Presentations of Degree Project in Industrial Analytics: Computer Science

All presentations are broadcast on Zoom
https://uu-se.zoom.us/j/63341165781

I den här tabellen hittar du information om när presentationer av examensarbeten sker.
Date	Time	Place	Course		Title	Opponent	Reviewer
2/6	15.15-16.00	105190	1DT112	Minglu Yao	Utilizing Causal Machine Learning in the Aftermarket Supply Chain	Jonas Sandgren	Stefan Pålsson
2/6	16.15-17.00	105170	1DT112	Jonas Sandgren	Transform Machine Learning for Predictive Maintenance in Workshop Environments	Minglu Yao	Lars-Henrik Eriksson
3/6	9.15-10.00	105190	1DT112	Nils Uwe Gegenmantel	Automating the Path from Data to Decision: Generating and Adapting Simulations with Gen-AI	Leon Wieprecht	Alexander Medvedev
3/6	10.15-11.00	105190	1DT112	Leon Wieprecht	Adaptive Genetic Algorithms for Real-Time IoT Workflow Scheduling under Uncertain Resource Availability	Nils Uwe Gegenmantel	Pontus Ekberg
9/6	8.15-9.00		1DT112
9/6	9.15-10.00		1DT112
9/6	10.15-11.00		1DT112
9/6	11.15-12.00	105150	1DT112	Julia Henningsson	Applying Machine learning to Demand Forecasting	Erik Dahlin Zhiyue Yuan	Lars-Henrik Eriksson
9/6	13.15-14.00		1DT112
9/6	14.15-15.00		1DT112
9/6	15.15-16.15	105170	1DT112	Erik Dahlin Zhiyue Yuan	Joining Requirements and Analyzing Results Using Artificial Intelligence	Julia Henningsson	Sofia Ouhbi
11/6	13.15-14.00	105190	1DT112	David Breh	Using machine learning to improve low-cost optical spectroscopy for liquid profiling related to plant cultivation	Chananya Pomkaew	Sven-Olof Nyström
11/6	14.15-15.00	105190	1DT112	Chananya Pomkaew	Single- and Multi-Agent Systems founded on Large Language Models for Structured Query Language Dialect Translation	David Breh	Lars Ericsson
11/6	15.15-16.00		1DT112
11/6	16.15-17.00		1DT112
12/6	8.15-9.00		1DT112
12/6	9.15-10.00		1DT112
12/6	10.15-11.00	105170	1DT112	Oskar Granlund	Reducing Supply Chain Uncertainty with Machine Learning: Forecasting Supplier Lead Times	Neha Dnyandeo Vaykole	Stefanos Kaxiras
12/6	11.15-12.00	105170	1DT112	Joel Korpi	Explainable Gradient Boosted Trees for Hydropower Dam Monitoring	Oskar Granlund	Fabio Bonassi
12/6	13.15-14.00	105190	1DT112	Neha Dnyandeo Vaykole	Explainable AI in Football: Enhancing XGBoost Interpretation with SHAP, Counterfactuals and LLM	Joel Korpi	David J.T. Sumpter
12/6	14.15-15.00		1DT112
12/6	15.15-16.00		1DT112
18/6	13.15-14.00	105190	1DT112	Linnéa Soto Carlsson	Optimizing Rotor Blades for Heavier-Than-Air Flight on Mar	Oliver Cronsten	Ken Mattsson
18/6	14.15-15.00	105190	1DT112	Oliver Cronsten	Uncertainty Quantification in Simulation-Based Inference Using Deep Generative Models	Linnéa Soto Carlsson	Per Mattsson
6/10	13.15-14.00	105170	1DT112	Ege Ebiller	XR for for Customer Training in Sales, Service, and Marketing of Tap Changers	Berhane Kiross Gebremeskel	Sofia Ouhbi
6/10	14.15-15.00	105170	1DT112	Berhane Kiross Gebremeskel	Federated Diffusion Models for Vehicular Applications	Ege Ebiller	Li Ju
6/10	15.15-16.00	105170	1DT112	Saba Yousefi Nasab Nobari	Measuring the Risk and Reward of Throw-Ball Passes in Professional Football Using Deep Learning and Physics-Based Modeling	Panagiotis Tzanetopoulos	David Sumpter
6/10	16.15-17.00	105170	1DT112	Panagiotis Tzanetopoulos	Explainable AI for Penetrative Pass Analysis in Football Analytics	Saba Yousefi Nasab Nobari	David Sumpter