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This shows you the differences between two versions of the page.

--- aira:start [2025/04/04 12:59] – [Schedule Spring 2025] mtm
+++ aira:start [2025/12/12 15:33] (current) – [2025-12-18] mzk
@@ Line 1: / Line 1: @@
 ====== Artificial Intelligence in Research and Applications Seminar (AIRA) ======
-GEIST is happy to announce, the launch of an open, online seminar on Artificial Intelligence in Research and Applications (AIRA).
+AIRA (the Artificial Intelligence Research and Applications) seminar (https://aira.geist.re) is an event organized by the GEIST group (https://www.geist.re) and the HCAI department (https://hcai.fais.uj.edu.pl) in the FAIS Faculty@JU in cooperation with the Mark Kac center (https://markkac.id.uj.edu.pl) and the JAHCAI lab (https://jahcai.id.uj.edu.pl).
-AIRA is a weekly event (with some breaks between semesters and holidays) devoted to recent results in AI research presented by invited guests from many AI-related fields as well as applications of AI methods and tools in areas of science, industry and business.
+AIRA was created in 2021 and has been regularly organized since then as an online/hybrid event.
+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.geist.re]] in advance
+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; however, participation with no presentation is also possible, e.g. for persons who are not computer scientists.
-(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://aira.geist.re webpage in advance (a dedicated MS Teams group for announcements is available for those who are interested).
 Scientific coordination: [[https://gjn.re|Grzegorz J. Nalepa]]
-Scientific secretaries [[https://szymon.bobek.re|Szymon Bobek]], [[https://www.geist.re/pub:about_us:mtm|Maciej Mozolewski]], [[https://www.geist.re/pub:about_us:mzk|Maciej Szelążek]]
+Scientific secretaries [[https://www.geist.re/pub:about_us:mzk|Maciej Szelążek]], [[https://www.geist.re/pub:about_us:mtm|Maciej Mozolewski]], [[https://szymon.bobek.re|Szymon Bobek]]
+Contact for enrollment of the JU PhD students [[https://fais.uj.edu.pl/wydzial/dziekanat|Mrs Ewa Lelek @ WFAIS]]
+===== Schedule Autumn 2025 =====
+  * **[RESEARCH TRACK] 2025.12.18**: Maciej Zięba, Associate Professor @ Wrocław University of Technology, [[#section20251218|Normalizing Flows - fundamental concepts and applications in counterfactual explanations.]]
+    * Meeting link:[[https://teams.microsoft.com/meet/34011375285219?p=OD1aOgi4cwK5D4JUBN|MS Teams]]
+    * Recording:  TDA
+    * Presentation slides:  TDA
+  * **[PHD TRACK] 2025.12.11**: Anna Sofia Lippolis, PhD Candidate @ University of Bologna, [[#section20251211|Enhancing Knowledge Engineering with LLMs.]]
+    * Meeting link:[[https://teams.microsoft.com/meet/35848296892369?p=0t3yTogmgGOsSwYeDO|MS Teams]]
+    * Recording:  TDA
+    * Presentation slides:  TDA
+  * **[PHD TRACK] 2025.12.04**: Bartłomiej Małkus, PhD Candidate @ Jagiellonian University, [[#section20251204|Towards Explainable Meta-Models for Ensembles of Financial Alphas.]]
+    * Meeting link: [[https://teams.microsoft.com/meet/33172349128262?p=yBHWXyoke6oH4OVDl4|MS Teams]]
+    * Recording:  TDA
+    * Presentation slides:  TDA
+  * **[RESEARCH TRACK] 2025.11.27**: Aleksander Mendyk, Professor PhD, DSc @  Jagiellonian University, [[#section20251120|AI/ML for pharmaceutical sciences – an industrial perspective.]]
+    * Meeting link: [[https://teams.microsoft.com/meet/39537797907603?p=jjjJD0gjg5LcGl6eL9|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQDqGs_UjUSkRIm82hi1-KgBAc4MLc-EjcVPaewZ6sYskI4?e=qTmdDd|View]]
+    * Presentation slides:  {{:aira:slides-aleksander-mendyk-2025-11-27.pdf|Download}}
+  * **[RESEARCH TRACK] 2025.11.13**: Tomáš Kliegr with the research team @ Prague University of Economics and Business, [[#section20251113|RAG research, LLMs as digital twins, Rule Learning in relational data - perspectives in AI Research .]]
