ISIS professors Gautam Biswas and Aniruddha Gokhale receive NSF Awards as a Result of Stimulus Plan

Two ISIS professors have received NSF awards in the area of Intelligent Learning Environments and Distributed Real-time Embedded Systems.

In response to our nation’s deep economic crisis, President Obama signed into legislation the American Recovery and Reinvestment Act of 2009 on February 17th.

The Recovery Act is a nation-wide stimulus program aimed at revitalizing and expanding the economy with approximately $3Billion invested into programming that maintains and/or creates employment opportunities, while moving us into the future in areas such as health care, energy independence, the environment -- as well as educational opportunities and development.

As such, The Institute for Software Integrated Systems would like to announce two new National Science Foundation (NSF) awards received as a result of The Recovery Act stimulus:

Formal Analysis of Choice-Adaptive Intelligent Learning Environments (FACILE) that support Future Learning
Principle Investigator: Professor Gautam Biswas

Currently, educators often make use of computer technology to aid and enhance the educational experience. Advanced technology has developed rich open-ended learning environments that use a number of different learning paradigms and resources. For example, students can complete quests in game environments, engage in inquiry, interact with virtual agents, run science simulations, take quizzes, access the web, and more generally make choices about different learning activities. The environment can then adapt intelligently by encouraging (alternative) choices.

This project continues with this endeavor to expand the technological-educational impact by developing computer environments that permit student choice when learning, and design feedback that provides metacognitive support to the student for making better choices. In this way, we can help students learn in the computer environment and transfer this learning towards making choices in real learning situations. Studies will be run in a number of middle school science classrooms in Metro Nashville to demonstrate the effectiveness of the system.

The project is committed to serving minorities and under-represented students in middle school science classrooms as well as graduate students working on their Ph.D. research.

SHF: Small: Automating the Deployment of Distributed Real-time and Embedded System Software using Hybrid Heuristics-based Search Techniques
Principle Investigator: Professor Aniruddha Gokhale

Modern automobiles and flight avionics systems form complex distributed real-time and embedded (DRE) systems. A high-end luxury car can have over 80 Electronic Control Units (ECUs), which are small embedded processors, and multiple networks linking the processors. Furthermore, several hundred software components can be distributed across these multiple networked ECUs. Optimizing the deployment of these software components, by packing the software more tightly onto the processors, can reduce the size of the required underlying infrastructure and have numerous positive side-effects, such as weight and power consumption savings.

Determining how to deploy software to hardware in DRE systems is a challenging problem due to the large number of complex constraints that must be dealt with, such as real-time scheduling constraints, component placement restrictions, and fault-tolerance guarantees. This research effort focuses on developing new hybrid heuristic and meta-heuristic techniques for determining how to deploy software to computational nodes. The algorithms and tools will be made available in open source through the Generic Eclipse Modeling System (, which is distributed by 45 world-wide mirrors, and the ESCHER tool repository. Opportunities for outreach will be sought through existing mechanisms in place at Vanderbilt University, such as the NSF Science and Technology Center called TRUST and Vanderbilt Center for Science Outreach (CSO) to host summer research students. As such, we continue to support graduate students belonging to underrepresented groups.