Research + Applied Projects
Current work in turbulence, non-equilibrium phase transitions, and complexity-stability, followed by selected applied modeling projects. This page pairs research directions with implementation-heavy work in scientific and statistical modeling.
See Research for papers and project pages; download the resume for a compact summary.
Tricritical Directed Percolation in Transitional Turbulence
Research on how pipe flow transitions from laminar to turbulent when body forces are applied. The work identifies a tricritical directed-percolation point that enriches the phase diagram of transitional turbulence.
Result: published in Physical Review Letters 135, 104001 (2025), with UC San Diego news coverage. The project connects fluid mechanics with non-equilibrium phase transitions and universal scaling near turbulent onset.
- Physics: laminar-turbulent transition, body-forced shear flow, directed percolation, tricritical scaling.
- Methods: finite-size scaling, stochastic modeling, phase-transition phenomenology, statistical inference.
- Why it matters: gives a compact universality-class explanation for transition behavior that previously looked inconsistent across forcing regimes.
May's Complexity-Stability Hypothesis in Neural Networks
Project asking whether optimized neural networks behave like random complex systems, or whether training produces stabilizing structure analogous to evolved ecological networks.
Goal: test whether gradient descent and selection pressure can move high-dimensional interaction systems away from the random-matrix instability predicted by May's theorem.
- Physics/math: random matrix stability, optimized interaction systems, complex networks, dynamical stability.
- Methods: spectral analysis, trained-network diagnostics, interaction-structure statistics, comparison to ecological stability mechanisms.
- Why it matters: reframes neural networks as optimized complex systems whose stability may be governed by statistical-physics structure, not only architecture or loss curves.
Universality Class of Stratified Shear-Flow Transitions
Project on whether stable density stratification is a relevant perturbation to directed-percolation universality at turbulent onset in shear flows.
Goal: use finite-size scaling and Binder cumulants to separate finite-domain artifacts from genuine stratification-induced changes to critical behavior.
- Physics: stratified Waleffe flow, geophysical shear turbulence, laminar-turbulent onset, directed-percolation scaling.
- Methods: large-domain simulation constraints, finite-size scaling, Binder cumulants, universality-class diagnostics.
- Why it matters: connects fundamental turbulence transition theory to stratified flows relevant to oceans, thermoclines, and atmospheric boundary layers.
Puff-Slug Transitions in Pipe Flow
Near the onset of turbulence in pipe flow, localized turbulent puffs decay or split, and at higher Reynolds numbers give way to expanding slugs. This project asks what controls the transition from puffs to slugs.
- Physics: puff splitting, directed percolation, slug fronts invading laminar flow.
- Model: stochastic predator–prey models of transitional turbulence, centred on Xueying Wang's three-trophic-state model.
Structural Health Monitoring from Sensor Time Series
End-to-end pipeline on a 34.8M-entry multi-sensor dataset to detect structural damage in buildings. Multivariate LSTM predicts 4-floor acceleration response from excitation input; prediction residuals serve as damage signal.
Result: test MSE 0.00278; on heavily damaged state error rose to MSE 0.0347 (~12.5× separation), enabling clean anomaly classification. Welch PSD / FFT feature engineering + multinomial classifier achieved 85.29% accuracy across 17 structural states.
- Physics/data: vibration response, excitation forcing, and frequency-domain damage signatures.
- ML role: forecast healthy dynamics, then use residuals and spectral features as damage signals.
- Analysis: residual-based anomaly detection and spectral classification separate healthy and damaged structural states.
Graph-RNN for High-Dimensional ECG Forecasting
NSF HDR ML hackathon ($4k prize pool). Forecasts ECG signals from multi-channel neural activity. 3-layer GRU + graph convolutions with scheduled sampling; tackles out-of-distribution generalization from session-to-session distribution shift.
Result: in-distribution MSE 32,925 with R² = 0.80; beat ARIMA/ETS baselines; currently 17th on global leaderboard.
- Physics/data: high-dimensional coupled physiological time series with session-level distribution shift.
- ML role: recurrent forecasting with graph structure and scheduled sampling for stable rollouts.
- Evaluation: baseline comparison, leaderboard-style benchmarking, and tests of session-to-session generalization.
Physics-Informed ML for Laser-Plasma Accelerators
Machine Learning Researcher at TAU Systems (Carlsbad). Surrogate modeling and physics-informed prediction for electron-bunch properties from laser/plasma parameters. Sanitized writeup to follow.
- Physics/data: laser-plasma accelerator inputs and electron-bunch output properties.
- ML role: physics-informed prediction and surrogate modeling for expensive experimental/simulation regimes.
- Focus: scientific ML at the interface of accelerator physics, surrogate modeling, and experimental tuning.
Golden Handcuffs: Mortgage Rate Lock-In and Housing Supply
A step outside physics: applying the same modeling discipline to a macroeconomic question. Automated pipeline quantifying how mortgage rate lock-in affects housing supply, pulling 24 FRED macroeconomic series into a 67-variable national/state-level panel dataset.
Result: XGBoost outperformed SARIMAX by 30%+ on national RMSE and 70%+ at the state level. Lock-in effects are amplified by weak labor markets, while industry structure shapes listing volumes nonlinearly in ways tree-based models detect but SARIMAX misses.
- Data: FRED API macro series, national housing indicators, and state-level economic panels.
- ML role: PCA, LASSO, and K-means to identify latent macro structure; XGBoost and SHAP for nonlinear forecasting and interpretability.
- Modeling focus: automated data ingestion, panel-data feature engineering, econometric baselines, model comparison, and interpretable ML.