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aira:start [2025/04/04 12:59] – [Schedule Spring 2025] mtmaira:start [2025/04/17 08:34] (current) – [Schedule Spring 2025] mzk
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 ===== Schedule Spring 2025 ===== ===== Schedule Spring 2025 =====
 +  * **[PHD TRACK] 2025.04.24**: Natalia Wojak-Strzelecka,  PhD Candidate @ Jagiellonian University, [[#section20250424|Enhancing concept drift detection, explanation and adaptation to changes in industrial data streams.]]
 +    * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1744878780977?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22a39bd3b1-7b43-47ab-b5ec-29d9ab0ccbb2%22%7d|MS Teams]]
 +    * Recording:  TDA
 +    * Presentation slides:  TDA
 +
   * **[PHD TRACK] 2025.04.03**: Dmytro Polishchuk,  PhD Candidate @ Jagiellonian University, [[#section20250403|Automated GitHub Repository Quality Evaluation: A Metrics-Based Approach.]]   * **[PHD TRACK] 2025.04.03**: Dmytro Polishchuk,  PhD Candidate @ Jagiellonian University, [[#section20250403|Automated GitHub Repository Quality Evaluation: A Metrics-Based Approach.]]
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1743415239609?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22a39bd3b1-7b43-47ab-b5ec-29d9ab0ccbb2%22%7d|MS Teams]]     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1743415239609?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22a39bd3b1-7b43-47ab-b5ec-29d9ab0ccbb2%22%7d|MS Teams]]
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 ===== Presentation details ===== ===== Presentation details =====
 +
 +==== 2025-04-24 ====
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 +{{ :aira:natalia-wojak-strzelecka-foto.jpg?200| }}
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 +**Speaker**: Natalia Wojak-Strzelecka,  PhD Candidate @ Jagiellonian University
 +
 +**Title**: Automated GitHub Repository Quality Evaluation: A Metrics-Based Approach.
 +
 +**Abstract**:
 +In this seminar, we will explore three complementary approaches to handling evolving data in industrial environments. We'll discuss methods for detecting and adapting to domain shifts in data streams, distinguishing between real system failures and normal process changes, and using explainable AI to better understand and interpret concept drift. The presented work combines domain adaptation, drift detection, and XAI to improve the robustness and transparency of machine learning models in real-time settings like manufacturing and healthcare.
 +
 +**Biogram**: 
 +Natalia has received Bachelor's (2020) and Master's (2022) degrees in Mathematics from Silesia Univerity of Technology, Faculty of Applied Mathematics. Her career path is deeply rooted in the industry, she started as a data scientist working on vibration signals for predictive maintenance applications and continuing as a modelling specialist at ArcelorMittal, where she develops and implements models for production optimization and image processing. Currently, as a PhD candidate, she is working on advanced domain adaptation techniques for industrial data stream applications and explainable anomaly detection.
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 ==== 2025-04-03 ==== ==== 2025-04-03 ====
aira/start.1743771544.txt.gz · Last modified: 2025/04/04 12:59 by mtm
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