Tim Luka Horstmann

AI research project in regulatory technology (RegTech), utilizing advanced machine learning and natural language processing techniques. The project focused on improving regulatory compliance in financial institutions through innovative automated analysis of thousands of legal documents containing financial regulatory information.

Conducted a comparative analysis of two state-of-the-art constituency parsers, the Charniak-Johnson parser and the Berkeley Neural Parser, as part of Cambridge’s L95, "Introduction to Natural Language Syntax and Parsing" course. The project focused on evaluating performance and identifying error patterns, highlighting the advantages of modern neural parsing techniques. Implemented in Python using bllipparser and benepar libraries.

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Development of a transformer-based machine learning model capable of providing abstractive summarizations to news articles as part of Cambridge's L90, "Overview of Natural Language Processing", course. Implemented in PyTorch.

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Worked with stakeholders across different organizations and drove resolution of the defect by setting up a QuickSight dashboard on top of Redshift ETL pipelines and using machine learning.

Development of a novel time series forecasting algorithm based on XGBoost regression models for accurately disaggregating customer demand data for use in Continental AG's customer demand planning processes.

Development of a computer vision model using YOLO for detecting fire-related objects in images. This project was part of a technical test for the Machine Learning Research Intern position at Hi! PARIS.

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