I grew up in Portland, Oregon, and earned my B.S. in Physics from UCLA in 2018. After working as a post-baccalaureate researcher at UCLA, I began my Ph.D. at Ohio State in 2020. I am an active member of the Dark Energy Spectroscopic Instrument (DESI) collaboration, where I focus on cosmology with the Lyman-alpha forest and applications of machine learning. I also serve as co-chair of DESI’s Education and Public Outreach Committee, helping to share the science of the survey with broader communities.
I am on the job market this fall!
Research
Broadly, my research lies at the intersection of cosmology and artificial intelligence (AI). I'm interested in the big questions about the Universe, especially the nature of dark energy, the mysterious force that's driving its accelerated expansion. To help answer these questions, I am an active member of the Dark Energy Spectroscopic Instrument (DESI) collaboration. DESI is the largest spectroscopic survey to date, observing ~50 million galaxies and quasars to measure the expansion history of the Universe with unprecedented precision. Roughly one million of these observations are devoted to Lyman-α (Lyα) forest quasars. These are high-redshift (z > 2) quasars whose continua are imprinted by neutral hydrogen in the intergalactic medium (IGM). The Lyα forest therefore traces the matter-density distribution of the IGM and provides the most powerful probe of the Universe’s expansion history in the range 2 < z < 4.
Predicting the Lyα forest continuum: I recently developed the Lyα Continuum Analysis Network (LyCAN), a convolutional neural network that predicts the quasar continuum in the Lyα forest region using only longer-wavelength features. The continuum within the forest region cannot be measured directly at high redshift due to absorption from intervening H I, which complicates cosmological analyses. We applied LyCAN predictions to DESI DR1 data to perform the most precise measurement to date of the evolution of the effective optical depth. I also intend to make LyCAN publicly available in the near future.
Information content of 3D Lyα forest correlations: The current Lyα forest cosmological analysis involves a continuum fitting procedure that suppresses information on large scales. In my paper out this week, we performed forecasts that demonstrate that access to the true continuum could enable up to a ~15% improvement in key cosmological parameters. For context, this forecast improvement is analogous to increasing the Lyα forest survey area by approximately 40%! Improvements like this could enable the Lyα forest to better distinguish between different models of dark energy at high redshift.
Improving the covariance matrix estimation with ML: To realize the gains mentioned above, several enhancements to the cosmological analysis may be required. I am currently working on machine learning methods to improve our estimation of the covariance matrix for the Lyα forest two-point correlation functions. An improved covariance matrix estimation method may enable us to extend our analysis to larger scales to extract even more cosmological information.
Outreach: I am currently co-chair of the DESI Education and Public Outreach Committee, where we work to make DESI's key science results accessible to broader audiences. My main contributions have been maintaining the collaboration’s website and compiling paper guides to accompany press releases. I have been a member of the committee since 2024. More details about other outreach activities are available in my CV, which is linked on my homepage.
Mentoring: I have been fortunate to mentor and advise undergraduate students through the
OSU Polaris program and summer research programs such as
SURP.
I have also advised high school students through the
Polygence program.