+    * Meeting link:[[https://teams.microsoft.com/meet/35466374465161?p=E80GGRrCPxyDKDYomv|MS Teams]]
+    * Recording - Barbara Moreová:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQBAZ5wBtjiaTYjBlQDQACWHAQFaeBCECRYAmhZiYB1PI6c?e=G4VFdq|View]]
+    * Recording - Mateusz Ploskonka:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQAC5l947emMQoLcwSSWuqhOATaOnpK3jqxWwR-U-EJ0ODs?e=p2R8tL|View]]
+    * Recording - Kateřina Hrudková:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQANTKVda1QPS45yKsybdKAYAZ1oSTeQo4b34J7sfF5pbWw?e=DVxlO5|View]]
+    * Presentation slides - Barbara Moreová:  {{:aira:slides-barbara-moreova-2025-11-13.pdf|Download}}
+    * Presentation slides - Mateusz Ploskonka:  {{:aira:slides-mateusz-ploskonka-2025-11-13.pdf|Download}}
+    * Presentation slides - Kateřina Hrudková:  {{:aira:slides-katerina-hrudkova-2025-11-13.pdf|Download}}
+  * **[RESEARCH TRACK] 2025.11.06**: Tomáš Kliegr and Lukáš Sýkora @ Prague University of Economics and Business, [[#section20251106|LLM-based feature generation from text for interpretable machine learning.]]
+    * Meeting link:[[https://teams.microsoft.com/meet/3127857308764?p=1vVlUBWtvXh3pJVm3z|MS Teams]]
+    * Recording - Tomáš Kliegr:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQA6xvsR6r__Sb0uBw018DxdARL_jxuITdNrnlTomJNFcCI?e=SjdjzJ|View]]
+    * Recording - Lukáš Sýkora:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQAD1MW0CrT7TbXn7305dHJUAWT51QewwEh9LPyrbfehOj8?e=KfQCx8|View]]
+    * Presentation slides - Tomáš Kliegr:  {{:aira:slides-tomas-kliegr-2025-11-06.pdf|Download}}
+    * Presentation slides - Lukáš Sýkora:  {{:aira:slides-lukas-sykora-2025-11-06.pdf|Download}}
+  * **[RESEARCH TRACK] 2025.10.30**: Peter van Dam,  Associate Professor @ Jagiellonian University, [[#section20251030|Inverse problem in electrocardiography: modeling the ECG.]]
+    * Meeting link:[[https://teams.microsoft.com/meet/38693113399487?p=QVsurHuK9sLM4PH7ek|MS Teams]]
+    * Presentation slides:  TDA
+  * **[RESEARCH TRACK] 2025.10.23**: Marek Pędziwiatr,  PhD @ Jagiellonian University, [[#section20251023|Eye tracking as a bridge between psychology and computer science.]]
+    * Meeting link:[[https://teams.microsoft.com/meet/3686533128348?p=2ZQTEpXJiiJDSLC3jJ|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQDdy0LegIdrQZkdzpl-cbfmAcPDZLzhRLLqWoJ4FcsjQp0?e=kGMbFU|View]]
+    * Presentation slides:  {{:aira:slides-marek-pedziwiatr-2025-10-23.pdf|Download}}
+  * **[PHD TRACK] 2025.10.16**: Karol Dobiczek,  PhD Candidate @ Jagiellonian University, [[#section20251016|Applying Counterfactual Explanations in Evolving Scenarios and Expert Domains.]]
+    * Meeting link:[[https://teams.microsoft.com/meet/3634985739900?p=D3AqLZM4yWIU1nXOWh|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQD8B4CDbZyuSqIs-5TSzXKSATAQkbiX71yEsz3ogYFMafg?e=hVk2FP|View]]
+    * Presentation slides:  {{:aira:slides-karol-dobiczek-2025-10-16.pdf|Download}}
+  * **2025.10.09**: Grzegorz J. Nalepa, AIRAmaster @ Jagiellonian University, //Introduction to AIRA to the new PhD students//
 ===== Schedule Spring 2025 =====
+  * **[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:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/Eb-PxGrj_L9Hka7sdu3TMY4BI2qKDq_5Bvl5hfUPyUwnNA?e=IFBlWx|View]]
+    * 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.]]
