I've built applied AI systems across NLP, computer vision, and autonomous-vehicle research, from transformer-based sentiment analysis to safety-constrained multi-agent navigation.
At MCIT, I developed an end-to-end NLP sentiment analysis system and scalable AWS SageMaker pipelines with S3, Lambda, MLflow, and Gradio. I reduced model latency by 25% through ONNX Runtime and quantization.
At DESY, I developed web applications for the PaNET ontology, linking data with SPARQL and HTTP requests and automating workflows through GitLab CI/CD. My work contributed to PaNET publications on standardized citation and neutron research.
I've also developed low-light image restoration and image-to-image translation models at Huawei, plus projects in art forgery detection and satellite-based disaster monitoring. I'm completing a Communications and Information Engineering degree with a minor in Software Application Development.
