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Research Scientist

Google

• Analyze large-scale training runs for metric anomalies that may be caused by data contamination.
• Develop improved decontamination recipes and test-train overlap metrics to identify contamination early and increase evaluation signal-to-noise ratio.
• Study how training stages affect performance, memorization, and generalization.
• Improve task-specific, efficient small language models using synthetic data.

PhD Research Fellow

Boston University Artificial Intelligence Research Group

• Studied natural language evaluation, with an emphasis on data contamination and its effect on reported model performance.
• Improved sentence embeddings, compositional generalization evaluation, and cross-lingual representations through augmentation, multitask learning, and contrastive learning.
• Conducted temporal bias research funded by the 2021 Google Research Scholar Program.
• Led an Ottoman handwritten text recognition project that received $50K from the Scientific Research Council of Turkey.

Research Scientist Intern

Google

• Quantified the effect of data contamination on LLM performance and evaluation reliability.
• Built scalable experimental frameworks to measure contamination impact and improve evaluation data selection.
• Host: Daniel Deutsch.

Research Scientist Intern

FAIR, Meta

• Developed methods to define and detect contamination between evaluation sets and LLM pretraining data.
• Analyzed performance overestimation caused by contamination; the methodology and code were used in contamination analysis for Llama 3.1.
• Host: Dieuwke Hupkes.

Applied Science Intern

Amazon

• Evaluated and improved compositional generalization in semantic parsing models.
• Extended TMCD splits for more robust convergence and studied meta-learning and chain-of-thought prompting strategies.
• Host: Emilio Monti.

Co-Founder and CEO

TRIA AI

• Led business development, closed contracts worth more than $100K, and advised enterprise clients on AI strategy.
• Deployed customer segmentation, lifetime value, and churn models that doubled targeted-ad conversion rates for LC Waikiki.
• Improved illegal-electricity-use detection by 150% for DEDAS, with an estimated $500K business impact.

PhD

Boston University

Computer Science

MSc

Bogazici University

Electrical and Electronics Engineering

BSc

Bogazici University

Electrical and Electronics Engineering