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Six faculty win NSF CAREER awards

Six early-career Cornell faculty members are recent recipients of National Science Foundation Faculty Early Career Development Awards, which support career development activities, as well as education and outreach initiatives, of teacher-scholars.

Ricardo Daziano, assistant professor of civil and environmental engineering and a David Croll Fellow, was awarded for his proposal, “Advanced demand estimators for energy efficiency in personal transportation.” His five-year, $410,000 award will support research and education efforts in decision-making analysis to better understand consumer behavioral response to energy-efficient engineered technologies. The main research outcome will be a solution for the joint estimation problem of a complex system of structural equations based on random utility maximization that can be applied to formulate demand models for energy efficiency.

John “Nate” Foster, assistant professor of computer science, received a five-year, $532,000 award for his proposal, “Principles and practice of distributed updates.” The goal of Foster’s project is to develop foundational principles and practical techniques for managing software updates in distributed systems. A central theme will be using bidirectional transformations to bridge the gap between different versions of a program, thereby allowing multiple versions to interoperate seamlessly.

Peter Frazier, assistant professor of operations research and information engineering, received a five-year, $400,000 grant for his proposal, “Methodology for optimization via simulation: Bayesian methods, frequentist guarantees, and applications to cardiovascular medicine.” His research objective is to develop improved methods for optimization via simulation, and to apply them to decision-making problems in cardiovascular medicine. Optimization via simulation is required for evaluating the quality of an option and requires running a stochastic simulation. Two types of improved methods will be developed. First, methods for ranking and selection will be developed using a link between Bayesian average-case and frequentist worst-case performance. Second, multistart gradient-based methods will be developed using Bayesian value of information analysis to efficiently allocate simulation effort across starts, allowing those starts more likely to have high-quality local optima to converge first.

Gregory Fuchs, assistant professor of applied and engineering physics and the Rebecca Q. and James C. Morgan Sesquicentennial Faculty Fellow, received a five-year, $600,000 grant toward his proposal, “Quantum information science with single defects in ZnO.” His research goal is to understand the photo-physics and spin-physics of single point defects in zinc oxide as a platform for quantum information science and quantum-enhanced metrology. His project deals with point defects in zinc oxide using a “one-at-a-time” approach that combines confocal fluorescence microscopy, time-resolved single-photon counting, and magnetic resonance to understand the structure and dynamics of this individually addressable quantum system.

Jan Lammerding, assistant professor of biomedical engineering and member of the Weill Institute for Cell and Molecular Biology, was awarded $400,000 over five years for his proposal, “The role of nuclear biomechanics during cell migration in 3-D environments.” This work could have important implications in normal cell biology and cancer metastasis, as recent studies in the Lammerding and collaborating labs have shown that deformability of the typically large and stiff nucleus can present a rate-limiting step in the spreading of cancer cells through the body. The project will address to what extent the deformability of the nucleus governs the transit of cells through narrow constrictions by monitoring cells as they migrate through microfluidic channels.

Ashutosh Saxena, assistant professor of computer science, received a five-year, $485,000 award for his proposal, “Robot perception through human context.” His research interest is to make humans central to reasoning algorithms for robots. Explicit modeling and consideration of humans, even when they are not present, will enable robots to better perceive, manipulate and plan. Through such reasoning, Saxena is working on advances in the following areas: modeling 3-D scenes with objects and humans; human activity detection and affordances; path planning; and robot manipulation for assistive tasks.

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