Muskaan Chopra Receives the Grace Hopper Award
We are delighted to share some exciting news: Muskaan Chopra has been awarded the Grace Hopper Award by the Institute of Computer Science at the University of Bonn.
Research Group – Applied Machine Learning Lab
© Maximilian Waidhas / b-it Led by Prof. Dr. Rafet Sifa, the Applied Machine Learning (AML) Lab focuses on addressing the challenges of implementing machine learning models in real-world settings while developing novel methods for pattern analysis and representation learning. The lab’s primary area of investigation is based on constructing hybrid, interpretable, and resource-aware learning systems with practical applications in text mining, behavioral analytics, and medical informatics.
At AML Lab, we have made it our mission to bridge the gap between cutting-edge technology and everyday challenges.
If you want to reach out to us, please write us:
amllab[at]bit.uni-bonn.de
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Research Focus
© stock.adobe.com Text Mining
Learn about the potential of text mining through our advanced NLP research focused on representation learning, finance & legal applications, and decision making processes.
© stock.adobe.com Medical Informatics
Discover how we are improving diagnostics and addressing the challenges of modern medicine by focusing on efficiency and accuracy to empower healthcare professionals to make informed decisions.
© stock.adobe.com Behavioral Analytics
Explore how psychology, data analytics and user experience are used to model human behavior to improve decision making and personalize services.
News
History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting
This is a short summary of our paper “History Rhymes: Macro-Contextual Retrieval for Robust Financial Forecasting” by Sarthak Khanna, Armin Berger, Muskaan Chopra, David Berghaus, and Rafet Sifa, published in the proceedings of the 2025 IEEE International Conference on Big Data.
Towards Reliable Machine Translation: Scaling LLMs for Critical Error Detection and Safety
This is a short summary of our paper “Towards Reliable Machine Translation: Scaling LLMs for Critical Error Detection and Safety” by Muskaan Chopra, Lorenz Sparrenberg, and Rafet Sifa, published in the proceedings of ECIR 2026.
From Retinal Pixels to Patients
This is a short summary of our paper “From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening” by Muskaan Chopra, Lorenz Sparrenberg, Armin Berger, Sarthak Khanna, Jan H. Terheyden, and Rafet Sifa, published in the proceedings of the 2025 IEEE International Conference on Big Data.
When RL Wins the Benchmark but Loses the Patient
This is a TL;DR of our paper “Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients” by Armin Berger, Manuela Bergau, Helen Schneider, Saad Ahmad, Tom Anglim Lagones, Gianluca Brugnara, Martha Foltyn-Dumitru, Kai Schlamp, Philipp Vollmuth, and Rafet Sifa, available on arXiv and to be published in the Proceedings of IJCNN 2026.
Leveraging Synthetically Generated Data for Real Estate Document Classification
This is a short summary of our paper “Leveraging Synthetically Generated Data for Real Estate Document Classification” by Tobias Deußer, Gregor Ramien, Nico Weber, Maximilian Meidinger, Max Hahnbück, Christian Bauckhage, and Rafet Sifa, published in the proceedings of the 2025 IEEE International Conference on Big Data.
Towards Automated Recipe Reconstruction
This is a short summary of our paper “Towards Automated Recipe Reconstruction: Optimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization” by Svetlana Schmidt, Linda Klasen, Ute Nöthlings, and Rafet Sifa published in the proceedings of the 2025 IEEE International Conference on Big Data.
Current Updates
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March 19, 2026
At last years IEEE BigData (2025) conference, the AML Lab had 9 papers accepted and presented. A few days ago, they were added to the IEEE Xplore repository.
Upcoming Events
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