Honors & Awards
NSF Graduate Research Fellow, National Science Foundation (2018)
Education & Certifications
B.S., University of California, Santa Barbara, Physics (2016)
Service, Volunteer and Community Work
Stanford Future Advancers of Science and Technology (Mentor), Stanford University (2017 - 2019)
San Jose, CA
Astronomy on Tap San Francisco (Speaker and Volunteer) (2018 - Present)
San Francisco, CA
Current Research and Scholarly Interests
My 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.
- Signatures of Velocity-dependent Dark Matter Self-interactions in Milky Way-mass Halos ASTROPHYSICAL JOURNAL 2020; 896 (2)
Milky Way Satellite Census. II. Galaxy--Halo Connection Constraints Including the Impact of the Large Magellanic Cloud
2020; 893 (1)
View details for DOI 10.3847/1538-4357/ab846a
- Constraints on Dark Matter Microphysics from the Milky Way Satellite Population ASTROPHYSICAL JOURNAL LETTERS 2019; 878 (2)
- Modeling the Connection between Subhalos and Satellites in Milky Way-like Systems ASTROPHYSICAL JOURNAL 2019; 873 (1)
- Modeling the Impact of Baryons on Subhalo Populations with Machine Learning ASTROPHYSICAL JOURNAL 2018; 859 (2)
- On the bispectra of very massive tracers in the Effective Field Theory of Large-Scale Structure JOURNAL OF COSMOLOGY AND ASTROPARTICLE PHYSICS 2018
- On the apparent power law in CDM halo pseudo-phase space density profiles MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY 2017; 470 (1): 500–511
- Constraints on the epoch of dark matter formation from Milky Way satellites PHYSICAL REVIEW D 2021; 103 (4)
- The SAGA Survey. II. Building a Statistical Sample of Satellite Systems around Milky Way-like Galaxies ASTROPHYSICAL JOURNAL 2021; 907 (2)
- Bounds on Velocity-dependent Dark Matter-Proton Scattering from Milky Way Satellite Abundance ASTROPHYSICAL JOURNAL LETTERS 2021; 907 (2)
Color associations in abstract semantic domains.
2020; 201: 104306
The embodied cognition paradigm has stimulated ongoing debate about whether sensory data - including color - contributes to the semantic structure of abstract concepts. Recent uses of linguistic data in the study of embodied cognition have been focused on textual corpora, which largely precludes the direct analysis of sensory information. Here, we develop an automated approach to multimodal content analysis that detects associations between words based on the color distributions of their Google Image search results. Crucially, we measure color using a transformation of colorspace that closely resembles human color perception. We find that words in the abstract domains of academic disciplines, emotions, and music genres, cluster in a statistically significant fashion according to their color distributions. Furthermore, we use the lexical ontology WordNet and crowdsourced human judgments to show that this clustering reflects non-arbitrary semantic structure, consistent with metaphor-based accounts of embodied cognition. In particular, we find that images corresponding to more abstract words exhibit higher variability in colorspace, and semantically similar words have more similar color distributions. Strikingly, we show that color associations often reflect shared affective dimensions between abstract domains, thus revealing patterns of aesthetic coherence in everyday language. We argue that these findings provide a novel way to synthesize metaphor-based and affect-based accounts of embodied semantics.
View details for DOI 10.1016/j.cognition.2020.104306
View details for PubMedID 32504912
Milky Way Satellite Census. I. The Observational Selection Function for Milky Way Satellites in DES Y3 and Pan-STARRS DR1
2020; 893 (1)
View details for DOI 10.3847/1538-4357/ab7eb9