Resume

ABOUT

Dr. Ozan Önal is an expert, researcher, and auditor working in the fields of artificial intelligence, data science, machine learning, explainable artificial intelligence, trustworthy AI, and the evaluation of AI systems.

His professional work focuses on the reliability of artificial intelligence systems, data governance, risk management, model evaluation, standardization, and conformity assessment.

In his academic research, he applies machine learning and explainable artificial intelligence methods to scientific problems, with a particular focus on data-driven modeling, prediction, validation, and interpretability.

# AREAS OF EXPERTISE

• Artificial Intelligence
• Machine Learning
• Data Science
• Deep Learning
• Explainable Artificial Intelligence (XAI)
• Large Language Models (LLMs)
• Retrieval-Augmented Generation (RAG)
• Image Processing and Computer Vision
• Trustworthy and Responsible AI
• AI Risk Management
• Data Governance
• Model Validation and Evaluation
• AI Governance
• AI Standardization
• Statistical Model Evaluation
• Biometric AI Systems

He conducts technical, managerial, and standardization-oriented work in the fields of artificial intelligence and data science.

His main areas of work include:

• Technical evaluation of artificial intelligence systems
• Trustworthy and responsible artificial intelligence
• Assessment of AI-related risks
• Data governance
• Model and data documentation
• Human oversight
• Validation of artificial intelligence systems
• Machine learning and data analytics
• Review and implementation of artificial intelligence standards
• Preparation of AI-focused technical guidelines and evaluation frameworks

# TRUSTWORTHY AND RESPONSIBLE ARTIFICIAL INTELLIGENCE

A significant part of his work focuses on evaluating artificial intelligence systems not only in terms of performance, but also from the perspectives of reliability, explainability, data quality, risk, human oversight, and governance.

Within this scope, he works on:

• Trustworthy Artificial Intelligence
• Responsible Artificial Intelligence
• Data Governance
• AI Risk Management
• Human Oversight
• Model Documentation
• Data Quality
• Evaluation of Artificial Intelligence Systems

# ARTIFICIAL INTELLIGENCE AND STANDARDIZATION

He follows national and international artificial intelligence standardization activities and contributes to technical studies related to the evaluation of artificial intelligence systems.

He contributes to the development of standards, guidelines, and technical documentation aimed at making artificial intelligence systems more trustworthy, measurable, and assessable.

His work particularly focuses on the following dimensions of AI systems:

• Lifecycle management
• Data management
• Model development
• Risk analysis
• Performance evaluation
• Explainability
• Human oversight
• Technical documentation

# RESEARCH AND MACHINE LEARNING STUDIES

In his doctoral research, he worked on predicting and analyzing the ionic conductivity of LLZO-based solid electrolytes using machine learning and explainable artificial intelligence methods.

Within this research, he applied:

• Regression models
• Random Forest
• Gradient Boosting
• XGBoost
• Support Vector Regression
• Ensemble methods
• Synthetic data generation
• Model performance analysis
• SHAP-based explainable artificial intelligence

His research focuses not only on predictive accuracy, but also on understanding model behavior and explaining the impact of individual variables on model predictions.

# VALIDATION OF ARTIFICIAL INTELLIGENCE SYSTEMS

One of his research interests is the statistical and technical validation of artificial intelligence systems used in high-reliability environments.

He particularly works on statistical evaluation approaches for biometric systems, face comparison systems, and the validation of extremely low error rates.

These studies aim to evaluate model performance not only through average performance metrics, but also by considering real-world risks, uncertainty, and the probability of errors under operational conditions.

# RESEARCH INTERESTS

• Artificial Intelligence
• Machine Learning
• Explainable Artificial Intelligence
• Trustworthy Artificial Intelligence
• AI Governance
• AI Risk Management
• Large Language Models
• RAG Systems
• Image Processing
• Biometric Artificial Intelligence Systems
• Model Validation
• Data Science
• AI Standardization
• Application of Artificial Intelligence in Scientific Research

# APPROACH

He approaches artificial intelligence not merely as the development of high-accuracy algorithms, but as a holistic system problem encompassing data, model development, validation, explainability, reliability, risk management, and governance.

By combining academic research, applied artificial intelligence, data science, and standardization experience, he continues his work on the development and evaluation of trustworthy, explainable, and assessable artificial intelligence systems.