Mohamed Rodani

Mohamed Rodani

Physics @ QMUL • Machine Learning, Quant Finance & Scientific Computing

Turning messy problems into clear models and readable explanations. Rigor-first approach to building strong fundamentals through consistent learning and hands-on practice.

Physics Machine Learning Quant Finance Scientific Computing Rigor-first Open-source / GitHub
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Quick Facts

Education Physics @ QMUL
Interests
ML Quant Scientific Computing
Currently Deepening fundamentals + building portfolio
Open to Internships + collaborations
Featured Projects 0+ projects Competition Results 1st, 2nd & Top 25% ML & Quant Focus Python, PyTorch

About

I'm a Physics student at Queen Mary University of London (QMUL), working at the intersection of mathematics, physics, machine learning, and quantitative finance. I build end-to-end ML projects with rigorous evaluation and reproducible results.

  • Focus: End-to-end ML systems, financial sentiment analysis, and quant frameworks
  • Build: From algorithms (backprop from scratch) to production apps (full-stack ML)
  • Practice: Kaggle competitions, hackathons, and collaborative projects through QMUL ML Society
  • Seeking: ML/quant spring weeks and internships for 2026–2027

Highlights

QMUL ML Society Christmas Hackathon - 2nd Place

2nd place finish predicting customer return behaviour using strategic feature engineering (17 features beat 55+ one-hot encoded)

Kaggle Top 25% - Loan Default Prediction

Ranked #950/3,850 in S5E11 with 0.92450 AUC using LightGBM + Optuna optimization

BERT Financial Sentiment Analysis

Fine-tuned BERT model achieving 87% test accuracy with improved negative sentiment recall

Projects

Featured Projects

Top projects with detailed case studies

Base Gas Optimiser - Hackathon Winner

Full-stack ML application that predicts Base network gas prices using scikit-learn models, helping users save on transaction fees. Built with React/TypeScript frontend, Python Flask backend, and real-time blockchain data integration. Features live gas indicators, 24-hour price predictions, and a savings calculator with MetaMask wallet support.

Hackathon Winner Up to 40% gas savings Full-stack ML app
Case Study GitHub
Base Gas Optimiser - Gas Price Prediction Dashboard
Python Flask React TypeScript Scikit-learn Blockchain

BERT Financial Sentiment Analysis

Fine-tuned BERT model (110M parameters) on financial news headlines labeled as positive, negative, or neutral. Achieved balanced F1 scores across all sentiment classes and improved negative class recall from 28.6% to 85.7% through weighted loss functions and early stopping, demonstrating strong performance on previously underperforming class.

87% test accuracy 85.7% negative class recall 5,842 headlines
Case Study GitHub
BERT Financial Sentiment Analysis - Confusion Matrix and Performance Metrics
Python PyTorch Transformers NLP FinBERT

QMUL ML Society Christmas Hackathon - 2nd Place

2nd place solution predicting customer return behaviour during holiday shopping. Used strategic feature engineering with 17 curated features (aggregation, temporal, original) that outperformed baselines with 55+ one-hot encoded features. Simple LogisticRegression model proved that quality features beat model complexity.

2nd Place 8,000 transactions 17 curated features
Case Study GitHub
Python Scikit-learn Feature Engineering Classification

More Projects

Additional projects and ongoing work

RYM Investment Framework

Quantitative investment strategy framework for UK Investment Challenge 2025. Systematic approach to portfolio construction with risk analysis and performance metrics. Built complete data pipeline for quantitative asset analysis.

Case Study GitHub
RYM Investment Framework
Python Quantitative Finance Data Pipeline

Kaggle Playground Series - Top 25%

Monthly Kaggle Playground Series competitions. November 2025 (S5E11): Loan Default Prediction - achieved Top 25% (#950/3,850) with 0.92450 AUC using LightGBM with Optuna optimization and multi-seed averaging on 593,994 training samples.

