IIT Faculty Members Win Four NSF CAREER Awards

Illinois Institute of Technology (IIT) is pleased to announce that four faculty members, Assistant Professor of Computer Science Ioan Raicu, Electrical and Computer Engineering Assistant Professors Yu Cheng and Kui Ren, and Assistant Professor of Mechanical Engineering Benxin Wu, have been awarded National Science Foundation (NSF) Faculty Early Career Development (CAREER) Awards for 2011. IIT junior faculty members have received 14 CAREER Awards since 1995.

The CAREER Award, one of NSF’s most prestigious honors, supports early career-development activities of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research within the context of the mission of their organizations. Such activities should build a firm foundation for a lifetime of leadership in integrating education and research.

Raicu was recognized for his proposal “Avoiding Achilles’ Heel in Exascale Computing with Distributed File Systems.” The award will provide five years of support and more than $450,000 for Raicu’s groundbreaking research, which focuses on distributed storage systems at exascales, systems that can operate at 1018 operations per second. Currently, the fastest supercomputers operate at petascales (1015 operations/second), while a modern workstations function at hundreds of gigascales (1011 operations/second). Computing systems performing one quintillion operations per second will help unravel significant scientific mysteries in domains such as weather modeling, national security, energy and drug discovery. It is predicted that exascales in computing will be reached in 2019 by connecting millions of nodes that each perform computations. The goal of Raicu's research is to find ways to distribute storage across these nodes in a way that will support the exponential growth in concurrency and scale expected over the next decade. These advancements will impact scientific discovery and economic development at the national level, and strengthen a wide range of research activities enabling efficient access, processing, storage, and sharing of valuable scientific data from many disciplines including medicine, astronomy, bioinformatics, chemistry, aeronautics, analytics, economics, and new emerging computational areas in the humanities, arts, and education.

Cheng’s project, “Exploring the Underexplored: A Fundamental Study of Optimal Resource Allocation and Low-Complexity Algorithms in Multi-Radio Multi-Channel Wireless Networks,” received five years of support and $80,000 for research focusing on the advancement of multi-radio multi-channel (MR-MC) networking. MR-MC networking provides a generic computing platform for a wide range of next-generation wireless networks. However, the capacity of MR-MC networking has been underexplored and the current state of the art of MR-MC network capacity analysis is limited to heuristic algorithms and loose capacity bound analysis. Cheng’s research targets breakthrough studies on optimal resource allocation and low-complexity algorithm development for MR-MC networks. His proposal weaves together optimization theory, graph theory, stochastic control, and asymptotic scaling law analysis to reveal the factors that distinguish the MR-MC network optimization from the single-radio single-channel (SR-SC) counterpart, develop low-complexity algorithms with theoretically provable performance, and exploit the particular advantages of MR-MC networking in incorporating new techniques for further capacity enhancement. The project will develop a set of novel theoretical tools to address the capacity optimization and computational complexity in a multi-dimensional resource space. The capacity planning and dynamic network control techniques developed through Cheng’s research are of critical importance to practical wireless network design and have the potential to be transformed into practical network protocols with low complexity and provable efficiency.

Ren’s proposal, “Secure and Privacy-assured Data Service Outsourcing in Cloud Computing,” which received one year of support and more than $100,000, explores secure and privacy-assured data service outsourcing mechanisms that are usable, scalable, and able to meet performance goals. Cloud computing refers to the vision of computing as a utility, which treats information technology services as a commodity with great efficiency, scalability, and minimal management cost. It creates a fundamental shift in how data services are deployed and delivered, enabling flexible and dynamic service outsourcing while reducing capital cost commitments for hardware, software, and operational overhead. Ren’s research focuses on deploying the most fundamental data services, including data utilization, data sharing, and data storage on the commercial public cloud, and investigates how encrypted cloud data can effectively be utilized or searched with strong privacy assurance when high service-level performance is simultaneously demanded by large numbers of data users and files, how data owners can reliably and efficiently enforce the dissemination of sensitive cloud data among large number of users in a fine-grained and scalable way when the data no longer locally reside within the owners’ trusted domain, and how a privacy-preserving cloud storage auditing mechanism can be enabled such that it maintains a strong guarantee of outsourced storage correctness on behalf of the data owners while not compromising the owners’ data privacy. Moving toward secure and privacy-assured data service outsourcing is fundamental to the success of cloud computing deployment. Ren’s project aims to satisfy this critical need and is expected to have a high impact on the successful deployment of cloud computing in practice.

Wu’s project, “Fundamental Research on a Novel Ultrasound-assisted Water-confined Laser Micromachining Technology,” was awarded five years of support and more than $400,000. He proposes a novel ultrasound-assisted, water-confined laser micromachining (UWLM) process, which combines laser micromachining with in-situ ultrasound in water to help better understand the coupled laser and ultrasound-material interactions in UWLM and potentially avoid or greatly reduce the defects generated by the current laser micromachining process. Laser micromachining has many important current and potential applications in the medical field, electronics, automotives, aerospace, and other areas. It is used for completing tasks including cutting medical stents, drilling cooling holes for aerospace engines, and surface texturing mechanical parts for friction reduction and energy savings. The knowledge gained from Wu’s research has the potential to improve product quality, increase manufacturing efficiency, and reduce cost, all of which will have a broad impact on the increasing number of fields that have a rapidly growing need for micromachining.

• "Fundamental Research on a Novel Ultrasound-assisted Water-confined Laser Micromachining Technology"
  www.nsf.gov/awardsearch/showAward.do?AwardNumber=1055805
• "Avoiding Achilles' Heel in Exascale Computing with Distributed File Systems"
  www.nsf.gov/awardsearch/showAward.do?AwardNumber=1054974
• "Exploring the Underexplored: A Fundamental Study of Optimal Resource Allocation and Low-Complexity Algorithms in Multi-Radio Multi-Channel Wireless Networks"
• www.nsf.gov/awardsearch/showAward.do?AwardNumber=1053777
• "Secure and Privacy-assured Data Service Outsourcing in Cloud Computing"
  www.nsf.gov/awardsearch/showAward.do?AwardNumber=1054317

More information about the NSF CAREER Awards is available at www.nsf.gov.

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