I am a Research Scientist at Google DeepMind in London, working in the Privacy and Security team. I am finishing up my PhD in Computer Science as an ELLIS student at CIS at LMU Munich and LTL at the University of Cambridge, advised by Prof. Hinrich Schütze and Prof. Anna Korhonen. During my PhD, I also had the opportunity to complete research internships at Google in Mountain View and Amazon in Madrid.

My research focuses on improving LLM capabilities through effective data utilization and synthetic dataset generation, with a particular emphasis on corpus-mining, counterfactuality, robustness, privacy, and multilinguality. Below are key questions and findings from my work:

• Data repurposing: How to generate high-quality synthetic datasets with LLMs?
  • Introduced reverse instructions to repurpose existing human-written texts for instruction tuning, improving long-form output quality.
  • Developed MURI (Multilingual Reverse Instructions), creating instruction-tuning datasets for 200 languages by repurposing multilingual human-written corpora.
  • Co-developed CRAFT, a method for generating task-specific synthetic datasets by retrieving and rewriting relevant documents from large-scale corpora.
• Counterfactuality & Robustness: How to effectively create counterfactual datasets and improve model robustness/capabilities?
  • Generated a counterfactual open-book QA dataset by utilizing hallucination in LLMs, demonstrating improved faithfulness across various QA datasets.
  • Created a counterfactual document-level relation extraction dataset, improving consistency in relation extraction models.
  • Investigated the high variance in few-shot prompt-based fine-tuning, proposing ensembling and active learning techniques for more robust finetuning.
• Multilinguality & Bias: Contributing to multilingual NLP and bias recognition.
  • Designed one of the first multilingual relation extraction datasets covering six languages.
  • Demonstrated significant differences in intrinsic bias toward nationalities among various monolingual models.
  • Analyzed gender-occupation bias in LLMs, linking it to pretraining data, and examining the effects of instruction tuning and alignment on bias mitigation.
• Turkish-specific contributions: As a Turkish researcher, I’ve contributed to various Turkish NLP resources: TurkishMMLU, sentiment analysis, and dependency parsing. I also co-organized the Turkic NLP workshop, SIGTURK, at ACL 2024 and 2026.

News

• March 2026: I am co-organizing the Second Turkic NLP Workshop at EACL 2026. My paper Tracing bias from pretraining data to alignment was accepted to LREC 2026.
• November 2025: Our paper Do We Know What LLMs Don’t Know? was accepted to EMNLP-Findings 2025.
• July 2025: TUMLU was accepted to ACL 2025.
• May 2025: I have started a new position as a Research Scientist at Google DeepMind in London, joining the Privacy and Security team!
• March 2025: Our paper MemLLM was accepted to TMLR.
• October 2024: 4 papers accepted at EMNLP 2024: LongForm, TurkishMMLU, SynthEval, CovERed.
• September 2024: I am visiting the Language Technology Lab at the University of Cambridge.
• August 2024: I attended ACL 2024 to co-organize the first Turkic NLP workshop, SIGTURK.
• May 2024: I presented LongForm and Hallucination Augmented Recitations at the DPFM workshop at ICLR 2024.
• December 2023: I attended EMNLP 2023 to present MEAL and Language-Agnostic Bias Detection in Language Models.