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Purdue researchers won NSF early-career awards in 2012

September 5, 2013


WEST LAFAYETTE, Ind. – Eleven Purdue University faculty members won 2012 Faculty Early Career Development awards from the National Science Foundation, one of the most prestigious NSF honors for outstanding young researchers.

The NSF issues about 400 Early Career awards annually. Purdue's 2012 recipients were Suzanne Bart, Guang Cheng, José Figueroa-López, Jeffrey Karpicke, Milind Kulkarni, Peijun Li, Jennifer Neville, Senay Purzer, Xiulin Ruan, Xavier Tricoche and Adam Wasserman.

Details about the Purdue awardees and their research follow:

Uranium Complexities

Bart, an assistant professor of chemistry, will focus on the synthesis, characterization and reactivity of reduced uranium complexes for small-molecule activation. She will study the use of depleted uranium as a catalyst for common reactions needed for the synthesis of many materials.  Depleted uranium is cheaper and more abundant than the transition metals, including rhodium, palladium and platinum, commonly used for such synthesis. She also will expand her initiative to increase the population of women among college-age science majors with her ChemTeens conference (formerly High School Day on Campus for Girls).


Cheng, an associate professor of statistics, is investigating two classes of bootstrap methods in semi-nonparametric models for quicker and more efficient analysis of massive data sets. Bootstrap methods can be used in classification analysis, like the programs used to determine what movie a customer may like based on previous selections. Cheng will evaluate under what circumstances these statistical methods can be accurately applied and investigate ways to adjust the methods for use with different datasets. He also will work to create a "statistical learning with stability control" method that combines the semi-parametric statistical model with computer science's machine-learning method. "Stable statistical learning" could be used for datasets with many variables and be applied to personalized medicine.

Lévy Models

Figueroa-López, an associate professor of statistics, will create new mathematical models and methods to analyze massive and rapidly recorded data sets like those that come from the thousands of transactions made each second in the stock market. The dramatic increase of data has led to more "errors" and noise unaccounted for in existing models of these systems. He will work to bridge high-frequency data analysis and continuous-time features of Lévy-type models. Figueroa-López also will use the grant to open research positions for undergraduate students.

Learning Through Testing

Karpicke, an associate professor and the James V. Bradley Chair of Psychological Sciences, will use his grant to study how retrieval practice, which are forms of self-testing, helps undergraduate students learn biology. His previous research shows that students who use retrieval practice retain the information longer and learn better, compared to students who reread or review study notes.

High-Performance Computing

Kulkarni, an assistant professor of electrical and computer engineering, will use his grant to develop automated techniques that improve the performance of "irregular programs" needed for demanding jobs, such as complex scientific computations or business analytics. The programs transform the relatively simple code of user applications into the machine code needed for complex computer systems. They are used for a range of applications, from modeling climate to analyzing consumer behavior and finding information about Facebook friends.

Wave Propagation

 Li, an associate professor of mathematics, will develop mathematical models, examine mathematical issues and design computational methods for problems that arise from acoustic and electromagnetic wave propagation in complex and random environments. Such models and methods will benefit and improve the various applications of the underlying inverse scattering problems such as locating oil and gas underground and other geophysical exploration; tumor detection and biomedical imaging; and military radar and sonar detection and evasion. Li also will develop a sequence of courses to introduce students to this field and support the Center for Computational Mathematics, which provides a platform to discuss future directions of research in this area.

Network Analysis

Neville, an associate professor of computer science and statistics, will study the mechanisms that influence the accuracy of machine-learning methods for large-scale networks, including social networks like Facebook and physical networks like the Internet. She will use this understanding to develop predictive models of network behavior and analysis methods to evaluate the effectiveness of different algorithms. Neville also will create methods to determine the significance of network patterns discovered by the algorithms and models. Data from social networks will be used for this work, but the research has applications in a wide range of domains including psychology, communications, education and political science.

Innovative Thinking

Purzer, an assistant professor of engineering education, will use her grant in research aimed at developing ways to measure the innovation skills of engineering students. Assessment and teaching tools developed in the work may be used to design engineering degree programs that produce more innovative graduates. The research has a potentially broad impact on the U.S. workforce through the wide distribution of its findings to education scholars as well as academic decision-makers.

New Materials

Ruan, an assistant professor of mechanical engineering, will use his grant for research aimed at developing advanced simulation tools to predict the behavior of materials that have never been synthesized before. Such an advance could accelerate the commercialization of new materials for applications ranging from solar energy to homeland security, electronics to medicine.

Visual Analysis

Tricoche, an assistant professor of computer science, will pioneer a comprehensive approach for the efficient visual analysis of large-scale data sets in the context of multidisciplinary collaborations spanning fluid dynamics, materials engineering, high-energy physics and cardiovascular research. He will apply new mathematical theories to model the behavior of various systems, identify what information is significant or could be of interest, and present it in an intuitive geometric visual display. Tricoche will make the software he creates publicly available and will offer workshops to promote a collaborative effort in the visualization community. He also will create new undergraduate and graduate courses to expose students to the importance of data analysis in science and engineering and the role of advanced visualization.

Quantum Chemistry

Wasserman, an assistant professor of chemistry, will develop methods to calculate the properties of molecules that extend the reach of quantum-chemical approaches based on Density Functional Theory. His approach allows for the calculation of ground and excited electronic states of molecules including metastable ones. Wasserman also has created a concept for calculating the properties of fragments of molecules and electron charge, called the partition potential. The concept overcomes problems presented by molecules undergoing bond stretching, which occurs just before bonds are created or broken and is important to understanding chemical reactions. These calculations will help in the design of materials and synthesis of new molecules. Wasserman also will work to increase the involvement of Hispanic minorities and organize scientific events that promote Hispanic participation in science research.

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