+    * Meeting link:[[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1748969648131?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:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EXXr2Bww-WlGtD4kSo63kioBnlcBhajhN7zEXY22ok6huw?e=zPIl1S|View]]
+    * Presentation slides:  {{:aira:slides-elżbieta-sroka-2025-06-05.pdf|Download}}
+  * **[PHD TRACK] 2025.05.29**: Bogdan Gulowaty, PhD @ Wrocław University of Science and Technology, [[#section20250529|Building transparent classification models.]]
+    * Meeting link:[[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1748506829263?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:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EZxjf0UnTCtChkK4Lk39qcIBDU9df_PNwuCe9D_2MpFn_w?e=GtoV3X|View]]
+    * Presentation slides:  {{:aira:slides-bogdan-gulowaty-2025-05-29.pdf|Download}}
+  * **[PHD TRACK] 2025.05.22**: Jacek Karolczak,  PhD Candidate @ Poznań University of Technology, [[#section20250522|Explainable AI: Moving from numbers to meaningful insights via prototype-based explanations.]]
+    * Meeting link:[[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1747385677830?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:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EYgrsIJ986RNpBAtv_11xLcBcDlCsAbrXYbUbMoTEMuSww?e=3g2tJQ|View]]
+    * Presentation slides:  {{:aira:slides-jacek karolczak-2025-05-22.pdf|Download}}
+  * **[PHD TRACK] 2025.05.15**: Anastasiya Pechko,  PhD Candidate @ Jagiellonian University, [[#section20250515|Adaptive Modular Housing Design for Crisis Situations.]]
+    * Meeting link:[[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1746780764971?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:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EbyiCvceYJdEnCVPvfgVlNsBVB5BDIsbHRpDRgc8_19rMA?e=wktReu|View]]
+    * Presentation slides:  {{:aira:slides-anastasiya-pechko-2025-05-15.pdf|Download}}
+  * **[RESEARCH TRACK] 2025.05.08**: Gianluca Guglielmo,  PhD @ Tilburg University, [[#section20250508|From Video Games to Real-life ”Games”: The Emergence of Real-life Expertise in (Serious) Video Games.]]
+    * Meeting link:[[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1746526946846?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:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EXI-yziJgQVEu8CHWewpTrABob_7ilL9hZeDNWU-OSbWWQ?e=nCNWWc|View]]
+    * Presentation slides:  {{:aira:slides-gianluca-guglielmo-2025-05-08.pdf|Download}}
+  * **[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:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EWKQNaHF8SNGhAjftjZOkZcB07wrJAXFXi4-k7saiWYx6Q?e=Iq5cb0|View]]
+    * Presentation slides:  {{:aira:slides-natalia-wojak-strzelecka-2025-04-24.pdf|Download}}
   * **[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]]
@@ Line 104: / Line 200: @@
       * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1715342946914?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
       * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EZe0bw0DZY5LtU3CCvns-6UBp432OE_5pD3_vs7zBOPhiA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=eU3MPT|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-      * Presentation slides: {{ :aira:slides-mateusz-bulat-20240515.pdf |Download}}
+      * Presentation slides: {{ :aira:slides-mateusz-bulat-20240515x.pdf |Download}}
   * **[RESEARCH TRACK] 2024.05.09**: Jason J. Jung [[#20240509| Deep Learning for Anomaly Detection in Multivariate Time Series: Approaches, Applications, and Challenges ]]
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1715082246704?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
@@ Line 403: / Line 499: @@
 ===== Presentation details =====
+==== 2025-12-18 ====
+<WRAP column 15%>
+{{ :aira:maciej-zieba-foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Maciej Zięba, Associate Professor @ Wrocław University of Technology
+**Title**: Normalizing Flows - fundamental concepts and applications in counterfactual explanations.
+**Abstract**:
+During the talk, I will begin with a brief introduction to generative flow-based models. Then, I will present practical examples demonstrating how this class of models can be applied in real-world scenarios. I will introduce flows as probabilistic regression models, highlighting their versatility as plug-in components and their generative capabilities for point clouds. I will also discuss how we applied flow-based models to a few-shot regression problem. Finally, I will illustrate how normalizing flows can be used to address counterfactual explanation tasks.
