About Me
Education:
Physics PhD Candidate, Yale University
BS Physics and Astronomy, Stony Brook University
Research Interests: Theoretical Astrophysics and Cosmology. Supernovae. Structure and Formation of the Universe. Machine and Deep Learning.
Apart from my research, I lead a machine-learning working group with undergraduate, graduate, and faculty from universities around the world! We focus on how
to implement interpretablility within new or pre-existing astrophysical and cosmological problems. Contact me if you are interested in participating!
I also have a physics-related website, called Trial and Error,
made for students from high school and beyond who are interested in the "how-to's" of academia, specifically within physics. I
had very little guidance on graduate applications, and I hope this will fill in those gaps for an aspiring researcher!
Fun Fact: I speak English, Hebrew, Italian, some Mandarin, and am learning Russian and Romanian!
If you have any questions about my work, feel free to send me an email (or copy-paste
naomi.gluck@yale.edu)!
LinkedIn: https://www.linkedin.com/in/naomigluck/
Research
Modeling the Halo-Gas-Galaxy Connection: We are developing a computationally efficient, physically motivated model of the evolution of both baryonic
and dark matter. This is done via JAX auto-differentiation methods, allowing for a fully differentiable model
of the gas within dark matter halos, as an update to the original Baryon Pasting code. We also include dependencies on
mass accretion history, concentration history, and AGN feedback. (Gluck et al. in prep)
Machine Learning the Circum-Galactic Medium with CAMELS
We develop a likelihood-free deep learning technique using convolutional neural networks (CNNs)
to infer broad-scale physical properties of a galaxy's circum-galactic medium (CGM) and halo mass from multiwavelength datasets.
Our CNNs, trained on CAMELS data, offer improved inferences by integrating both HI and X-ray maps, providing a novel approach
to mapping the CGM across different galaxy formation models. (See Gluck et al. 2023 for additional details.)
Undergraduate Research
Stony Brook University - Uncertainty Quantifications
This particular uncertainty quantifications study looks at how incertitude in the winds
affect a 1 solar mass star and it's resulting white dwarf structure. I applied the publically available stellar evolution code, MESA (Modules for Experiments in Stellar Astrophysics)
to run simulations through the SeaWulf cluster to explore the wind parameter space. Three test suites were run, each with a different
statistical model: evenly spaces, Cauchy, and uniform random. Research objectives include studying and determining the boundaries of the
Reimers and Blocker wind parameters to see if the stellar evolution code completes with certain values, and to look at the final parameter values of the resulting white dwarf from each of the three test suites.
Technion Israel Institute of Technology - The Red Supergiant Problem
The Red Supergiant problem discusses the inconsistencies of observable supernovae,
as there is a gap between which progenitor stars lead to observable supernova explosions. Using MESA (Modules for Experiments in Stellar Astrophysics) I
simulated different progenitor stars of varying ZAMS (zero age main sequence) and wind and mass loss parameters. These mass loss parameters are key to understanding which stars will
be obscured by dust by the time they explode as core collapse supernovae. (See Gofman, Gluck, and Soker 2020 for additional details.)
Posters
Posters
APS 2023 April Meeting
Ureca Poster 2020
Observational Data SS Cyg
Teaching
Undergraduate Courses at Yale
1. PHYS 378 (Introduction to Scientific Computing and Data Science): Spring 2023 (Graduate Teaching Fellow),
Fall 2023 (Guest Instructor), Fall 2024 (Associate in Teaching Fellowship awarded, Co-Instructor)
2. PHYS/ASTR 343 (Gravity, Astrophysics, and Cosmology): Fall 2022
3. PHYS 120 (Quandum Physics and Beyond): Spring 2022
4. PHYS 200 (Fundamentals of Physics): Fall 2021
Research Mentorship at Yale
I have mentored several undergraduate students through the Nagai Group (in Physics, Astrophysics, and Mathematics) in their research projects, including their junior and senior thesis papers,
and summer fellowships.
STEM Tutoring
1. Science Courses: Physics (regents, honors, AP 1, AP C, university-level courses), Chemistry (regents, honors, AP), Biology (regents, honors, AP)
2. Mathematics Courses: Geometry, Algebra/Trigonometry, Calculus (Pre, AP-AB, AP-BC), Exeter Math Curriculum
Resources
Python/Coding
Python Libraries for Lazy Scientists
Displaying Pipelines (scikit-learn)
QuantEcon | Python/Julia examples with basis in economics
HelloUniverse | Machine-learning applications to astrophysical datasets
The Hitchhiker's Guide to Python
Python for Scientific Computing (SBU) | Includes machine learning, Python packaging, unit tests, extensions, git/Github version control
Terminal Command Line Notes | Linux terminal commands with specifics for Yale's GRACE cluster.
Graduate School and Academics
Trial and Error | A how-to on navigating a degree in physics, from high school, to undergrad, to graduate school!
List of Fellowships & Grants (Yale)
Overleaf CV Template
Graduate Application Organizational Template
LaTeX Math Wiki | for advanced mathematical needs
Physics and Astronomy
ASTR 610 - Theory of Galaxy Formation | Lecture by Frank van den Bosch, Yale
Extras
PhD Simulator
Artwork
