
Second-year Biomedical Engineering student at Istanbul Medipol University with interests in protein engineering, oncology, and neuroengineering. My BSc combines life sciences and engineering fundamentals, including biology, chemistry, physics, and core engineering topics such as signal processing and feedback systems.
I have many hands-on laboratories experiences in molecular and cell biology techniques (PCR, electrophoresis, cell culture, protein assays, microscopy, cancer screening) and experience in EEG signal analysis, including FFT-based processing and basic machine learning applications in neuroscience.
I also use Python, MATLAB, OOP, and C/C++ for data analysis and problem-solving in biomedical contexts.
My interests are focused on AI-driven biomedical data analysis, particularly in neuroengineering and computational biology.
Currently seeking internship or research opportunities to further develop practical experience in biomedical engineering.
Cellular and molecular biology lab work:
Biochemistry lab work:
General Chemistry lab work:
Cognia & International Baccalaureate
Score: 39/45 (≈98.9%)
Completed a strong pre-university diploma program with a strong focus on languages, sciences, mathematics, and analytical thinking.
Subjects:
Core Components:
Main Goals Achieved:
PYTHON
C/C Programming
MATLAB
Assembly XC8
Wolfram Alpha
Literature Review and Synthesis
Scientific writing and reporting
Presentation and communication of scientific concepts
Experimental design and analysis
Problem-solving and critical thinking
EEG signal analysis (FFT-based processing)
Introduction to machine learning in biomedical data
Signal processing fundamentals
Systems thinking in biological engineering
Applied convolutional neural networks (CNNs) for classification of EEG signals in neuroscience research contexts
Machine learning techniques for biomedical signal analysis, including EEG-based pattern recognition
EEG signal analysis (time-domain and frequency-domain)
Data visualization (Matplotlib)
Statistical analysis of biomedical datasets
Protein structure and mutation analysis (conceptual / research-based)
Genotype–phenotype relationship modeling
Bioinformatics fundamentals
Biological data interpretation using computational tools
Handling structured datasets (CSV, lab data)
Feature scaling / normalization
Hypothesis thinking