The Fannie and John Hertz Foundation Announces 2019 Graduate Fellows
The Fannie and John Hertz Foundation, a nonprofit organization dedicated to advancing groundbreaking applied science with real-world benefits for all humanity, has announced the 2019 recipients of the prestigious Hertz Fellowship.
Drawing from nine of the country’s most prestigious research universities, 11 young researchers were selected from a pool of more than 840 applicants to receive up to five years of academic support valued at up to $250,000. Along with the funding, Fellows will also be free of many constraints typical of other fellowships, providing them the freedom to pursue innovative research wherever it may lead.
“It is increasingly challenging to get funding for truly creative scientific research, but it is even more so for young researchers to pursue their own ideas,” said David Galas, Chairman of The Hertz Foundation’s Board of Directors and a senior investigator at the Pacific Northwest Research Institute. “I am very gratified that these terrific new Hertz Fellows will now be able to focus on the research questions they find most compelling in their fields.”
This year’s Fellows will pursue research at the core of discovery and impact, with interests ranging from finding better ways for matching drugs to new diseases to developing better therapies for cancer, to manipulating the quantum world for technological advancement. As noted by Robbee Baker Kosak, president of the Fannie and John Hertz Foundation, “By combining approaches from mathematics and computer science, machine learning and biology, and so much more, this year’s class reflects the dynamic nature of science and engineering today. It also reflects the type of bold, risk-taking research that the Hertz Foundation has supported for almost six decades. I eagerly look forward to watching them fuel the innovation essential to our lives and the world.”
The 2019 class joins a community of Fellows that includes leading scientists and engineers who have been honored with the Nobel Prize, the National Medal of Science, the Turing Award, the Breakthrough Prize, and the MacArthur Fellowship (“Genius Grant”). They are also leaders in business and industry whose accomplishments include developing groundbreaking diagnostics and treatments for disease, new innovations for energy creation and storage, novel tools for exploring earth and space, and creating new supercomputer designs. Read below to learn more about the 2019 Hertz Fellows.
Introducing the 2019 Hertz Fellows
Alex Atanasov (Harvard University; Physics) — Alex Atanasov is fascinated by phenomena existing at vastly different scales while also adhering to common scientific principles. For example, the branches of certain plants exhibit the same splitting angles as running rivers that carve across a continent. A PhD student in theoretical physics at Harvard University, Atanasov studies the interrelationship of these fractal-like patterns and the underlying physical property that characterizes them. He is especially interested in how this property affects strongly interacting systems, such as when an ordinary metal becomes a superconductor. He studies these systems using techniques and concepts from quantum field theory, the framework for understanding the interactions of fundamental particles.
Dolev Bluvstein (UC Santa Barbara; Physics) — Dolev Bluvstein approaches nanoscale technology with the ambition to revolutionize the capacity of humans to understand the universe and master its behavior. He has joined the growing wave of research in quantum information science that could potentially transform modern computing, communications, and biological and environmental sensing. His interests involve technologically harnessing the unique rules of quantum mechanics, which dictate the laws of physics at atomic and subatomic scales. A senior physics major at the University of California, Santa Barbara, Bluvstein’s work includes exploiting an atomic-sized defect in diamond to reshape nuclear magnetic resonance technology — an advancement that could have far-reaching implications in biology and medicine.
Dylan Cable (Massachusetts Institute of Technology; Computer Science) — Cable’s exploration of neuroscience and biology as a Stanford University undergraduate convinced him that life science problems are the most important to solve. His goal: to uncover the inner workings of vital biological processes by forging new linkages between mathematics, machine learning (teaching computers to learn by experience) and computational biology. A first-year PhD student in computer science at the Massachusetts Institute of Technology, he specializes in improving physical methods for biological data collection and creating mathematical methods for biological data analysis. Cable regards data collection and data analysis as inseparable and must be woven into a deep knowledge of the biological problems he seeks to solve.
Jordan Edmunds (UC Berkeley; Electrical Engineering) — A PhD student in electrical engineering at the University of California, Berkeley, Edmunds specializes in the fabrication of neural interfaces— tools for studying and interacting with the brain. As an undergraduate at UC Irvine, where Edmunds earned dual degrees in electrical engineering and biological sciences, he created devices that connect with the human nervous system. The work included developing an electrical stimulator to elicit muscle contractions and restore movement to paraplegics. Work on the full system continues at the UCI Brain-Computer Interface Lab. He now works closely with Michel Maharbiz, the co-inventor of tiny, wireless sensors called neural dust for use as biomedical implants.
