Research Areas
Detailed overview of my research focus and expertise
LLM finetuning and interpretation
My research focuses on efficient fine-tuning of LLMs using parameter-efficient methods like LoRA/QLoRA (implemented for Mistral-7B), rigorous evaluation via modular benchmarking suites (MMLU, TruthfulQA, ARC), and training data analysis to understand how dataset composition, quality, and bias impact model behavior.
Learn MoreAuto-labelling & Data Selection
My professional work and research focuses on scalable auto-labelling and efficient data selection for multi-modal data. I have extensive experience working with sensor data (e.g., perception systems), which includes sensor-data, time-series and structured/semi-structured data, and have applied AI methods to improve data efficiency, system performance, and scalability.
Learn MoreMulti-modal sensor fusion and semi-automated labelling systems
My PhD research focused on applied deep learning for multi-modal sensor data, particularly in the context of automated driving. I developed methods for sensor fusion and semi-automated data labelling, bridging machine learning research with scalable, production-relevant systems.
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