Simons Society of Fellows: Junior Fellows

Junior Fellows are outstanding young scientists, no more than two years after Ph.D. at the time of the appointment start date, who receive support from the foundation for three years to conduct independent research at an institution of higher learning in the New York City area. A Junior Fellow receives a stipend each year, funds for fringe benefits, as well as a research allowance. Junior Fellows have no teaching obligations and are expected to be in residence in the New York City area during the academic year. Junior Fellows attend weekly dinners, regular lectures, and conferences organized by the foundation.

To be a Junior Fellow, one must be engaged in research in one of the following areas of science: Life Sciences, including genetics, cellular and organismic biology, neurosciences and basic aspects of biomedical research, Physical and Engineering Sciences including Astronomy, Chemistry, basic aspects of Engineering, Earth Sciences, Physics and related disciplines, and Pure and Applied Mathematics and Computer and Information Science.

Junior Fellows by year

The Simons Foundation is pleased to announce the Junior Fellows appointed in 2016.

Ruth Angus

Ruth Angus is a postdoctoral researcher in the department of astronomy at Columbia University. She received a D.Phil from the University of Oxford where she worked with Professor Suzanne Aigrain on methods for dating stars. Before that, she completed a Masters in physics at the University of Southampton, UK.

Ruth is interested in extrasolar planets — planets outside our own Solar system. She uses her expertise in star-dating to measure the ages of thousands of stars and planets in the Milky Way. By doing this she hopes to reveal the processes behind the formation of these alien worlds and their distribution across the galaxy.

Gilad Asharov

Gilad Asharov is an incoming postdoctoral research scientist in the department of computer science at Cornell Tech. He completed his Ph.D. at Bar-Ilan University in Israel under the supervision of Yehuda Lindell. He spent a few months as a postdoctoral researcher at the Hebrew University of Jerusalem and at the IBM Thomas J. Watson Research Center.

His research interests are in the field of cryptography, with a focus on secure protocols. His research concentrates on feasibility, including the question of what cryptographic tasks can and cannot be realized under various cryptographic assumptions, and on efficiency, including design and optimization of highly efficient cryptographic protocols that have a rigorous proof of security. He also studies the interplay of cryptography and related fields, such as computational complexity, data structures, game theory and computer networks.

Timothy Burbridge

Timothy Burbridge is a postdoctoral fellow in the Neuroscience Institute at NYU Langone Medical Center. He received his B.A. in biology from Williams College and his Ph.D. in neurobiology at Yale University. He is broadly interested in neural circuit development and, more specifically, in the activity-dependent factors that control mammalian axon, dendrite and synapse growth and pruning. He is particularly interested in the genetic and molecular factors that lie downstream of early neural activity patterns and which are believed to mediate neural circuit and activity modifications. His prior work was focused on deciphering the relative importance of both very early (cell migration and positioning) and somewhat later (spontaneous activity-dependent plasticity) stages of circuit development and their respective contributions to adult circuits and developmental disorders. He aims to use this knowledge of early activity-dependent circuit plasticity to investigate the genetic and molecular processes that are believed to translate early spontaneous and sensory-evoked neural activity patterns into both structural changes in interneuron subtypes as well as altered levels and patterns of inhibitory and excitatory activity in the maturing central nervous system. Burbridge’s overarching goal is to relate findings from these critical periods of development to neurodevelopmental disease and injury models, with the objectives of improved diagnosis and treatment of currently enigmatic and intractable conditions.

James Dama

James Dama is an incoming postdoctoral scientist in the department of chemistry at Columbia University. He completed his Ph. D. at the University of Chicago, where he worked with Gregory Voth on multiscale physics methods for the study of biomolecules, with particular focuses on adaptive enhanced sampling methodology and the physics of many-body coarse-grained free energies. At the University of Chicago, he was supported by Windt family and Harper dissertation fellowships and received the Cao-Lan-Xian best thesis prize in Physical Chemistry. Prior to his work at the University of Chicago, Dama received a B.S. in chemical engineering from the California Institute of Technology, where he researched synthetic biology, electrophoretic separations and colloid physics.

As a postdoctoral researcher, Dama continues his interests in the statistical mechanics of soft matter, and in particular parsimonious and decomposable representations for the dynamics of many-particle classical chemical systems. At Columbia, he will join the laboratory of Professor David Reichman, where he plans to continue working on using universal physics and simplified representations to guide the study of biomolecular systems, but with a new focus on explaining nonequilibrium and time-dependent phenomena in persistently disordered matter.

Logan Grosenick

Logan Grosenick is a postdoctoral fellow in the Department of Statistics at Columbia University. He received a Ph.D. in neurosciences from Stanford University, where he worked with Karl Deisseroth and Marc Levoy developing novel methods for volumetric functional neuroimaging of neural circuits in behaving animals. Previously he completed a Master of Science in statistics at Stanford, working with Jonathan Taylor, Brian Knutson and Patrick Suppes, to predict human behavior from single-trial fMRI, MEG and EEG data, developing interpretable models that were both accurate and yielded insight into brain function. 

