Hello, I'm
Pursuing research at the intersection of mathematics, machine learning, and physical intelligence — from frontier model development to embodied AI and brain–computer interfaces.
Computer Science & Mathematics undergraduate at the University of Manchester, aiming for AI research at frontier labs and working toward building the next generation of intelligent systems.
I co-authored a research paper on nonlinear time-series forecasting for air pollution using chaos theory and statistical modelling, presented at the CHAOS 2025 International Conference in Greece. The work deepened my expertise in dynamical systems, probability theory, and data-driven analysis.
At Manchester Stinger Motorsports, I develop vision-informed control algorithms for an autonomous Formula Student race car — improving trajectory tracking accuracy by 80% and reducing response latency by 18 ms through data-driven optimisation and telemetry analysis.
During my internship at BASARSOFT, I engineered large-scale data validation pipelines for 21 million geospatial records, improving data integrity across 3 systems by 40%. I also completed an intensive ML, AI & Data Science summer programme at Sabancı University.
My broader interests span embodied AI, neuromorphic and biological computing, and hardware–software co-design — with quantum computing as a long-term horizon. I want to be at the edge of what's possible and help build it.
Modelling system sensitivity and chaotic dynamics across environmental datasets spanning 5 cities and 3 years of observations. Built reproducible Python pipelines covering automated preprocessing, statistical feature extraction, and visualisation across 5 dataset variants — increasing iteration efficiency by 40%.
Chaos and learning sit closer than most people think. Both ask how small perturbations propagate through nonlinear systems; both depend on the geometry of high-dimensional trajectories. The paper was an excuse to live inside that question for fifteen months.
"Real systems don't apologise for being nonlinear. The challenge is to model them honestly."
Pulling these intuitions into representation learning and embodied control — where the dynamics aren't a dataset, they're the agent.
Co-founder & CTO. Mobile-first AI chatbot retrieving real Turkish court decisions via YargiMCP and generating grounded answers with Claude API. JWT auth, per-user history, court document viewer. Cloud assistant powered by vLLM + Gemma 4.
End-to-end ML pipeline for financial time-series prediction. Feature engineering on technical indicators, gradient-boosted models, and a custom evaluation framework that surfaces confidence intervals instead of point estimates.
ML-informed control algorithms for an autonomous Formula Student car. Hyperparameter sweeps, telemetry analysis from 40+ runs, and a leaner ROS perception–planning–control pipeline — 18 ms faster end-to-end.
Reproducible Python research pipelines for nonlinear time-series across 5 environmental datasets — automated preprocessing, statistical feature extraction, visualisation. Underpinned the CHAOS 2025 paper.
Context-aware retrieval & ranking pipeline for music. Tag-based candidate generation, lightweight ranking on weather / season signals, and a low-latency PostgreSQL schema designed for real-time scoring.
High-performance 2D physics engine with custom collision detection and resolution algorithms. Real-time kinetic energy transfer, Verlet integration-based physics, and spatial partitioning for O(n log n) collision queries.
BSc (Hons) Computer Science & Mathematics — expected First Class.
Modules: Linear Algebra · Probability · Statistics · Data Science. Research focus on frontier AI and embodied intelligence.
Intensive modules in Machine Learning, AI & Data Science.
International Baccalaureate Diploma · HL: Mathematics Analysis & Approaches, Physics.