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2012 - Akhil Langer

The future brings uncertainty.  Predicting what will happen in a given situation—or multiple situations—is often difficult, especially when there is a wide margin for human and natural error.  This is particularly challenging in financial decision-making, such as investment or environmental planning.

To reduce this error, Akhil Langer is developing a parallel computing framework to simplify the decision-making process by incorporating future uncertainty to make decisions that, for example, maximize profits and minimize expenses.  Problems arise when attempting large-scale decision-making on a single computer, because there are too many possible scenarios to process.  This is why Akhil is working on parallel solutions that can exploit tens of thousands of computers to solve large optimization problems.

One application is the problem of aircraft allocation for the U.S. Department of Defense.  The Department of Defense must allocate 1,300 U.S. military aircrafts on different missions—a task it presently assigns manually.  Akhil’s goal is to optimize aircraft allocation using parallel computing on some of the most powerful supercomputers.

As an undergraduate, Akhil previously worked on a project that involved the distribution of health-related information to users through their cell phones.  Users could ask questions, such as how to locate a doctor, or request information on a certain drug via informal text messages.  Akhil's software would then analyze their queries and return appropriate data from a health database to answer their questions.

Akhil received his undergraduate degree in Computer Science from the Indian Institute of Technology Roorkee and holds a Master's degree in Computer Science from the University of Illinois at Urbana-Champaign.