Computational Linguistics Researcher
14 years of combined experience in academia and industry, specializing in Table Understanding and Visually-Situated Language, building large-scale multimodal applications.
Work Experience
Conducting research on multimodal approaches to visually-situated text tasks, including table, document, and chart understanding.
- Multimodal Table Understanding
- Visually-Situated Language
- Optical Context Compression
Contributed to Luminous (European project for multimodal dialog models in mixed reality) and Antidote (medical question answering through RAG).
- Cross-modal Entity Linking Research
- Fine-tuned LLMs applied to medical question answering
- Retrieval-Augmented Generation (RAG)
Fine-tuned and deployed Deep Neural Network-based models for text summarisation and sentence slot filling for commercial applications.
- Deep Learning applied to Natural Language Processing
- Abstractive summarisation and Data-to-Text
- Developing and deploying production ready ML models on AWS infrastructure
Applied data science research methods to business challenges including anomaly detection and customer segmentation.
- Machine Learning applied to retail customer segmentation
- Assistance on Deep Learning research projects
Worked on research projects in Information Security, IoT, and Machine Learning, developing foundational data science and research skills.