Benjamin Eysenbach (Carnegie Mellon University; Machine Learning) — Eysenbach is a first-year PhD student at Carnegie Mellon University who teaches computers to make smart decisions that help humans. His research uses statistics and machine learning to make robots safer and enabling them to learn autonomously, with less human engineering effort. Before his PhD, Eysenbach spent a year conducting robotics research at Google Brain, teaching simulated robots to do backflips and learning how to avoid breaking themselves. He studied mathematics as an undergraduate at the Massachusetts Institute of Technology. As an undergraduate researcher, he taught computers to understand images and videos using machine learning and computer vision.
Bailey Flanigan (University of Wisconsin-Madison; Biomedical Engineering) — Flanigan combines approaches from computer science, economics, and the physical sciences to address disparities and complex problems in society. Her interest in complex systems originally drew her primarily to medical questions as an undergraduate in biomedical engineering at the University of Wisconsin-Madison. She conducted undergraduate research in cancer genetics, heart disease, and functional magnetic resonance imaging, and then broadened her focus to include social and economic systems. As an undergraduate, she managed a team of engineers that designed and implemented a potable water system in rural Ecuador. She will travel to South Africa this summer to research maternal health and build software for a rural hospital. Next fall she will begin graduate work in theoretical computer science, having completed a research fellowship in economics last academic year at Yale University.
Noah Golowich (Harvard University; Mathematics and Computer Science) — Golowich’s research interests include developing a greater understanding of the theoretical reasons behind the success of today’s deep learning methods, which use neural networks to perform tasks that could revolutionize technologies as seemingly unrelated as language translation, game theory, and self-driving cars. A senior at Harvard University, Golowich is completing a joint degree in mathematics and computer science. He also has contributed to the theory of communication complexity, which involves structuring communication between computers in the most efficient way to solve a given problem. Some of his work in communication complexity is motivated by cryptography — generating efficient algorithms for two parties to communicate securely and efficiently.
Melissa Mai (Johns Hopkins University; Biophysics and Mathematics) — Currently a senior biophysics and mathematics major at the Johns Hopkins University, Mai has pursued different experimental and computational projects studying vaginal health, blood cell development, protein evolution, and cell motility. Through her diverse research experiences, she has become interested in the cooperation and competition among individuals in a population and, more generally, in the application of quantitative, physics-based approaches to biological problems. This fall, Mai will begin her PhD in biophysics at Harvard University, where she hopes to integrate both experiment and theory to delve further into cellular biophysics on a population scale.
Nitya Mani (Stanford University; Mathematics and Computer Science) — Mani explores the underlying properties of networks, called graphs, which characterize transportation systems, social media, biological systems, and other aspects of modern life. She is particularly interested in how nodes in a graph can be grouped to have many internal connections or to influence the rest of the network. She also uses machine learning on networks with applications that include finding new diseases that an existing drug can treat. A senior in mathematics and computer science at Stanford University, Mani also grapples with challenging optimization problems that reside at the intersection of those fields. Many everyday questions, including finding optimal pharmaceutical treatments and processing images, are optimization problems that are difficult to solve using current techniques.
Jacqueline Turner (University of Colorado Denver; Medical Science) — Driven by a desire to improve cancer patient care, Turner is training to become a clinician-scientist at the CU Denver School of Medicine and aspires to translate laboratory research results directly into clinical practice. As an undergraduate at the International Biorepository and Research Laboratory at the University of Colorado Denver, her work on gene rearrangements in tumor cells already has helped introduce basic scientific research into the clinical setting, starting with the successful treatment of a stage IV melanoma patient. Turner then identified novel therapies for three other stage IV melanoma patients. Her goal is to develop methods that will impact the treatment and prevention of tumor formation.
Nina Zubrilina (Stanford University, Mathematics) — From squeezing spheres into ever-shrinking volumes of space to analyzing the structure of the tree-like diagrams that help scientists understand the evolutionary relationship of plant and animal species, Zubrilina’s research ferrets out the relationship between classes of objects and phenomena that might otherwise remain opaque. Zubrilina uses mathematics to deepen our biological understanding of the tree of life — science’s depiction of how life on Earth grows, branches, and blossoms. Last summer she made novel contributions to sphere packing while interning at Microsoft Research. This geometrical problem has intrigued mathematicians for centuries and is closely connected with error-correcting codes and signal transmission, from cellphones to deep space communication.