About
I work on large-scale signal detection problems involving extremely low-signal-to-noise experimental datasets, developing methods to identify rare high-frequency candidate events. My research combines theoretical modeling with production-style data-analysis pipelines, including matched filtering, phase-coherent signal processing, machine-learning-assisted detection methods, and robust spectral estimation techniques. I specialize in building scalable Python-based workflows for extracting weak signals from noisy data in complex sensor systems.
Selected publications
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Dark-sector modifications to Kerr and Reissner–Nordström black hole evaporation
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Constraints on the maximal number of dark degrees of freedom from black hole evaporation, cosmic rays, colliders, and supernovae
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Precision gravity constraints on large dark sectors
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Maximal gravitational wave signal from asteroid-mass primordial black hole mergers at resonant microwave cavities
Research Software & Analysis Pipelines
Resonant Microwave Cavity Signal Processing
End-to-end analysis pipelines developed in collaboration with ADMX scientists to search for MHz–GHz gravitational waves using resonant microwave cavity detectors.
- Phase-preserving FFT binning, receiver-response flattening, and absolute frequency calibration
- Welch-based PSD estimation and robust stacking across large ensembles of high-resolution scans
- Frequency-domain matched filtering for narrowband and quasi-monochromatic signals
- Open-source analysis code: github.com/cewasiuk
Modeling, Simulation & Inference
Theory-to-code workflows for black-hole evolution, gravitational-wave phenomenology, and detector sensitivity studies.
- Numerical evolution pipelines (ODE solvers, interpolation, parameter scans)
- Statistical modeling and noise-aware inference for experimental sensitivity estimates
- Machine-learning project: CNN / U-Net tumor segmentation and severity grading (Erdős Institute capstone)
Technical Expertise
Data Analysis & Signal Processing
- Matched filtering and optimal detection statistics
- Power spectral density estimation (Welch, median / robust stacking)
- Fourier-domain methods (FFT / rFFT), binning, resampling
- Noise modeling, stationarity testing, statistical inference
Scientific Computing & Modeling
- Numerical simulation pipelines and parameter sweeps
- ODE-based evolution models and numerical integration
- Monte Carlo methods and uncertainty propagation
- Metadata-aware workflows and large-scale data aggregation
Machine Learning & Data Science
- Deep learning with CNN and U-Net architectures
- Image segmentation and classification (MRI data)
- Model evaluation (Dice, IoU, ROC, calibration)
- Uncertainty quantification and robustness analysis
Programming & Research Tools
- Python (NumPy, SciPy, pandas, PyTorch, PyCBC, Matplotlib)
- FFTW / pyFFTW, HDF5, YAML / JSON
- Git / GitHub, Linux / Unix environments
- Scientific writing and documentation (LaTeX)
Contact
Best way to reach me is email. Links to LinkedIn and GitHub are below.