Get To Know More

About Me

Education

Istanbul Technical University
BSc in Artificial Intelligence
and Data Engineering

Welcome to my personal website! 👋 I am Berke Kurt, an Artificial Intelligence and Data Engineering undergraduate at Istanbul Technical University. My academic journey has been fueled by a deep passion for exploring the transformative potential of AI, particularly in the realm of deep learning. I constantly engage in creating personal projects and conducting independent studies to deepen my understanding. Feel free to explore my work, and don't hesitate to reach out—I'd be thrilled to connect and explore how we can create something amazing together!

Browse My Recent

Projects

Multilingual Idiom Identification (Turkish & Italian)

A project focused on token-level identification of idiomatic multi-word expressions (MWEs) in Turkish and Italian sentences using a transformer-based sequence labeling pipeline. Key innovations include:

Training on a combined multilingual corpus to improve coverage and generalization across languages.
Language-specific BERT encoders with feature fusion from the last hidden layers, followed by a BiLSTM to capture long-range context.
CRF decoding to enforce coherent BIO tag sequences and recover complete idiom spans.
Two-phase optimization: task-specific training with frozen BERT, then gradual layer-wise unfreezing with reduced learning rates to fine-tune stably.
Achieved 90 overall F1 in the course competition, demonstrating strong performance on morphologically rich language data.

Distributed Image Similarity Search

A big data project focused on detecting duplicate and near-duplicate images at scale for automated plagiarism/copyright checks, using deep image embeddings and distributed processing. Key innovations include:

Extracting semantic image representations with DINOv3 and comparing them using cosine similarity for robust near-duplicate detection.
Precomputing and L2-normalizing embeddings, then storing them in Parquet for fast, scalable Spark workflows.
Designing a distributed pipeline in Apache Spark and Google Cloud Dataproc that supports top-k retrieval and threshold-based flagging.
Benchmarking across multiple dataset sizes (up to ~157K images) and showing that distributed similarity search stays fast as the archive grows, enabling near real-time retrieval.

Predictive Analytics for E-commerce Product Replenishment

A project aimed at forecasting customer repurchase timing in e-commerce using machine learning techniques. Key innovations include:

Utilization of XGBoost and CatBoost models for predicting repurchase windows.
Advanced feature engineering, including temporal analysis and product grouping via Jaccard similarity.
Application of over 1 million transactional records to optimize inventory management and supply chain operations.

Emotion Representation in TTS Models

A project focused on improving the emotional expressiveness of Text-to-Speech (TTS) systems by integrating emotion embeddings into the synthesis pipeline. Key innovations include:

Leveraging StyleTTS2's diffusion-based architecture to generate high-quality, emotionally stable speech.
Incorporating an emotion discriminator trained on datasets like RAVDESS to refine emotional accuracy in synthesized audio.
Application of diverse datasets (RAVDESS, LibriTTS) for robust emotional modeling.
Introducing emotion labels into the TTS pipeline and optimizing their integration using cross-entropy loss to enhance emotional fidelity in synthesized speech.

Database Design and Implementation

A project aimed at designing a robust database system for music streaming platforms to manage vast amounts of data related to artists, albums, songs, playlists, and user interactions. Key features include:

Development of a RESTful API using Flask-RESTX with SQLite backend, JWT-based authentication, and modular architecture for secure and efficient data management.
Comprehensive CRUD operations for managing entities like users, playlists, songs, and albums, ensuring seamless interaction with the database.
Integration of advanced relational modeling to maintain data integrity and enable complex queries like tracking user engagement or calculating total listening time per album.

Berke Kurt

About Me

Education

Areas of Interest

Deep Learning

Natural Language Processing

Speech and Audio Processing

Data Mining

Cognitive Neuroscience

Game Theory

Algorithms

Database Systems

Information Retrieval & RAG

Big Data

MLOps