Hello! I'm Emre.
I'm a Computer Science undergraduate with a strong foundation in Python and C++, and a keen interest in computer engineering and CPU architecture. I'm looking to apply my technical skills in a role bridging software development and computer engineering.
Beyond coding, I enjoy playing tennis, piano, and being terrible at chess.
Education
University of Exeter | BSc Computer Science
Expected Graduation: 06/2027
- On track for First Class honours
- Relevant modules include: Fundamentals of Machine Learning, Data Structures and Algorithms, Object-Oriented Programming
- Achieved 4th place at the Exeter hackathon
Imperial College London | BEng Electrical Engineering
Course not complete
- Object-oriented programming in C++
- Digital Electronics and Computer Architecture
- Group design project - remote control server
Lycée Francais Charles de Gaulle | French baccalaureate
2015 - 2022
- Specialty subjects: Mathematics, Physics, Chemistry
- Graduated with distinction (félicitations du jury) 18.21/20
Technical Skills
- Programming Languages: Python (advanced), C++ (advanced), Java (intermediate)
- Software Development: Object-oriented programming, version control (Git), test-driven development, code optimization
- Computer Science: Data structures (arrays, trees, graphs, hash tables), algorithms (sorting, searching, graph traversal), complexity analysis
- Development Tools: Visual Studio Code, Jupyter Notebook, gdb/g++
- Web Development: Flask, HTML/CSS, web application architecture
- Mathematics: Linear algebra, statistics, probability theory, discrete mathematics
- Project Experience: Custom simulators, algorithm implementations, competitive programming
- Machine Learning & Data Science: See dedicated section below for skills in this area
Machine learning & data science skills
- Regression models: implemented linear and polynomial regression with feature selection and visualization
- Classification: built perceptron and logistic regression classifiers for MNIST and CIFAR10 datasets
- Clustering algorithms: applied k-means and DBSCAN with Silhouette and DB scoring metrics
- Dimensionality reduction: compared PCA, t-SNE, and UMAP for visualization and ML preprocessing
- Performance analysis: evaluated models using precision/recall metrics beyond simple accuracy
- Data preparation: performed feature engineering, normalization, and exploratory analysis