Two researchers at the U.S. Department of Energy’s Argonne National Laboratory’s Mathematics and Computer Sciences Division have received 2012 Early Career Research Program awards, granted to exceptional researchers beginning their careers.
The two are among 68 selectees announced by the DOE’s Office of Science, and were chosen based on peer review from about 850 nominations submitted last November. The selectees for this year are from 47 different institutions in 25 states.
The winners from MCS are:
- Pavan Balaji, computer scientist, Mathematics and Computer Science Division
- Victor Zavala, assistant computational mathematician, Mathematics and Computer Science Division
“Argonne is extremely proud that four of our young researchers have been chosen for this important award,” said Eric D. Isaacs, director, Argonne National Laboratory. “These young scientists and engineers will play a vital role in our nation’s future, helping to assure that invention and innovation continue to fuel America’s global competitiveness in the years to come.”
Balaji has made outstanding contributions to data movement and runtime systems for parallel programming models on very large computing systems. His research will focus on efficient communication systems for supercomputers equipped with complex computational and memory hierarchies, including heavily hierarchical processing units and complex heterogeneous architectures such as integrated or discrete accelerators.
Balaji received his Ph.D. in computer science and engineering from Ohio State University in 2006. He has published almost 100 peer-reviewed articles in journals and conference proceedings and has given nearly 120 invited presentations and invited tutorials. He has been co-editor of several journal special issues.
Zavala will develop extreme-scale optimization solvers to guide the design and real-time coordination of national infrastructure systems (for example, electricity, natural gas, water, transportation) and to understand the physical flexibility of these systems, a key feature in determining the likelihood and economic impact of cascading shortages. He will also seek to enable scaling on high-performance computing architectures by exploiting physical characterizations of optimization and uncertainty domains.
Zavala received his Ph.D. in chemical engineering from Carnegie Mellon University in 2008, with an emphasis in mathematical modeling and numerical optimization. At Argonne, he investigates impacts of stochastic optimization and weather forecasting on the national power grid and develops real-time optimization algorithms to enable the deployment of predictive models at fast time scales. He also leads collaborative projects with building energy management companies to create and test new automation architectures that integrate predictive control, machine learning models, and emerging sensor technologies to maximize energy efficiency and occupant comfort.