Portrait of Monica Valluri

Monica Valluri

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Her research is based on the theoretical framework of Galactic Dynamics. Two profoundly mysterious unseen components of galaxies are central supermassive black holes and dark matter halos (massive, invisible halos of matter whose presence is inferred only from their gravitational effects on visible objects like stars.) Dr. Valluri uses galactic dynamics to interpret and model motions of stars observed with state-of-the-art telescopes using new and powerful numerical methods. Her work has led to important insights into how these dark components influence the structure and evolution of galaxies. Some of the topics she is currently working on include:

  • accurately measuring the masses of supermassive black holes in any type of  galaxy, their effects on their host galaxies, and their role in galaxy evolution;
  • understanding the orbital structure of stellar bars in spiral galaxies and their interactions with supermassive black holes
  • the properties (such as space and velocity distribution) of the mysterious “dark matter” that constitutes most of the mass in the Universe;
  • understanding the dynamical structure of the Milky Way Galaxy from the properties of tidal streams, and the orbits of stars in the Milky Way’s halo;
  • the role of non-linear dynamical processes (e.g. chaos and dynamical relaxation) in sculpting galaxies.

Mariana Carrasco-Teja

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Dr. Mariana Carrasco-Teja received her PhD from the Mathematics Department at the University of British Columbia (UBC) (Vancouver, BC). She was part of the Institute of Applied Mathematics (UBC), an institute established to enhance interdisciplinary teaching and research using applied mathematics as a common language between engineers and scientists.  Her dissertation involved modeling and simulating the primary cementing of oil and gas wells, a crucial step to ensure a safe and efficient extraction of oil and gas.  After receiving her PhD, she continued her work as a postdoctoral fellow at the Complex Fluids Laboratory in UBC until she moved to Ann Arbor to join the Department of Chemical Engineering at the University of Michigan. Since becoming a member of the Cell Adhesion and Drug Delivery Laboratory, she’s had a chance to work closely with bioengineers while applying her modeling skills into optimizing vascular-targeted drug micro- and nano-carriers.

She was named MICDE Assistant Director in July 2015, and MICDE Associate Director in September 2019.

Siqian Shen

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Prof. Shen’s research derives multifaceted mathematical optimization models for decision making under data uncertainty and information ambiguity. The models she considers often feature stochastic parameters and discrete (0-1) decision variables. The goal is to seek optimal solutions for balancing risk and cost objectives associated with complex systems. She also develops efficient algorithms for solving the large-scale optimization models, based on integer programming, stochastic and data-driven approaches, and special network topologies. In particular, her research has been applied to cyberinfrastructure design and operations management problems related to power grids, transportation, and Cloud Computing systems.

A sensor monitored network for research allocation and routing in highly uncertain environments (e.g., post-disaster delivery, highly congested traffic system, or high-demand computing network). The network is structured by solving a general mathematical optimization model.

A sensor monitored network for research allocation and routing in highly uncertain environments (e.g., post-disaster delivery, highly congested traffic system, or high-demand computing network). The network is structured by solving a general mathematical optimization model.

Karthik Duraisamy

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Prof. Duraisamy is interested in the development of computational models, algorithms and uncertainty quantification approaches with application to fluid flows. This research includes fluid dynamic modeling at a fundamental level as well as in an integrated system-level setting. An overarching theme in his research involves the use of simulation and data-driven methods to answer scientific and engineering questions with an appreciation of the effect of modeling uncertainties on the predicted results. Prof. Duraisamy’s group is also interested in developing numerical algorithms to  operate on evolving computational architectures such as GPUs. He is the Director of the Center for Data-Driven Computational Physics.

Combustion in a turbulent boundary layer.