I am an physicist with a deep interest in statistical physics, machine learning, and quantitative research.

• 👀I’m passionate about complex systems - from Statistical Physics to Finance - driven to uncover novel insights and meaningful metrics.
• 🌱 My primary research areas include:
  • Quantitative Research: Utilizing statistical and machine learning methods to analyze and interpret complex data sets.
  • Physics: Investigating the properties of nonerquillibrium systems.
  • Quantitative Image Processing: Developing and applying techniques to extract meaningful data from images.
• I’m eagerly looking forward to collaborations that can push the boundaries of our understanding in complex systems.
• 📫 You can reach me at: hisaylama@gmail.com

• Statistical Physics and Thermodynamics
• Quantitative Image Processing
• Quantitaive modelling
• Machine Learning

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 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

• LinkedIn

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