プロフィール詳細

プロジェクトを作成

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Ethan V.に依頼

United States

Freelance Physicist & Data Analyst | Expertise in Python, Academic Writing, & data analysis/visualization

プロフィール概要

I am an astrophysics researcher with a strong background in observational astronomy, cosmology, and computational modeling. I hold a B.S. in Physics and Astronomy with a minor in Mathematics from the University of Rochester (cum laude, Math/Physics GPA: 3.96/4.00) and will begin my Ph.D. in Physics at Johns Hopkins University in 2026, specializing in cosmology. My research spans stellar population synthesis, large-scale structure, and galactic dynamics. At the Harvard-Smithsonian Center for Astrophysics, I developed the first high-resolution simple stellar population (SSP) library optimized for JWST, incorporating multiple stellar atmosphere models and advanced telluric correction techniques. This work, validated on JWST and WAGGS data, significantly improved spectral fits and was presented at the AAS 246 meeting. At the University of Rochester, I created a novel statistical framework for constraining primordial non-Gaussianity using higher-order moments of the density field, achieving ~21% improved sensitivity over the two-point correlation function. I have also led projects at the Leibniz Institute for Astrophysics Potsdam, using Gaia DR3 and simulations to revise estimates of the Milky Way bar’s structure, and at Los Alamos National Laboratory, applying Bayesian Monte Carlo methods to fit high-dimensional hydrodynamic model parameters. My technical skills include Python, Bayesian inference, MCMC, spectral fitting (pPXF), large-scale data analysis, and high-performance computing. I have observational experience on the 6.5 m Magellan telescope in Chile and extensive experience working with survey data from DESI, Gaia, and JWST. Beyond research, I have served as a graduate course evaluator, undergraduate teaching intern, and physics tutor, as well as a mentor for undergraduate students. I have authored multiple peer-reviewed papers and presented at international conferences, demonstrating strong communication skills and project leadership.

専門分野

• ★ Bayesian Statistics
• ★ Astrophysics
• ★ Python
• ☆ Statistics
• ☆ Applied Statistics
• ☆ Regression Analysis
• ☆ Multivariate Analysis
• ☆ Nonlinear Regression
• ☆ Quantum Statistics
• ☆ Earth & Atmospheric Science
• ☆ Theoretical Physics
• ☆ Computational Physics
• ☆ Applied Physics
• ☆ Astronomy
• ☆ Solar Physics
• ☆ Cosmology
• ☆ Applied Mathematics
• ☆ Mathematical Models
• ☆ Computational Sciences
• ☆ Differential Equations
• ☆ Pure Mathematics
• ☆ Calculus
• ☆ Geometry
• ☆ Discrete Mathematics
• ☆ Integral Equations

サービス

• Writing
• Research
• Data & AI

Writing Technical Writing, General Proofreading & Editing

Research Scientific and Technical Research, Systematic Literature Review, Secondary Data Collection

Data & AI Data Visualization, Big Data Analytics, Data Processing, Data Insights

職務経験

Researcher

University of Rochester

8月 2023 - 現在

Research Intern

Leibniz Institute for Astrophysics Potsdam

5月 2022 - 現在

Research Fellow

Center for Astrophysics Harvard & Smithsonian

9月 2024 - 8月 2025

Workshop Fellow

Los Alamos National Laboratory

6月 2023 - 8月 2023

学歴

Ph.D. (Physics)

Johns Hopkins University

7月 2025 - 現在

Physics and Astronomy, B.S. (Physics and Astronomy)

University of Rochester

8月 2020 - 5月 2024

(Physics)

University of Sydney

2月 2023 - 6月 2023

認定資格

• 認定資格の詳細は未入力です。

出版物

JOURNAL ARTICLE

E Vislosky, Ivan Minchev, S Khoperskov, M Martig, Tobias Buck, T Hilmi, Bridget Ratcliffe, J Bland-Hawthorn, Alice Quillen, M Steinmetz, et al. (2024). Gaia< DR3 data consistent with a short bar connected to a spiral arm . Monthly Notices of the Royal Astronomical Society.

REPORT

Modeling Dynamic Behavior of Stainless Steel (SS304L) [Slides] @article{osti_1995121, title = {Modeling Dynamic Behavior of Stainless Steel (SS304L) [Slides]}, author = {Vislosky, Ethan Langford and Baker, James Andrew Jr. and Nguyen, Thao Thi Tam and Plohr, JeeYeon Nam}, abstractNote = {Abstract not provided.}, doi = {10.2172/1995121}, url = {https://www.osti.gov/biblio/1995121}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {8} } . Los Alamos National Laboratory.