Dr. Özhan Kayacan

• Applying Machine & Deep Learning algorithms to perform Monte Carlo simulations and statistical analysis of the collective behavior of swarms/agents • Using Logistic Regression, KNN, Random Forest, Decision Tree, Bayes, Support Vector Machine, ANN, CNN, and RNN) to analyze the obtained data from computer simulations • Implementing Python codes using Jupyter, Pandas, Numpy, Scikit-learn, PyTorch

• Data extraction using SQL and Excel, implementing pre-processing • Statistical modeling using Python (Jupyter, Pandas, Numpy, Scikit-learn, PyTorch) • Data visualization, building dashboards using Excel, PowerBI, and Tableau • Using Machine Learning models such as Logistic Regression, KNN, Random Forest, Decision Tree, Bayes, Support Vector Machine, ANN, NLP, etc. • Time Series Analysis, Cohort Analysis, Basket Analysis, A/B test, Basket Analysis, Churn Prediction Analysis, etc.

• Developed and implemented swarm & artificial intelligence (AI) approach using pheromone-dependent interactions for studying predator-prey relations using Python and C++ • Application of Machine Learning algorithms to perform statistical analysis of the obtained data based on the pheromone-dependent model, Python used to perform analysis • Supervised students as a secondary reviewer in MSc Thesis

• Identifying data using SQL, Excel, Google Sheets, Python • Preparation of data for analysis (EDA) • Preprocessing using Excel, SQL, and Python • Time Series Analysis, Profitability Analysis, Seasonality Analysis, Basket Analysis, Advertising A/B Test, Effect Size Analysis • Visualization of data using Excel, Tableau, and Power BI

• Extracting data using SQL, MS Excel, and Python • Preparation of data for analysis (EDA) • Preprocessing using Python (Jupyter, Pandas, Numpy, Scikit-learn, PyTorch) • Cohort Analysis, A/B tests, Churn Prediction, Basket Analysis, Seasonality Analysis, etc. • Logistic Regression, KNN, Random Forest, Decision Tree, Näive-Bayes, Support Vector Machine, ANN, CNN, etc. • "Sentimental Analysis", and "Returns Analysis" with NLP • Data visualization with Excel, Tableau, and Power BI

• Developed and implemented AI-based models • Application of Machine Learning models to perform statistical analysis of the obtained data based on the models used in projects • Developed models for image segmentation, luminescence, liquid crystals, and nucleation-growth phenomena • Solid knowledge of Mathematical Physics, Statistical Physics, and Computational Physics • Supervised students in M.Sc. Thesis (2 students) and Ph.D. Thesis (2 students) • 29 publications in SCI journals

Performed computer simulations for defects in nematic liquid crystal displays