Tim Luka Horstmann

T-REX is a live, interactive tool for verifying whether a textual claim is supported or contradicted by tabular data using state-of-the-art language models. It highlights relevant cells and streams reasoning steps in real-time to ensure transparency and accessibility.

This project is an advanced continuation of a course project from Language Models and Structured Data, where we explored early-stage table fact-checking systems.

Try the live T-REX demo Watch the video demo View the source on GitHub

My MPhil thesis project at the University of Cambridge, supervised by Professor Dr Paula Buttery, in collaboration with RegGenome and Cambridge Judge Business School.

Work is ongoing and will be published soon.

About:
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.

An interactive chatbot integrated into this website, allowing users to ask questions about my CV and experience. Fully hosted on HF spaces.

• Powered by a quantized Qwen3 - 1.7B model (via llama-cpp-python) served via a FastAPI backend.
• Features streaming responses and conversation history management.
• Includes infrastructure for Retrieval-Augmented Generation (RAG) using FAISS/Sentence Transformers (experimented, but currently using full CV context for faster performance and full context).
• Optimized for CPU-only inference within Hugging Face free tier hardware constraints.

Try the Chatbot View Backend Space (HF)

This project was developed as part of the Machine and Deep Learning Data Challenge (CSC_51054_EP) at IP Paris. Our goal was to detect sub-events (like goals or red cards) from tweet activity during FIFA World Cup matches.

Approach: We implemented and compared three different strategies: traditional ML models (e.g., LightGBM, SVM), graph-based methods analyzing adjacency matrices, and LLM-based approaches. Our final model, the EnhancedPeriodClassifier, enriched BERTweet embeddings with handcrafted features and reached an accuracy of 77%.

Key Features:

• Preprocessing pipeline adapted from SOTA research
• Graph construction and deviation analysis
• Custom neural architecture combining pre-computed embeddings and multi-head attention
• Extensive hyperparameter tuning with Optuna

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A voice-enabled advanced AI assistant combining local LLMs, ASR (Whisper), TTS engines and external tool integration (MCP) for a seamless conversational experience.

• Wake-word detection & streaming text + audio
• Local inference via llama-cpp-python
• Multiple TTS engines (Kokoro, Coqui, FastTTS)
• Tool integration: Maps, Search, Weather, Custom functions

View on GitHub

Research project focusing on generating natural-sounding French speech using advanced deep learning techniques during my internship at Hi! PARIS, supervised by Eric Moulines.

A machine learning pipeline designed to analyze cardiac audio signals and classify heart murmurs. The project leverages advanced audio processing techniques such as MFCCs and spectral contrast, coupled with sensitivity-focused models, to enhance clinical decision-making in mobile health applications.

• Implemented audio preprocessing with bandpass filtering and resampling.
• Extracted key features (MFCC, spectral contrast, RMS energy) for classification.
• Trained multiple ML models emphasizing sensitivity for medical contexts.

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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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View Experiments on wandb