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Dr. Cranos Williams

Drs. Cranos Williams (PI), Terri Long (CoPI), James Tuck (CoPI), and Joel Ducoste (CoPI) have been awarded $999,754 by the National Science Foundation for research on Dynamic Regulatory Modeling of the Iron Deficiency Response in Arabidopsis thaliana.

The award will run from August 15th, 2012 to July 31st, 2017.

Research Abstract

Multicellular organisms such as plants react to abiotic stress with a multitude of physiological and molecular responses orchestrated by key regulatory proteins, or transcription factors. Experimental datasets, such as transcriptional profiles, are often used to identify critical, yet, uncharacterized transcription factors in these responses.  Limitations in these datasets caused by constraints in experimental perturbations and finite experimental resources are the reasons why traditional approaches have revealed few key regulating and controlling elements, particularly in model organisms such as Arabidopsis thaliana.  The PIs hypothesize that additional computer-based simulations from dynamic gene regulatory models can be used in combination with clustering approaches to expand the perturbation space and assess secondary and tertiary control mechanisms, leading to the identification of hidden regulatory relationships between genes and transcription factors. They propose to develop a novel modeling and parallel computing paradigm to identify previously uncharacterized regulatory components that control iron homeostasis in A. thaliana across multiple cell types.

The interdisciplinary approach proposed by these PIs presents a new paradigm that 1) unifies novel genomic experimental techniques, engineering modeling approaches, and parallel computing to clarify the role of known regulatory elements and 2) identifies new regulating components involved in iron homeostasis within and across different cell types. Their integration of systems engineering, plant biology, and computer engineering will help create new solutions to existing problems and encourages a vision for addressing challenging issues that have, to date, remained intimidating using traditional approaches. Their results will lead to methods for stretching critical resources and increasing crop yields to feed the projected 9 billion people in 2050 through development of plants that exhibit improved function in low nutrient soils, or plants that can contain elevated nutrient content.

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Dr. John Muth

Dr. Tania Paskova and Dr. John Muth have been awarded $383,532 by the National Science Foundation for research on III-Nitride LED Structures on Sidewall Grown Semipolar Facets.

The award will run from July 1st, 2012 to June 30th, 2015.

Research Abstract

The proposed research addresses a long-standing issue of growing importance to the nitride-based optoelectronic technology, namely the internal quantum efficiency of nitride emitters in green-yellow region, and how the nonpolar/semipolar alignment of the active device regions can help to improve the device performance.  An in-depth investigation will be undertaken to gain a comprehensive understanding of the basic properties of semipolar GaN/InGaN LED structures produced by lateral sidewall growth. The dominating growth mechanisms, the defect formation and evolution, the In and doping element incorporation efficiency, and the strain in structures with different semipolar orientations will be studied, aiming to establish the best approach for producing low-defect-density semipolar LED structures with enhanced internal quantum efficiency.

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Dr. Dror Baron

Dr. Dror Baron has been awarded $422,732 by the National Science Foundation for research on CIF: Small: Universal Signal Estimation from Noisy Measurements.

The award will run from September 1st, 2012 to August 31st, 2015.

Research Abstract

Motivation: A ubiquitous feature in many signal processing systems is to learn the input statistics from historical data. In these systems, Bayesian methods perform statistically optimal signal processing. However, there are applications including file compression, speech recognition, network monitoring, and compressed sensing in which it might be impractical to learn the statistics a priori. In such applications, a statistical approach that adapts to the data at hand must be used.
The information theory community has championed the use of universal algorithms, they achieve the best possible statistical performance asymptotically despite not knowing the input statistics.
These algorithms have had tremendous impact in lossless compression, where the goal is to describe data as succinctly as possible while allowing a decoder to reproduce the input perfectly. In sharp contrast, universal algorithms have had little impact on other areas.

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Dr. Eric Rotenberg

Dr. Eric Rotenberg has been awarded $350,000 by the National Science Foundation for research on SHF: Small: Design for Competitive Automated Layout (DCAL) of Mobile Application Processor.

The award will run from August 1st, 2012 to July 31st, 2015.

Research Abstract

For two decades, personal computers and servers have been powered by increasingly sophisticated superscalar processors. The last few years has even witnessed the introduction of superscalar processors into smart phones and tablet PCs, in order to provide richer user experiences. There are important trends in both domains: server-class processors require unsustainable design effort, as evidenced by a select few, highly trained, large design teams in industry proliferating superscalar processors; mobile devices are evolving at an extraordinary pace. These trends suggest it is time to take a radical departure in the way superscalar processors are designed. In particular, the PI proposes superscalar processor design automation. This project explores challenges and solutions at key levels:

Automatic FPGA-based processor-in-system exploration Efficient and automatic ISA/microarchitecture decoupling Automatic RTL generation via a superscalar design language A low-effort physical design strategy and alternative to custom design.

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