About

I am an Applied Researcher focusing on large language model (LLM) reasoning, distillation, efficient model systems, and dependable evaluation. I enjoy carrying a research question from its assumptions and experiments through the engineering decisions required to make the result useful.

My research path began in scientific and physics-informed machine learning, uncertainty quantification, and engineering design. It now extends to foundation models, including critique-guided distillation, policy distillation, adaptive inference, mixture-of-experts (MoE) compression, and methods for retaining model capabilities during adaptation.

Education

I completed my Ph.D. in Civil Engineering at Vanderbilt University. My doctoral work developed computational methods for decision-making and engineering systems under uncertainty.

Before Vanderbilt, I earned an M.S. in Applied Mathematics from Delft University of Technology and an M.S. in Computational Engineering from the University of Erlangen-Nuremberg. I began my training with a B.S. in Mechanical Engineering from Bilkent University.

Academic Interests

• Language model reasoning and distillation
• Efficient model systems and mixture-of-experts models
• Adaptive inference and agentic evaluation
• Trustworthy evaluation and capability retention
• Scientific and physics-informed machine learning
• Uncertainty quantification and engineering design