cv

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Basics

Name Mariia Efremova
Label Physics student · GW follow-up astronomy
Email maalefremova@edu.hse.ru
Url https://ovalb-pine.github.io/
Summary 4th-year physics student developing a pipeline for optical counterpart searches of gravitational-wave events (currently applied to GW190425) at IKI RAS, seeking to broaden experience in neutrino physics.

Work

  • 2025.09 - Present

    Moscow, Russia

    B.Sc. Thesis: Search for the Optical Counterpart of GW190425 in ZTF Data
    Space Research Institute (IKI RAS)
    Research supervisor: Alexei Pozanenko.
    • Compiled a catalog of ~27,000 potential host galaxies within the GW190425 localization area, motivated by the radio source reported by Rodin et al. (2026), and retrieved ZTF difference-image cutouts within 2 days of the trigger.
    • Built a 4-stage vetting pipeline: (1) SNR-based photometric filtering (SNR ≥ 3); (2) triplet extraction and real/bogus classification via BRAAI (Duev et al. 2019); (3) temporal vetting to exclude candidates with pre-trigger detections; (4) FWHM consistency checks against ZTF image metadata.
    • Pipeline has narrowed the ~27,000-galaxy sample to 9 surviving candidates so far, with vetting ongoing.
  • 2024.08 - 2024.08

    Moscow, Russia

    Summer Intern
    Space Research Institute (IKI RAS)
    Research supervisor: Alexander Meshcheryakov.
    • Classified HMXBs into supergiant/Be systems using a Decision Tree (Scikit-Learn), achieving 87% accuracy; produced a Corbet diagram.

Volunteer

  • 2026.10 - Present
    Educational Video Localization & Narration
    2swap (YouTube)
    Translated, adapted, and narrated 8 educational mathematics videos for a channel with 300k+ subscribers, handling full localization end-to-end, from script adaptation for a Russian-speaking audience to final voiceover.

Education

  • 2026.09 - Present
    Two-year advanced program (with scholarship)
    Lomonosov Moscow State University
    Neutrino Physics & Astroparticle Physics Program
    • Theory and experimental tracks covering neutrino physics, particle astrophysics, general relativity theory, and methods of high-energy physics
  • 2023.09 - 2027.06
    B.Sc.
    National Research University Higher School of Economics
    Physics (Major: Space Physics; Minor: Applied Programming)

Awards

Certificates

Publications

Skills

Programming & Data Analysis
Python (NumPy, SciPy, Pandas, Scikit-learn, Astropy, Matplotlib)
Jupyter
Astronomical Data
ZTF
SDSS
DESI Legacy Surveys
GLADE+
NED-LVS
CatWISE
Gaia DR3
Photometry and image processing
IRAF
Gravitational-Wave Data
GWpy
GWOSC
Signal processing and Fourier analysis
Statistical Methods
MLE
Expectation–Maximization
Regression
Optimization
Hypothesis testing
Bootstrap resampling
Computing & Tools
Linux
Git
LaTeX

Languages

Russian
Native
English
C2 (TOEFL iBT 6.0/6.0)

Projects

  • 2025.05 - 2025.05
    Practical Workshop in Optics: Michelson interferometer
    • Aligned the mirrors and measured the number of light-dark transitions while changing the optical path length.
    • Determined the laser wavelength and spectrum, the coherence lengths of two LEDs, the refractive index of a plexiglass sample, and the coefficient of thermal expansion of an aluminum rod.
    • Used Python to produce plots and perform approximations for the refractive index and rod length versus temperature.
    • Gave a 30-minute presentation on the work, supplemented by information on the working principles of LIGO/VIRGO/KAGRA/GEO600.
  • 2025.04 - 2025.04
    Mixture models / EM / Gaia cluster finding
    • Implemented MLE and Expectation–Maximization (EM) for 1D and 2D Gaussian mixtures (diagonal covariances), including convergence/regularization options.
    • Applied EM to Gaia DR3 proper-motion data (astroquery): computed membership probabilities, cluster centroids, and relative population fractions for h and χ Persei.
    • Visualized sky-position and proper-motion scatter plots colored by membership probability; annotated cluster centers and 1-sigma circles.
  • 2025.03 - 2025.03
    Computational Cosmology: optimization methods, Hubble constant, dark matter
    • Implemented Gauss–Newton and Levenberg–Marquardt optimization algorithms from scratch in Python.
    • Applied nonlinear least-squares fitting to Type Ia supernova data to infer cosmological parameters H0 and ΩΛ.
    • Performed numerical integration of cosmological distance relations using SciPy.
    • Estimated parameter uncertainties via Jacobian-based error propagation and bootstrap resampling.
    • Produced plots of distance modulus vs. redshift and optimization convergence.