CV

Education

• B.S. in Electrical Engineering, Uludag University
• M.S. in Computer Engineering, Uludag University
• Ph.D in Computer Science, Eötvös Loránd University, expected 2026

Work experience

• AI/ML Researcher ~ 9/2022 to Present, NIPG ELTE, Budapest, HU
  • AI/ML Development & Implementation: Led finetuning and deployment of advanced AI models including SAM-2, YOLOWorld and Llama 2, demonstrating expertise in model training, validation, and maintenance aligned with real-world applications
  • Data Pipeline Development: Designed and implemented end-to-end data processing pipelines for ML applications, including data cleaning, preprocessing, and feature/keypoint engineering to optimize model performance
  • Data Science & Visualization Utilized Python-based visualization libraries (Matplotlib, Seaborn, Plotly, Tensorboard) to create comprehensive analytical dashboards and performance metrics for ML models
  • Object Detection & Tracking: Experience in object detection, segmentation, and tracking, leveraging recent algorithms to enhance visual recognition systems using local/global feature descriptors
  • Model Fine-tuning & Customization: Implemented LoRA (Low-Rank Adaptation) techniques for Stable Diffusion models, enabling efficient fine-tuning and customization of image generation capabilities while maintaining model quality with reduced computational requirements
• Research Assistant: 6/2019 to 9/2022, Uludag University, Bursa, TR
  • Algorithm Optimization: Optimized the PRNU grid-search algorithm to enhance the accuracy and efficiency of image processing tasks, resulting in significant performance improvements
  • Artificial Intelligence Research: Conducted extensive research and successfully implemented various artificial intelligence technologies

Skills

• Programming Languages & Frameworks
  • Python Pytorch TensorFlow, Scikit-Learn, C#
• Data Science & Visualization:
  • MatPlotLib, Numpy, Pandas, MS Excel, IBM SPSS
• Computer Vision:
  • OpenCV, Pillow
• Development Tools & Environment
  • Git version control
  • Docker, Apptainer
  • Anaconda
  • Linux, Bash scripting

Publications

A new strategy for curriculum learning using model distillation

K. Karakose and M. Bilgin (2020). "“A new strategy for curriculum learning using model distillation”." Global Journal of Computer Sciences: Theory and Research.

Teaching

Affective Computing (Practice)