13 Purdue researchers earn NSF Early Career recognition
WEST LAFAYETTE, Ind. — Thirteen Purdue University assistant and associate professors received National Science Foundation Faculty Early Career Development (CAREER) Awards between April 2023 and the end of July 2024 to fund their research.
CAREER awards recognize faculty who have the potential to serve as academic role models in research and education and to lead advances in the mission of their department or organization. The five-year grants are NSF’s most prestigious award in support of early career faculty.
NSF announces CAREER awards throughout the year. Faculty listed below received awards with an NSF project start date between April 1, 2023, and July 31, 2024. (Some faculty within that range were included in an April 2023 story on CAREER award recipients.) Faculty awards within that date range are:
Leifu Chang, associate professor of biological sciences (College of Science), for a project titled “Probing Structural Dynamics and Regulatory Mechanisms of RNA-Guided CRISPR-Cas12 Endonucleases and Their Analogues.” Chang will use the award to explore the working mechanism of DNA-editing CRISPR-Cas12 endonucleases and their analogues, which could facilitate their development as improved genome editing tools. His work will address two critical knowledge gaps in the CRISPR field by unraveling the overlooked regulatory interplay among coexisting anti-CRISPR proteins and elucidating the structure and mechanisms of emerging yet underexplored Cas12 analogues.
Yiheng Feng, assistant professor of civil engineering (College of Engineering), for a project titled “Securing Next-Generation Transportation Infrastructure: A Traffic Engineering Perspective.” Feng will use the award to establish an integrated research and education plan to investigate the cybersecurity problem in next-generation transportation infrastructure. The project will explore the impact of cyberattacks on safety and mobility and develop corresponding mitigation strategies to safeguard the transportation system.
Jonathan Hood, assistant professor of chemistry (College of Science), for a project titled “Ultracold Molecules Assembled in a Tweezer Array for Quantum Simulation.” Hood will use the award to build a quantum simulator from an array of laser-trapped and cooled ultracold molecules. In contrast to atom-based simulators, the molecules introduce new types of interactions, for example, the long-range and anisotropic interaction from molecular dipole moments.
David Johnson, the Ravi and Eleanor Talwar Rising Star Associate Professor of Industrial Engineering (College of Engineering) and associate professor of political science (College of Liberal Arts), for a project titled “Risk-Based Methods for Robust, Adaptive, and Equitable Flood Risk Management in a Changing Climate.” Johnson will use the award to leverage unique datasets and state-of-the-art modeling capabilities to improve decision-makers’ ability to manage risk from extreme events by better quantifying natural hazards risks and identifying risk-informed, adaptive and equitable management strategies.
Dominic Kao, assistant professor of computer information technology (Purdue Polytechnic Institute), for a project titled “Broadening Participation in Computing Through Virtual Identities.” Kao will use the award to create computational interventions and virtual representations that reduce stereotype threat — a psychological state where individuals feel at risk of confirming negative stereotypes related to their social group — in online educational settings, thereby contributing to the development of a diverse and globally competitive workforce.
Franki Kung, associate professor of psychological sciences (College of Health and Human Sciences), for a project titled “Mechanistic Dehumanization of Asians: Identifying Causes, Consequences, and Countermeasures for a More Inclusive STEM Workforce.” Kung will use the award to study the phenomenon of “mechanistic dehumanization” — a bias that reduces people’s humanity through viewing or treating them as like machines or robots — that affects especially Asian Americans and Asians in the U.S. in the context of workplaces and educational settings.
Anuran Makur, assistant professor of computer science and electrical and computer engineering (College of Science), for a project titled “Information Propagation Over Networks.” Makur will use the award to investigate how information originating from certain parts of a network dissipates over time as it flows through the remainder of the network, by developing a general theory of information propagation over networks, which would in turn provide insights in several other application domains.
Romila Pradhan, assistant professor of computer information technology (Purdue Polytechnic Institute), for a project titled “Data Preparation for Trusted and Fair Data Science.” Pradhan will use the award to develop novel technologies to realize the potential of robust, fair and explainable data-driven decision-making systems. Pradhan hopes the project will advance understanding in the field of responsible data science, particularly by highlighting the impact of data quality issues and data preparation steps on the robustness and fairness of data science pipelines.
Wenhai Sun, assistant professor of computer information technology (Purdue Polytechnic Institute), for a project titled “Towards Machine-Learnable Enhancing Framework for Local Differential Privacy.” Sun will use the award to develop a novel AI-assisted privacy-enhancing framework to better address the tension between privacy, security and utility objectives. Generalizable theories and principles will be produced to guide the developed intelligent machine agent to sense the deployment environment and learn optimal responses to the observed adversarial activities and expected utility and privacy goals while offering algorithmic transparency and accountability through human-on-the-loop self-explainable AI.
Tyler Tallman, associate professor of aeronautics and astronautics (College of Engineering), for a project titled “Inverse Mechanics in Self-Sensing Materials: Basic Knowledge, Education, and Service.” Tallman will use the award in research on inverting electro-mechanical coupling in self-sensing materials. Mechanical self-sensing concepts have been widely studied in diverse applications including robotics, structural sensing, health care and rehabilitation. By inverting the relationship between electrical changes and mechanical loading in these materials, it is possible to know the full-field mechanics from only a small number of electrical measurements, which will lead to more accurate, more robust and faster solutions.
Jianguo Wang, assistant professor of computer science (College of Science), for a project titled “The Case for Disaggregated Database Systems.” Wang will use the award to build a new database system for the cloud that is specifically optimized for resource disaggregation to substantially improve performance, scalability and elasticity. This will, in turn, significantly reduce costs for database customers and yield substantial economic benefits for society in mission-critical applications, such as finance, e-commerce and transportation.
Ming Yin, assistant professor of computer science (College of Science), for a project titled “Redesigning the Human-AI Interaction Paradigm for Improving AI-Assisted Decision Making.” Yin will use the award to create radical new designs for human-AI interaction in which is engagement-oriented to improve human-AI collaborations in decision-making. The project will improve collaborative decision-making performance in domains where AI-based decision aids are used to support people such as finance, law enforcement, education, and cybersecurity.
Tianyi Zhang, assistant professor of computer science (College of Science), for a project titled “Regularizing Large Language Models for Safe and Reliable Program Generation.” Zhang will use the award to advance the understanding of the limitations of large language models (LLMs) in program generation and develop principled regulation approaches to enhance the correctness, safety and robustness of LLM-generated code. The project addresses weaknesses in the use of LLMs, such as ChatGPT, in generating complex programs from natural language, which eliminate the need to memorize and grapple with program syntax and semantics and expand access to programming.
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Media contact: Mary Martialay, mmartial@purdue.edu