Top 25% (Nov 2025) 0.92450 AUC 593K samples
Case Study GitHub
Kaggle Playground Series Overview
LightGBM Optuna Kaggle Classification

ML Mini Projects

Collection of focused ML experiments building core intuition: Backpropagation from Scratch (NumPy implementation), regression models, classification algorithms, and other foundational ML techniques. Each mini-project focuses on understanding the mechanics behind common algorithms.

Machine Learning Mini Projects
Python Machine Learning Data Science

Heat Pump Explained

Educational hackathon project explaining heat pump technology for non-technical audiences. Combined physics principles with computational modelling and interactive visualizations to make thermodynamics concepts accessible.

Case Study GitHub
Heat Pump Explained
Python Physics Data Visualization

Experience

December 2025

Coinbase Hackathon 2025 - Winner

  • Built full-stack ML application for Base network gas price prediction in a team, helping users optimize blockchain transaction fees through real-time price forecasting
  • Developed scikit-learn models integrated with React/TypeScript frontend and Python Flask backend for seamless user experience
  • Implemented real-time blockchain data integration with live gas indicators and 24-hour price predictions
  • Created savings calculator with MetaMask wallet support, enabling users to quantify potential cost savings
  • Delivered production-ready solution demonstrating end-to-end ML pipeline from data collection to deployed application
November 2025

Diverse AI Hackathon 2025

  • Built AI transparency engine for smart heat pump systems in a 5-person team, addressing trust barriers in home energy automation through pattern detection and natural language explanations
  • Developed rule-based classifier identifying 5 operational scenarios (tariff optimisation, VPP curtailment, predictive heating, efficiency anomalies) with 3-tier recommendation system
  • Integrated Claude Sonnet 4 API with <50 word constraint, converting technical metrics (COP, flow temps, grid signals) into actionable insights
  • Grounded solution in EoH dataset (742 UK installations, 2+ years monitoring data) for realistic COP/SPF simulation
  • Delivered Streamlit dashboard with live Q&A chatbot, data transparency features, and benefit quantification (£ + kg CO2)
2023

WCIT Hackathon - 4th Place

Team Member

Competed in hackathon team, developing innovative solution. Secured 4th place finish among competing teams through technical excellence and collaborative problem-solving.

2022

Tesco Group Competition - 1st Place

Team Member / Lead

Won first place in competitive challenge. Led key technical contributions and collaborated with team to deliver winning solution through innovative approach and strong execution.

2025 - Present

QMUL Machine Learning Society - Quant Division

Active Member

Active participant in Machine Learning Society's quantitative finance division. Engaging in discussions on quantitative trading strategies, machine learning applications in finance, and collaborative projects.

Education

2025 - Present

Physics Degree

Bachelor of Science in Physics

Skills gained from studying Physics include mathematical modelling, computational methods, problem-solving, and analytical thinking.

2022 - 2024

A Levels

Mathematics, Further Mathematics, Physics

Completed A Levels in Mathematics, Further Mathematics, and Physics, building a strong foundation in quantitative and analytical skills.

2022

GCSEs

4 Grade 8s, 3 Grade 7s, 2 Grade 6s

Completed GCSEs with strong results across multiple subjects.

Skills

Proficient Comfortable Learning

Languages & Tools

Python NumPy Pandas Matplotlib Seaborn Scikit-learn Git Jupyter PyTorch XGBoost LightGBM TensorFlow

Machine Learning

Supervised Learning Tree-Based Models Deep Learning NLP & Transformers Computer Vision Feature Engineering Time-Series PINNs

Mathematics & Theory

Linear Algebra Calculus Probability & Statistics Differential Equations Mathematical Modelling

Contact

Seeking ML/quant spring weeks and internships for 2026–2027. Open to ML hackathons and collaborative projects. If you're recruiting in machine learning, quantitative finance, or running ML-focused events, I'd love to connect.

GitHub LinkedIn

GitHub Activity

My contribution activity

GitHub Contributions Chart for M-Rodani1