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aira:start [2025/06/06 13:04] – [Schedule Spring 2025] mtm | aira:start [2025/10/16 10:00] (current) – [2025-06-26] mzk | ||
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====== Artificial Intelligence in Research and Applications Seminar (AIRA) ====== | ====== Artificial Intelligence in Research and Applications Seminar (AIRA) ====== | ||
- | GEIST is happy to announce, | + | AIRA (the Artificial Intelligence Research and Applications) |
- | AIRA is a weekly | + | AIRA was created |
+ | During this time we had almost 100 speakers (as of mid 2025) including researchers from many EU countries, PhD students from the Jagiellonian University, Poland, and Europe. | ||
- | **Please save your Thursdays between 3:15-4:45 PM Warsaw Time** | + | The objective of AIRA is to create an open venue to discuss AI on an interdisciplinary level and from multiple perspectives. We invite AI talks from technical and exact sciences, but also welcome researchers from social sciences and humanities to share their views on AI development and its applications. Finally we are also open on practitioners from industry and business using AI in their companies. |
- | The program will be published at [[https:// | + | AIRA also formally exists as a PhD course in the Jagiellonian University in the Technical Computer Science program as one of the foundational seminars ([WFAIS.SDSP-IT001.01] and [WFAIS.SDSP-IT001.02]). The students are invited to presents their research plans, as well as to share the progress of their PhD projects. Furthemore, we welcome PhD students from other programs in the JU who are interested in AI. PhD students can participate actively presenting their work and perspectives; |
- | (a dedicated MS Teams group for announcements is available for those who are interested). | + | |
+ | **Please save your Thursdays between 3:15-4:45 PM Warsaw Time on MS teams.** | ||
+ | |||
+ | The program will be published at the https:// | ||
Scientific coordination: | Scientific coordination: | ||
- | Scientific secretaries [[https://szymon.bobek.re|Szymon Bobek]], [[https:// | + | Scientific secretaries [[https://www.geist.re/ |
+ | |||
+ | Contact for enrollment of the JU PhD students [[https:// | ||
+ | |||
+ | |||
+ | ===== Schedule Autumn 2025 ===== | ||
+ | |||
+ | * **[PHD TRACK] 2025.10.16**: Karol Dobiczek, | ||
+ | * Meeting link:[[https:// | ||
+ | * Recording: | ||
+ | * Presentation slides: | ||
+ | |||
+ | * **2025.10.09**: | ||
===== Schedule Spring 2025 ===== | ===== Schedule Spring 2025 ===== | ||
+ | * **[RESEARCH TRACK] 2025.06.26**: | ||
+ | * Meeting link: | ||
+ | * Recording: | ||
+ | * Presentation slides: | ||
- | * **[PHD TRACK] 2025.06.05**: | + | * **[RESEARCH |
* Meeting link: | * Meeting link: | ||
- | * Recording: | + | * Recording: |
* Presentation slides: | * Presentation slides: | ||
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+ | ==== 2025-10-16 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
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+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **Title**: Applying Counterfactual Explanations in Evolving Scenarios and Expert Domains. | ||
+ | |||
+ | **Abstract**: | ||
+ | Counterfactual explanations (CE) are one of the building blocks of many explainable machine learning methods, however their application and effects when applied to realistic scenarios are often missing. This seminar presents two works on counterfactual explanations that address some of these scenarios. First work explores the effects of applying CEs in an evolving domain and model, the second investigates whether existing methods for natural language CEs can handle being applied to expert domains. Other current and future directions in applying CEs and other XAI methods will be discussed. | ||
+ | |||
+ | **Biogram**: | ||
+ | Karol Dobiczek is a PhD candidate in the team led by professor Grzegorz J. Nalepa at the Jagiellonian University in Kraków. He received his Master' | ||
+ | </ | ||
+ | <WRAP clear></ | ||
+ | |||
+ | ==== 2025-06-26 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **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, | ||
+ | |||
+ | **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. | ||
+ | </ | ||
+ | <WRAP clear></ | ||
==== 2025-06-05 ==== | ==== 2025-06-05 ==== |