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List of results

• Beyond 5G baseband processing on a multicore architecture  + (2021 march -)

• Meta-learning for Multivariate Signals  + (2022 Fall - 2023 Summer)
• Developing a device for rapid water quality assessment  + (2022-11-01 - 2023-04-30)
• Leveraging LLMs for Clinical Note Annotation and Uncertainty Estimation  + (2023-2024)
• Uncertainty quantification for data driven clinical decision making  + (2023-2024)
• Multivariate Time Series Analysis with Irregularly Sampled Data  + (2023-2024)
• Machine Learning for Segmentation of Lensed Galaxies: Distinguishing Source Galaxies from Gravitational Lenses  + (2023-2024)
• Predicting Energy Consumption for Heavy-Duty Vehicles via Time Series Embeddings (in collaboration with Volvo)  + (2024 Fall)
• Evaluating Privacy Leakage Attacks on Fine-Tuned Clinical Models with Synthetic Data  + (2025-26)
• Design and Evaluation of an LLM-Based Travel Planner with Dynamic Event and Accommodation Data  + (2025-26)
• Interactive Anomaly Detection  + (4th of November 2019 to 29th May 2020)
• Model-Based Testing of Zero-Copy Protocols  + (6 months, starting as soon as possible)
• Analysis of industrial time series  + (ASAP)
• Quantum Machine Learning models for predicting disease  + (ASAP)
• Graph Neural Networks for cardiovascular disease  + (ASAP)
• Gait analysis using wearable sensors in Parkinson's disease  + (Autumn 2020 - Spring 2021)
• Explainable Decision Forest  + (Autumn 2024)
• Project opportunity at PERIsign  + (Autumn 2024)
• Tabular Health Data Under Attack: Benchmarking Privacy Risks and Defenses  + (Autumn25-Spring26)
• Constrained dynamic path planning for truck and trailer  + (Dec 2014 - June 2015)
• Comparative study of an automated testing coverage for a TCP/IP stack implementation  + (Dec 2020 - May 2021)
• Security analysis of IIoT connectivity protocols  + (Dec 2021 - May 2022)
• Reinforcement learning for End-to-End autonomous driving  + (December 2016 - June 2017)
• Automatic Generation of Realtime Machine Learning Architectures  + (December 2020, May 2021)
• Recurrent and Deep Learning for Machine Prognostics  + (Fall 2017)
• Automatic Machine Learning (AUTO-AUTO-ENCODER!)  + (Fall 2018)
• Peer Group Discovery in District Heating Substations and Heat Pumps for Self-Monitoring  + (Fall 2018)
• Wisdom of the crowd, is there a single crowd?  + (Fall 2018)
• Deep Recurrent Networks for Machine Prognostics  + (Fall 2018)
• Understanding usage of Volvo trucks  + (Fall 2018)
• Understand Patterns in Volvo Truck Lifetime Repair History  + (Fall 2018)
• Contactless monitoring of blood pressure using photoplethysmography  + (Fall 2018)
• Machine Learning and LeadTime Prediction  + (Fall 2018)
• Deep learning and Back Order Solutions  + (Fall 2018)
• Automatic Generation of Descriptive Features for Predicting Vehicle Faults  + (Fall 2019)
• Detecting changes in causal relations  + (Fall 2019)
• Detecting different types of machines based on usage  + (Fall 2019)
• Estimating Architectural Properties of Buildings Based on Heating Data  + (Fall 2019)
• Representation Learning for Deviation Detection  + (Fall 2019)
• Forklift Trucks Usage Analysis  + (Fall 2019)