My research focuses on conflict and peace, with an interdisciplinary emphasis on climate change and sustainability. I integrate conventional machine learning techniques and Transformer-based AI models to predict civil conflict and analyze peace agreements.

Methodologically, I combine traditional statistical methods with classical NLP, machine learning, and advanced Transformer-based AI approaches. My contributions have been recognized with the Open Science Foundation Award.

My research has been published in Political Research Quarterly, Foreign Policy Analysis, Problems of Post-Communism, Nations and Nationalism, Asia-Europe Journal, Caucasus Survey, and Middle East Policy.

My current projects include:

• Leveraging large language models (LLMs) for qualitative research.
• Exploring generative AI adoption in academia.
• Conducting survival analysis of ceasefires and war recurrence.
• Applying survival-based machine learning approaches to forecast civil war recurrence.
• Using models like BERT and T5 to predict the success of peace agreements based on their textual content.
• As part of my book project, I develop custom Transformer-based language models, including fine-tuned versions of BERT and RoBERTa, to analyze homophobic political and media discourse in authoritarian regimes.