Simons Foundation Announces First Pivot Fellowship Recipients
The Simons Foundation is pleased to announce its first class of Pivot Fellows. The program will support the seven accomplished researchers as they apply their talent and expertise to a new field in mathematics or the natural sciences.
Each fellow will receive support for one year of training in their new field under a mentor, followed by the opportunity to apply for up to three years of research funding in the new discipline.
“Scientists who move into new fields have had outsize impact,” says Simons Foundation president David Spergel. “This new program aims to enable and accelerate this process and to break down barriers between fields.”
The program is open to faculty in the natural sciences, mathematics, engineering, data science and computer science at academic institutions or equivalent positions elsewhere. The fellowships provide salary support as well as research, travel and professional development funding. In addition, mentors will receive a $50,000 research fund. At the end of the fellowship year, fellows will be invited to apply for a 3-year research award in the new field for up to $1.5 million over the 3-year period.
Amit Acharya is a professor in the Department of Civil and Environmental Engineering at Carnegie Mellon University and a member of the university’s Mechanics, Materials, and Computing research group. He is an engineer by training with expertise in continuum mechanics, theoretical materials science and applied mathematics.
During his fellowship, he will work with his mentor Ambar Sengupta of the University of Connecticut. They plan to develop the mathematical background to establish a branch of mathematical gauge theory directly adapted to studying the dynamics and collective behavior of topological defects called dislocations and disclinations in nonlinear elastic solids. Their goal is to study topological defect dynamics in elasticity by path integral methods (at least in the finite-dimensional approximation) to understand its statistical properties, and thus hopefully shed light on the plasticity of crystalline solids — a scientifically intricate macroscopic phenomenon of great technological importance.
Viviana Acquaviva is an astrophysicist using data science techniques to study the universe. Currently, she is a professor in the physics department at the New York City College of Technology and the CUNY Graduate Center. She is also a long-term visiting scholar at the Flatiron Institute’s Center for Computational Astrophysics. She is an innovator and advocate for the application of machine learning in physics and astronomy.
Acquaviva’s goal as a Simons Foundation Pivot Fellow is to bring the numerical modeling, statistics and machine-learning skills she currently uses in astrophysics to develop better models of the Earth. Improved models mean more accurate predictions of Earth’s future conditions; improved extrapolation in data-poor regimes and regions; faster emulation in data-rich but computationally intensive regimes; validation of numerical approximations; uncertainty quantification; and dimensionality reduction to foster physical understanding. At the end of her fellowship, she hopes to understand the different components of a climate model (cryosphere, atmosphere, ocean, land) and how they interact with each other, in order to select her own research questions to pursue.
Julia Hsu is a professor of materials science and engineering in the Erik Jonsson School of Engineering and Computer Science at the University of Texas at Dallas and holds the Texas Instruments Distinguished Chair in Nanoelectronics. She is an experimental materials physicist who studies materials physics phenomena using traditional approaches such as performing optimization by varying one variable at a time. In materials research, optimization is ubiquitous, but the outcomes are typically affected by a multitude of variables. Furthermore, the various input parameters have complex and nonlinear interrelationships, creating a combinatorically vast space of possible experimental conditions that cannot be exhaustively tested in practice since performing experiments is time-consuming and expensive.
Hsu’s Pivot Fellowship discipline will be machine learning, which sits at the intersection of mathematics, computer science and statistics. She aims to apply machine learning to materials synthesis and processing to guide expensive experiments to make more effective use of limited resources and to elucidate the complex interdependence among the variables. During the fellowship, Hsu will be guided under the mentorship of Tonio Buonassisi of the Massachusetts Institute of Technology and will apply machine-learning approaches to optimization problems in materials synthesis and processing, particularly energy materials.
Albert Keung is a synthetic biologist and stem cell engineer who works at North Carolina State University as an associate professor of chemical and biomolecular engineering, a University Faculty Scholar and Goodnight Early Career Innovator. His group developed new technologies and experimental platforms to study epigenetic regulation in stem cell-derived models of early human neurodevelopment, to help decipher the ‘histone code’ using protein-engineering platforms, and to engineer synthetic biology systems for new applications outside the realm of native biology, such as DNA-based digital data storage.
Through the Simons Foundation Pivot Fellowship, Keung will immerse himself in the field of interpretable machine learning through the expertise and mentorship of Cynthia Rudin of Duke University. Specifically, he will focus on key approaches to deal with the sparsity of representation, especially in the context of the immense diversity of human health and biology, and how interpretable machine-learning methods can promote responsible and equitable models in the face of this challenge. Related interpretable methods could provide insights into biological function rather than just establish correlations, a common challenge in data-intensive biological research. Supporting these efforts will be the development of new approaches to interface iterative experimental design cycles with machine learning to explore parameter spaces both native and non-native to biology. A central goal of the fellowship will be to develop strategies in which molecular and biological concepts can be fused with disparate macroscale data types and applied to challenges facing society, including health care, clinical outcomes, drug discovery and epidemiology.
Albion Lawrence is a professor of physics at Brandeis University. His training and current field of study concern theoretical high-energy physics and cosmology. Recently, he has been excited by the prospects of studying Earth’s ocean, atmosphere and cryosphere to address foundational questions in complex dynamical systems and nonequilibrium statistical physics.
His goal for the Simons Foundation Pivot Fellowship is to establish a research program in physical oceanography rooted in observation and using tools and concepts from his background in theoretical physics. The specific project proposed with Lawrence’s mentor, Jörn Callies of the California Institute of Technology, is to begin the study of higher-order statistics of ocean turbulence and internal waves via satellite altimetry. This work will deepen our understanding of the nature of ocean turbulence and the internal wave field across scales and give us tools to disentangle different types of ocean dynamics, which have very different properties with respect to the transport of important physical properties, chemical substances and biological organisms.
J. Xavier Prochaska is a distinguished professor of astronomy and astrophysics at the University of California, Santa Cruz. As an observational astrophysicist, he studies the gas within, around and between distant galaxies. His research programs reveal the physical conditions of this gas: its enrichment by heavy elements, the ionization state, the surface density and its dynamics.
With the Simons Pivot Fellowship, he will transition to the physical oceanography field, working under Daniel Rudnick’s mentorship at the Scripps Institution of Oceanography (SIO) at the University of California, San Diego. Prochaska’s primary goals during the fellowship are to apply his expertise in spectroscopy, machine learning and instrumentation to research complex processes in submesoscale dynamics; generate data-driven predictions for the onset of harmful algal blooms; and develop new technologies for in-situ hyperspectral imaging of coastal regions. From the large and diverse pool of scientists and staff at SIO, he will build a network to inspire new collaborations and threads of research that will bridge the oceanographic and astronomical communities.
Diwakar Shukla is an associate professor in the Department of Chemical and Biomolecular Engineering at University of Illinois Urbana-Champaign. He is also an affiliate faculty member in the Center for Biophysics and Quantitative Biology, Plant Biology and Bioengineering. His research work focuses on understanding biological processes using physics-based models and techniques.
Through his new fellowship, he will pivot to experimental plant biology from computational chemistry. During this work, he will bring the disciplines of computational chemistry and experimental plant biology under one roof to enable rapid cycles of design and innovation in plant protein engineering. With mentoring from Stephen Long of the University of Illinois at Urbana-Champaign, he will develop a framework for integrating ideas from machine learning to guided protein engineering, experimental plant biology and design validation in living plant tissues. The training phase’s overall goal is to demonstrate these synergistic approaches on a problem of high agricultural significance.