, leading to time-dependent switching behavior. The system’s equations of motion are determined by a many-body quantum field theory and solved using a diagrammatic Monte Carlo approach. The computations were performed at Columbia University and the University of Michigan.

Issam El Naqa

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Our lab’s research interests are in the areas of oncology bioinformatics, multimodality image analysis, and treatment outcome modeling. We operate at the interface of physics, biology, and engineering with the primary motivation to design and develop novel approaches to unravel cancer patients’ response to chemoradiotherapy treatment by integrating physical, biological, and imaging information into advanced mathematical models using combined top-bottom and bottom-top approaches that apply techniques of machine learning and complex systems analysis to first principles and evaluating their performance in clinical and preclinical data. These models could be then used to personalize cancer patients’ chemoradiotherapy treatment based on predicted benefit/risk and help understand the underlying biological response to disease. These research interests are divided into the following themes:

  • Bioinformatics: design and develop large-scale datamining methods and software tools to identify robust biomarkers (-omics) of chemoradiotherapy treatment outcomes from clinical and preclinical data.
  • Multimodality image-guided targeting and adaptive radiotherapy: design and develop hardware tools and software algorithms for multimodality image analysis and understanding, feature extraction for outcome prediction (radiomics), real-time treatment optimization and targeting.
  • Radiobiology: design and develop predictive models of tumor and normal tissue response to radiotherapy. Investigate the application of these methods to develop therapeutic interventions for protection of normal tissue toxicities.
Machine Learning in Radiation Oncology: Theory and Applications

Machine Learning in Radiation Oncology: Theory and Applications

William Currie

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William Currie, PhD, is Professor in the School of Environment and Sustainability at the University of Michigan, Ann Arbor. Prof. Currie is interested in interdisciplinary approaches to the study of the environment and the development of sustainability science. His research and scholarly interests include ecosystem ecology, biogeochemistry including carbon and nutrient cycling, physics and energetics, landscapes and coupled human-natural systems, land conservation and management, biofuels and food security, computational modeling and simulation, synthesis using models, and philosophical foundations of modeling.

Prof. Currie has a background in ecosystem ecology, biogeochemistry (nutrient and carbon cycling), energetics, systems dynamics modeling and individual-based / agent-based modeling. He is interested in using our current understanding in these fields to investigate ecosystem change and dynamics in coupled human-environment systems.

Prof. Currie studies the linkages among carbon, nutrient, and water cycling and energy flows and transformations in terrestrial ecosystems and human-environment systems.  He is interested in using our current understanding of ecosystems to explore creative, new understanding of the two-way interactions in human-environment systems.  He works at scales from field plots to landscapes, collaborating with other researchers and students to integrate understanding and build models for synthesis.  The goal of this research is to contribute to the developing field of sustainability science using an approach that grows out of ecosystem science.