School of Humanities and Sciences


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  • Ethan Nadler

    Ethan Nadler

    Ph.D. Student in Physics, admitted Autumn 2016

    Current Research and Scholarly InterestsMy research aims to uncover the particle nature of dark matter through its influence on small-scale cosmological structure, and particularly on the abundance and properties of the faintest galaxies. My collaborators and I have characterized the imprint of dark matter microphysics on cosmological structure formation in terms of the minimum halo mass—i.e., the mass scale below which deviations from the cold or collisionless assumptions underlying standard dark matter theory prohibit the formation of gravitationally bound dark matter halos. This approach is compelling because the minimum halo mass is sensitive to a wide variety of dark matter properties, including its production mechanism, formation time, stability, warmth, self- and Standard Model-interactions, and de Broglie wavelength.

    By combining the minimum halo mass formalism with a detailed galaxy–halo connection model based on high-resolution cosmological simulations, we are placing robust constraints on dark matter microphysics using the population of Milky Way satellite galaxies. Along with Vera Gluscevic, Kimberly Boddy, and Risa Wechsler, I derived the impact of early-universe dark matter–baryon scattering on dwarf galaxy abundances and set new constraints on these interactions, improving upon previous cosmological limits by several orders of magnitude. In an upcoming paper with the Dark Energy Survey, we apply this methodology to the full population of ultra-faint Milky Way satellites to place some of the most stringent limits to date on sterile neutrino, WIMP-like, and ultra-light axion dark matter. I am involved in related efforts to constrain dark matter self-interactions, following my work with Arka Banerjee and Susmita Adhikari on the phenomenology of self-interacting dark matter in the Milky Way.

    I have worked closely with Yao-Yuan Mao, Gregory Green, and Risa Wechsler on a flexible and rigorous galaxy–halo connection model for faint systems. We developed a machine-learning algorithm that emulates subhalo disruption in hydrodynamic simulations, which we used to infer the properties of dwarf galaxy halos in a Bayesian framework. I am actively incorporating observations of Milky Way analogs from the Satellites Around Galactic Analogs (SAGA) survey into these constraints, which will place the Milky Way in a cosmological context and test the environmental dependence of the galaxy–halo connection in the dwarf regime.

    I'm broadly interested in analytic and statistical techniques to describe cosmological structure formation; my undergraduate research with Peng Oh focused on the phase-space structure of dark matter halos, and I've studied halo clustering statistics using effective field theory techniques with Leonardo Senatore. I am also excited about interdisciplinary research, having co-authored papers in cognitive science and computational linguistics following my participation in the Santa Fe Institute's Complex Systems Summer School.

    I am committed to education and mentorship with an emphasis on promoting diversity and equity for underrepresented groups in the physics community. I have mentored several undergraduate and post-baccalaureates on projects ranging from subhalo disruption in galaxy clusters (with Abigail Lee, now a University of Chicago graduate student) and hydrodynamic simulations (with Nicel Mohamed-Hinds, now a University of Washington graduate student), cosmological simulations of Milky Way-like systems (with Deveshi Buch, a Stanford undergraduate), and analyses of Large Magellanic Cloud analogs in the SAGA survey (with Veronica Pratt, also a Stanford undergraduate). I volunteered for Stanford’s Future Advancers of Science and Technology organization for several years, and I've volunteered as a public speaker for San Francisco’s Astronomy on Tap program.

  • Stephanie Nail

    Stephanie Nail

    Postdoctoral Research Fellow, Political Science

    BioI am a Postdoctoral Research Fellow at Stanford University working with Douglas Rivers, Morris Fiorina, and David Brady. I obtained a PhD in Political Science in July 2019 from the University of California, Merced. I received a Bachelor of Science in Psychology (Mathematics) and Managerial Economics with a minor in Statistics from the University of California, Davis in 2014.

    I study how people use information to make decisions with methodology ranging from experimental studies and instrumental variables to spatial models and new measures. My current methodological interests include experiments, survey sampling, design, and analysis, Bayesian statistical inference, mathematical statistics, causal inference, and behavioral game theory. Substantively, my current interests include the study of information, party identification, polarization, judgement and decision-making, political behavior, and legislative politics.

    At the undergraduate level, I have taught "Introduction to Judgment and Decision Making," an upper-division course that incorporates the foundations of information processing and biases while applying them to real life situations in political science, cognitive science, economics, and management.