I co-founded Compresr out of EPFL’s dlab to build context-compression infrastructure for LLMs. We were taken into Y Combinator W26. I am the infrastructure engineer on a four-person team: the multi-tenant cloud platform, the on-premise product, an open-source Go proxy, the GPU serving layer, and the benchmark harness we make decisions with.
A year of NLP research with Prof. Robert West on making language models behave under tight context budgets. First-authored GRAD (EMNLP 2025 Findings), where an LLM is trained with GRPO to generate few-shot demonstrations instead of retrieving them, and designed the prompt-compression benchmark programme that became the basis for an ACL submission and, later, a product.
I led applied research and prototyping efforts in multimodal AI, focusing on cross-modal representation learning, graph-based embeddings, and neural search systems. I developed scalable pipelines to generate scene graphs from satellite imagery and knowledge graphs from textual data, and to align their graph embeddings in a shared representation space.
Developed a machine learning pipeline to forecast daily ad spend on Google Ads based on client-specific campaign data. Deployed a web backend for dynamic budget strategy adjustment, automated alerts, and integration with Azure Cloud infrastructure.
During my studies, I served as a teaching assistant for multiple courses, assisting in lectures, labs, and tutorials