Daniel Ariza

Developed full-stack applications for biophysical instruments using C#/.NET, ASP.NET, Python/FastAPI, TypeScript/React, REST APIs, gRPC/Protobuf, Docker, and cloud-ready service patterns. • Designed decoupled, scalable service components with domain-driven boundaries, asynchronous cross-service communication, and PostgreSQL-oriented data modelling. • Implemented and benchmarked biophysical analysis algorithms in Python scientific packages, iterating closely with researchers on accuracy and production fit.

- Developed LLM workflows for factual verification, including claim generation, deduplication, evidence retrieval, and verification over scientific text. - Fine-tuned and evaluated LLM-based methods for claim verification against benchmark datasets. - Built knowledge-graph extraction flows that convert unstructured text into triples for retrieval-augmented reasoning and verification.

Thesis at the German Primate Center (DPZ), Göttingen: deep learning and statistical analysis for bioacoustics research.