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--- aira:start [2025/06/22 18:52] – [Schedule Spring 2025] mzk
+++ aira:start [2025/06/26 20:17] (current) – [Schedule Spring 2025] mtm
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   * **[RESEARCH TRACK] 2025.06.26**: Artur Miroszewski,  PhD @ Jagiellonian University, [[#section20250626|Exploring Quantum Machine Learning through Earth Observation Case Studies.]]
   * Meeting link:[[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1750076384548?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
+  * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/Eb-PxGrj_L9Hka7sdu3TMY4BI2qKDq_5Bvl5hfUPyUwnNA?e=IFBlWx|View]]
-  * Presentation slides:  TDA
+  * Presentation slides:  {{:aira:slides-artur-miroszewski-2025-06-26.pdf|Download}}
   * **[RESEARCH TRACK] 2025.06.05**: Elżbieta Sroka,  PhD @ Łukasiewicz Research Network - EMAG Institute of Innovative Technologies, [[#section20250605| Interaction Design in Consideration of User Research and UX Specialist Perspectives.]]
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+==== 2025-06-26 ====
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+**Speaker**: Artur Miroszewski,  PhD @ Jagiellonian University
+**Title**: Exploring Quantum Machine Learning through Earth Observation Case Studies.
+**Abstract**:
+The analysis of satellite images has attracted significant research interest due to its numerous applications and unparalleled scalability in Earth Observation (EO). Although artificial intelligence algorithms for EO emerge at a steady pace, the community still needs to address many practical challenges that are concerned with such highly dimensional and unprecedentedly large volumes of image data. Quantum Machine Learning (QML) is a promising research avenue here. Despite the growing interest and funding in the field, current results remain inconclusive, shifting focus toward understanding the strengths and limitations of QML rather than solely expanding applications of such methods. This seminar addresses a critical gap by consolidating advancements in QML, with a special focus put on quantum kernel methods - which already proved their value in EO - to evaluate their role in advancing state-of-the-art EO solutions and exploring the potential quantum advantage via identifying their benefits and shortcomings in an unbiased and thorough way.
+**Biogram**:
+Artur Miroszewski received the Ph.D. degree in theoretical physics from the National Centre for Nuclear Research, Otwock, Poland, in 2021. He is a Postdoctoral Researcher with the Jagiellonian University, Kraków, Poland.
+He is involved in European Space Agency projects exploring the potential of quantum machine learning for satellite data analysis and serves as a quantum computing lecturer at the IEEE GRSS HDCRS summer schools. He is a co-chair of the QUEST IEEE GRSS Technical Committee.
+His main research interest include the study and applications of quantum kernel methods.
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+<WRAP clear></WRAP>
 ==== 2025-06-05 ====