Grosenick is interested in developing and deploying engineering approaches to observing, controlling and understanding neuronal circuit dynamics in behaving animals. He has expertise in animal behavior, functional neuroimaging and developing large-scale machine learning approaches for modern massive data sets, and he hopes to combine these skills to build more accurate, interpretable and practically useful models of how brains function in health and disease.

Keith Hawkins

Keith Hawkins is an incoming postdoctoral research fellow in astronomy at Columbia University. He received his B.S. in astrophysics with minors in mathematics and African studies from the Honors Tutorial College at Ohio University in 2013 and is completing his Ph.D. in astronomy at the Institute of Astronomy, University of Cambridge, under the guidance of Gerry Gilmore and Paula Jofre.

His research interests are in galactic archaeology, with the goal of revealing the formation and evolution of our galaxy through detailed chemical and dynamical studies of individual stars. His doctoral research focused on dissecting the Milky Way using stellar spectroscopy. He specializes in detailed chemical abundance analysis, data mining of large surveys, and low- and high-resolution spectroscopy. At Columbia, he will continue to be an active member of large spectroscopic surveys, and will make use of the wealth of data (both in kinematics and in chemistry) in those surveys not only to better understand the structure of our galaxy but also to expand his research to other nearby systems.

Mijo Simunovic

Mijo Simunovic obtained his first Ph.D. at the University of Chicago in the field of theoretical chemistry. Working with Gregory Voth, he applied theoretical modeling and mesoscale-level simulations to study how proteins organize on lipid membranes to change their shape. His research demonstrated a mechanism of membrane deformation and protein self-assembly that is key in many cellular phenomena. Mijo earned his second Ph.D. from the University of Paris 7 in the field of condensed matter physics as a Chateaubriand Fellow. Working with Patricia Bassereau at the Curie Institute, he employed experimental biophysical techniques to elucidate the mechanical basis of important cellular processes. In particular, his work helped in discovering a new mechanism by which cells internalize cargo from the environment, driven by an unexpected interplay of membrane proteins and molecular motors. For his research efforts in Chicago he was awarded the Cao-Lan-Xian best thesis prize in Physical Chemistry, while his thesis in Paris received the highest distinction from the jury. During his graduate studies, Mijo also received several teaching prizes and a research award from the American Chemical Society.

Mijo continues to carry out research at the interface of physics and biology at the Rockefeller University. Working with Eric Siggia and Ali Brivanlou, he uses embryonic stem cells to investigate the physical and molecular mechanisms that control the timing and size in early human development.

Yi Sun

Yi Sun will be joining the Department of Mathematics at Columbia University as a postdoctoral research scientist. He completed his Ph.D. at the Massachusetts Institute of Technology under the supervision of Pavel Etingof. Prior to MIT, Dr. Sun received a M.A.St. in mathematics from the University of Cambridge with the support of the Churchill Scholarship and an A.B. and A.M. in mathematics from Harvard University.

Dr. Sun’s research focuses on representation theory, integrable systems, and their applications to special functions, integrable probability, and random matrices. His thesis work includes a structural study of the Macdonald-Ruijsenaars integrable system and its degenerations via quantum groups and a solution to the first case of the Etingof-Varchenko conjecture relating trace functions for quantum affine algebras to theta hypergeometric integrals. In the past, he has also worked on applications of stochastic processes to network methods in machine learning and axiomatic foundations of cost-sharing methods in economics.

Li-Cheng Tsai

Li-Cheng Tsai is a postdoctoral researcher in the Department of Mathematics at Columbia University, working with Professor Ivan Corwin. Tsai received his B.S. degree in Physics from National Taiwan University, and later received his Ph.D. degree in Mathematics from Stanford University, with Professor Amir Dembo being his thesis advisor.

Tsai’s research interests lie in probability theory. His graduate study ranges a series of topics on interacting particle systems, including characterizing the asymptotic fluctuation of systems in terms of stochastic partial differential equations, and pathwise construction of certain infinite dimensional systems with long-range and singular interactions. He was awarded the KITP Graduate Fellowship in 2016.

Zheng (Herbert) Wu

Zheng (Herbert) Wu is a joint postdoctoral fellow in the laboratories of Drs. Richard Axel and Michael Shadlen in the Department of Neuroscience at Columbia University. He received his Ph.D. in neurobiology from Harvard University and his B.S. from Fudan University in China. His graduate work focused on dissecting the neural circuits underlying parental behavior in mice. Using a battery of molecular and genetic tools, he identified a population of neurons in the anterior hypothalamus as key regulators of parental care. He has published in journals such as Nature and Science, and was a guest speaker at the Society for Social Neuroscience meeting.

As a postdoctoral researcher, Dr. Wu is interested in understanding working memory and context-dependent decision making. To elucidate their neural basis, he has developed an odor-guided delayed-match-to-sample task in mice and is using a wide variety of techniques including electrophysiology, calcium imaging and optogenetics. These experiments will establish a system to address context-dependent decision making and will have broad implications for understanding the neural circuits of flexible behavior in health and disease.