About
About Me
Hello! I’m Deniz Akdemir, a data scientist and researcher specializing in machine learning and biostatistical analysis. My work focuses on developing innovative approaches to extract meaningful insights from complex datasets, particularly in clinical and healthcare domains.
Research Metrics
- 3548 Citations
- 24 h-index
- 31 i10-index
Research Interests
- Machine Learning Applications: Implementing advanced models for real-world problems, with a focus on tabular data analysis
- Anomaly Detection: Developing methods using autoencoders and rule-based techniques to identify patterns and outliers
- Clinical Data Analysis: Creating tools for healthcare data interpretation and quality assurance
- Statistical Modeling: Applied statistical analysis for evidence-based decision making
Expertise
My technical toolkit includes:
- Languages & Frameworks: Python, PyTorch, scikit-learn
- Data Analysis: Statistical modeling, exploratory data analysis, feature engineering
- Machine Learning: Deep learning, transformer models, rule mining, anomaly detection
- Visualization: Data storytelling through effective visualizations
Current Work
I’m currently exploring the application of transformer architectures to tabular data and developing near-deterministic rule mining techniques for anomaly detection in clinical datasets. My work aims to bridge the gap between theoretical machine learning and practical applications in healthcare and biostatistics.
Connect With Me
Feel free to contact me with questions about my research, collaboration opportunities, or to discuss potential projects. You can also check out my resume for more details about my professional background.