
AI4SE Researcher and Software Engineer
JetBrains Research · AISE Lab, Delft University of Technology
I work on AI for software engineering: large language models for code, developer-AI interaction, and the verification of agentic systems. My aim is AI assistance that developers can trust, grounded in empirical evidence of what they need and in formal guarantees designed to serve those needs.
// under review
// 2026
// 2025
Benchmarking AI Models in Software Engineering: A Review, Search Tool, and Enhancement Protocol
IEEE Transactions on Software Engineering (journal-first at FSE 2026) · first author
Rethinking IDE Customization for Enhanced HAX: A Hyperdimensional Perspective
IDE Workshop 2025 (co-located with ICSE) · first author
Delft University of Technology. Supervised 50+ MSc students in group research projects across a 10-week intensive course, across two academic years. Coordinated assessment for a cohort of more than 130 students per year.
ICSE ('25, '26C1, '26C2), FSE ('25), ASE ('25), EMSE ('25), ISSTA ('26).
International Conference on Software Maintenance and Evolution.
DeepLearning4Code Workshop @ NeurIPS 2025 · DeepLearning4Code Workshop @ ICML 2026 · FSE × AIWARE Competition — Poisoned Chalice @ FSE 2026.
Workshop on Software Engineering for Agentic AI Systems.
Delft University of Technology. Thesis on AI-agent supported migration of legacy systems at the Dutch Inspectorate for Living Environment and Transport (ILT), supervised by Prof. Arie van Deursen.
University of Amsterdam. Number and set theory, theory of statistics, differential equations, information theory, and Markov chains.
Delft University of Technology. Data elective track. Bachelor thesis on AST-guided membership inference for code language models.
Human-AI Experience (HAX) team. Developer-centric trustworthy AI for software engineering.
Amsterdam. In-IDE calibration of large language models at production scale, within the AI4SE collaboration between JetBrains and TU Delft.
AI for Software Engineering, supervised by Prof. Maliheh Izadi. Benchmarking, agent verification, and IDE personalization.
Synthetic data generation, benchmarking, and a method for synthesizing time-series data conditioned on metadata.
Bias minimization and fairness in machine learning through synthetic data.