I am a physicist working on nonlinear physical systems, with a primary focus on morphology, interfacial phenomena, and computational modelling. My work combines statistical physics, continuum modelling, and machine learning approaches to study complex evolving structures and patterns.

• 🔬 My research focuses on:

  • Statistical and Nonequillibrium Physics
  • Wetting, Interfaces, and Morphological Evolution
  • Computational and Variational Modelling
  • Scientific Machine Learning for Physical Systems

• 📊 I am particularly interested in combining physics-based models with data-driven approaches for analysing evolving morphologies and extracting physically meaningful representations.

• 🤝 I am open to collaborations in soft matter physics, nonlinear systems, computational modelling, and scientific machine learning.

• 📫 Reach me at: hisaylama@gmail.com

Here are some of my notable projects:

1. Credit Risk Analytics & Real-Time Scoring (LendingClub Case Study) - Built an end-to-end pipeline for feature engineering, model training, and a live scoring web-app. [Data Science in Finance]

2. Graph-Based Pattern & Anomaly Detection from Images - Converted images to networks to detect endpoints/junctions; can be framed as graph analytics for fraud/risk signal discovery. [Quantitative Image Processing]

3. Interactive Analytics App for High-Dimensional Data (Chemical Fingerprint) - Developed a GUI to slice, filter, and visualize large matrices, analogous to building BI tools for portfolio/credit dashboards. [Soft-Matter Physics/Analytical Chemistry Analysis]

4. Optimization-Driven Signal Reconstruction (Phase retreival algorithm) - Implemented inverse-problem methods to recover missing/noisy signals for optical holography; transferable to data imputation and time-series smoothing in finance. [Quantitative Image Processing]

5. Monte Carlo Simulation (Brownian motion) - Built stochastic simulators (Langevin/agent models) to study ecological problem -> transfareable to generate scenarios applicable to VaR, liquidity, and credit stress testing. [Statistical Physics and Thermodynamics/Machine Learning]

6. Dynamic Phase Transitions in Non-Equilibrium Systems (Nonequillibrium Physics) Converted noisy microscopy images into quantitative signals via a MATLAB→Python pipeline (denoising, segmentation, feature extraction); modeled dynamics, detected jamming transitions; published in PNAS Nexus. Code/Data · [Statistical Physics and Thermodynamics]
   Media coverage: ScienceDaily · Phys.org · Bioengineer.org · University of Tokyo

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Thank you for visiting my profile! If you share similar interests or have exciting collaboration opportunities, feel free to get in touch.