+**Biogram**:
+Maciej Zięba is a research scientist at Tooploox and an Associate Professor at Wroclaw University of Science and Technology, where he received a Ph.D. degree in computer science and a master's degree in economics. He also obtained a master's degree in computer science at the Blekinge Institute of Technology in Sweden. In 2017, he was a visiting scholar at the University of Wollongong (Australia). His research is directed towards deep learning, especially generative models and representation learning. He was the co-author of a variable number of research papers published in influential journals and presented at the top ML conferences, including NeurIPS, AAAI, ICML, CVPR, and ICLR. He is also leading genwro.ai research group (https://genwro.ai.pwr.edu.pl/) at Wroclaw University of Science and Technology.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-12-11 ====
+<WRAP column 15%>
+{{ :aira:anna-lippolis-foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Anna Sofia Lippolis, PhD Candidate @ University of Bologna
+**Title**: Enhancing Knowledge Engineering with LLMs.
+**Abstract**:
+The development and spread of Large Language Models (LLMs) are having a growing impact on the world of the Semantic Web, profoundly transforming the field of Knowledge Engineering. This field, traditionally characterized by a high degree of manual work and collaboration between technical professionals and domain experts, faces various challenges related to scalability and the continuous evolution of knowledge. In this context, LLMs are emerging in several areas, from law to medicine, as tools that support researchers: from the automatic generation of ontologies to the assessment of the quality and semantic coverage of conceptual models, and even the exploration of analogical reasoning, through which it is possible to identify structural correspondences between different domains. This talk will present current research directions on collaboration between LLMs and researchers for knowledge modeling, within a critical overview of the opportunities offered by these tools for the future of the Semantic Web.
+**Biogram**:
+Anna Sofia Lippolis (she/her) is a PhD student at the University of Bologna, Italy, affiliated with the National Research Council’s Institute for Cognitive Sciences and Technologies (Rome, Italy). Her work investigates how semantic technologies intersect with Digital Humanities research and how AI can automate knowledge-engineering practices.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-12-04 ====
+<WRAP column 15%>
+{{ :aira:bartlmiej-malkus-foto.jpeg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Bartłomiej Małkus,  PhD Candidate @ Jagiellonian University
+**Title**: Towards Explainable Meta-Models for Ensembles of Financial Alphas.
+**Abstract**:
+One branch of systematic trading research studies large libraries of formulaic alphas: small predictive models built from price and volume data. In practice, these alphas are combined into an ensemble whose composition changes with market conditions. From an ML perspective, this can be viewed as a meta-model that selects and weights weak experts based on their characteristics and the current environment.
+In this talk I will introduce this setting with minimal financial background (cross-sectional returns, information coefficient, long–short factor portfolios), and then reframe it in familiar ML terms. I will show how individual alphas can be treated as models with their own structural and behavioural features, and how this enables clustering them into "families" and reasoning about dynamic ensemble construction. Finally, I will sketch the idea of an explainable meta-model that maps alpha features and market descriptors to ensemble decisions, and highlight open methodological questions and possible research directions.
+**Biogram**:
+Bartłomiej Małkus is a PhD candidate at the Jagiellonian University in Technical Computer Science since 2021. He completed BSc and MSc studies in Computer Science at the AGH University of Science and Technology, and MSc studies in Financial Markets at the Cracow University of Economics. His field of interest are neurosymbolic approaches, prototypical networks, and financial applications of explainable ML. Commercially, he works in IBM on database and master data management solutions.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-11-27 ====
+<WRAP column 15%>
+{{ :aira:aleksander-mendyk-foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Aleksander Mendyk, Professor @ Jagiellonian University
+**Title**: AI/ML for pharmaceutical sciences – an industrial perspective.
+**Abstract**:
+Although reluctantly, pharmaceutical industry follows the footsteps of the other industries into the digital age. Among the digital innovations, data-driven approaches are more and more exploited in the various stages of drug discovery and development, including manufacturing as well. AI/ML is perceived as a disruptive technology capable of bringing safe innovation strategies and in the same time extending well known statistical process control and good practices for the benefit of development of safe and efficacious drugs. This movement towards AI/ML applications is stimulated both by industry itself and Regulatory Agencies like EMA and FDA. This talk will outline current areas and trends in the AI/ML applications for the pharmaceutical sciences, which are the backbone of the marketed medicinal products.
