Nine Stanford faculty receive NSF CAREER Award

Stanford faculty members Anne Dekas, Sean Follmer, Monroe Kennedy, Petra Persson, Barbara Simpson, Erik Sperling, Jenny Suckale, Madeleine Udell, and Johan Ugander have each received a National Science Foundation (NSF) CAREER Award.

The NSF awards this grant to support early-career 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.

Five more faculty members received NSF CAREER awards earlier this year.

With this grant, Dekas aims to investigate the genetic potential and activity of uncultured microorganisms about 600 to 13,000 feet deep. The educational elements include training students and researchers at various levels, a laboratory course on the effects of climate change on deep-sea microbial activity, and development of an annual Northern California Geobiology Symposium featuring student and postdoctoral research. Dekas is an assistant professor of Earth system science in the Stanford Doerr School of Sustainability.

Follmer’s work will address the accessibility of 3D printing technologies for people who are blind or visually impaired. The research will focus on the development of 2.5D refreshable and scalable tactile displays and rigorous investigation of their use by the intended audience. The project also includes development and deployment of STEM learning materials for the target audience, as well as course development in the area of accessible user interfaces and technology. Follmer is an assistant professor of mechanical engineering in the School of Engineering. He is also a member of Stanford Bio-X and a faculty affiliate of the Institute for Human-Centered Artificial Intelligence (HAI).

Kennedy’s award supports research on improving the capabilities of soft robotic fingers through developing new sensors and calibration and modeling techniques. The research also promotes education in robotics. Working with the non-profit organization Black In Robotics, the research team will broaden the participation of underrepresented groups in robotics by helping students learn to design and research robotic systems capable of performing advanced service tasks. Kennedy is an assistant professor of mechanical engineering in the School of Engineering. He is also a member of Bio-X and the Wu Tsai Human Performance Alliance, a faculty affiliate at HAI, and a faculty member of the Center for Design Research.

Through this grant, Persson is researching the consequences of assisted reproductive technologies and prenatal screening technologies on families. The research project will study how these technologies affect reproductive decisions, as well as whether these technologies affect inequalities across families. The educational component focuses on training students in accessing and using big administrative data for research. Persson is an assistant professor of economics in the School of Humanities and Sciences and a faculty fellow of the Stanford Institute for Economic Policy Research.

Simpson’s work will advance fundamental understanding of the risks posed by natural hazards to the built environment by laying the algorithmic foundation for high-fidelity simulations using graphics processing units (GPUs). In parallel, the multi-disciplinary components of this research will be integrated with a larger educational commitment to develop, disseminate, and continuously reflect on an inclusive teaching pedagogy to enhance student persistence and joy in computation, training students with the skills needed for an increasingly technology-driven workforce. Simpson is an assistant professor of civil and environmental engineering in the School of Engineering and the Stanford Doerr School of Sustainability.

Sperling’s project utilizes an open international research consortium that was initiated and is led by Sperling, called the Sedimentary Geochemistry and Paleoenvironments Project (SGP), to aggregate and analyze the geochemical record. SGP aims to assemble a large database of sedimentary geochemical records using a crowdsourcing approach, and then employ statistical analyses to understand the evolution of environmental conditions on Earth. Activities in the grant will also contribute to development of the SGP website, making geochemical data centrally available for the first time. This grant will also support new sedimentary geochemistry studies of under-studied time intervals – projects that will involve undergraduate students from diverse institutions and backgrounds. Sperling is an assistant professor of geological sciences in the Stanford Doerr School of Sustainability.

The goal of Suckale’s proposal is to better understand why and how ice speeds up as it flows toward the ocean over rough rock. The research involves developing a suite of customized models that will be available as open-source tools to the glaciological community. A coordinated educational program aims to increase participation in polar science for high-school students from historically disadvantaged communities that are prone to be impacted by sea-level rise in the near future. Suckale is an assistant professor of geophysics in the Stanford Doerr School of Sustainability. She is also a faculty affiliate of HAI and a member of the Institute for Computational and Mathematical Engineering (ICME).

With this award, Udell is developing new methods to accelerate and automate the basic machine-learning workflow. This project will help democratize machine learning and promote data-driven decision-making by developing automated methods to clean data and choose machine learning models that make these methods widely available and easy to use. The project also advances these goals by training data scientists in how to use these models and understand their potential risks. Udell is an assistant professor of management science and engineering in the School of Engineering.

Ugander’s project focuses on developing theoretical foundations and applications for new machine learning algorithms that learn and predict human decisions descriptively from data, as they are, rather than as behavioral theories prescribe them to be. By operationalizing these behavioral theories within a machine learning framework, this research will make it possible to employ the lessons of behavioral economics to improve the design of large-scale web systems. Ugander is an associate professor of management science and engineering in the School of Engineering and a member of the ICME.