+**Biogram**:
+Prof. Aleksander Mendyk, PhD, DSc. is an expert in application of artificial&computational intelligence methods in pharmaceutical technology&biopharmacy, in vitro in vivo correlation (IVIVC) and bioequivalence, Author of over 100 publications. A pharmacist and programmer both in Open Source and commercial applications (R, Python, Java). Currently Head of the Department of Pharmaceutical Technology and Biopharmaceutics, Jagiellonian University-Medical College (JUMC), Kraków, Poland and Vice Dean for Science and Development at the Faculty of Pharmacy JUMC.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-11-13 ====
+<WRAP column 15%>
+{{ :aira:Tomáš_Kliegr_research_team-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Tomáš Kliegr with the research team @ Prague University of Economics and Business
+**Title**: RAG research, LLMs as digital twins, Rule Learning in relational data - perspectives in AI Research .
+**Abstract**:
+- Mateusz Ploskonka: RAG Research Presentation - Mateusz will present a comprehensive empirical evaluation of 32 LLMs (from 0.6B to 1T parameters) across various Retrieval-Augmented Generation (RAG) configurations. His research challenges the ""bigger is better"" assumption by identifying a critical performance plateau at 30B parameters, offering evidence-based guidance for practitioners to balance answer quality, cost, and sustainability in production RAG deployments.
+- Barbara Moreová: LLMs as digital twins for simulating effect of cognitive biases - Barbara will present her research on using Large Language Models to create digital twins for the simulation of human cognitive biases.
+- Kateřina Hrudková: Rule Learning in Relational Data: Methods, Interoperability, and Bioscience Applications -  Kateřina’s research bridges the gap between expressive Inductive Logic Programming (ILP) and scalable RDF rule learners. She will present tools (popper2rdf, rdf2popper) that enable this interoperability and demonstrate a practical application using the RDFRules system to discover interpretable ""nuggets"" from large-scale biomedical knowledge graphs, aiding in tasks like drug repurposing for COVID-19 and classifying microbial media.
+**Biogram**:
+Tomáš Kliegr is a Professor at the Faculty of Informatics and Statistics at the Prague University of Economics and Business (VSE Praha), where he is part of the Data Science & Explainable AI (DSXAI) research team. His research interests include Explainable AI (XAI), Interpretable Machine Learning, and neurosymbolic methods. He has published on topics such as the effect of cognitive biases on model interpretation in journals including Artificial Intelligence and Machine Learning. He is active in the rule-based systems community.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-11-06 ====
+<WRAP column 15%>
+{{ :aira:kliegr-sykora-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Tomáš Kliegr and Lukas Sykora @ Prague University of Economics and Business
+**Title**: LLM-based feature generation from text for interpretable machine learning.
+**Abstract**:
+Traditional text representations like embeddings and bag-of-words hinder rule learning and other interpretable machine learning methods due to high dimensionality and poor comprehensibility. This article investigates using Large Language Models (LLMs) to extract a small number of interpretable text features. We propose two workflows: one fully automated by the LLM (feature proposal and value calculation), and another where users define features and the LLM calculates values. This LLM-based feature extraction enables interpretable rule learning, overcoming issues like spurious interpretability seen with bag-of-words. We evaluated the proposed methods on five diverse datasets (including scientometrics, banking, hate speech, and food hazard). LLM-generated features yielded predictive performance similar to the SciBERT embedding model but used far fewer, interpretable features. Most generated features were considered relevant for the corresponding prediction tasks by human users. We illustrate practical utility on a case study focused on mining recommendation action rules for the improvement of research article quality and citation impact.
+**Biogram**:
+Tomáš Kliegr is a Professor at the Faculty of Informatics and Statistics at the Prague University of Economics and Business (VSE Praha), where he is part of the Data Science & Explainable AI (DSXAI) research team. His research interests include Explainable AI (XAI), Interpretable Machine Learning, and neurosymbolic methods. He has published on topics such as the effect of cognitive biases on model interpretation in journals including Artificial Intelligence and Machine Learning. He is active in the rule-based systems community.
+Dr. Lukas Sykora is a Research Assistant at the Department of Information and Knowledge Engineering and a Lecturer at the Prague University of Economics and Business.  He holds a PhD in Applied Informatics (2025), where his doctoral thesis focused on action rule mining. He has authored several publications on this topic, including "Apriori Modified for Action Rules Mining." He also brings industry experience as a Solution Architect Team Lead at Ogilvy.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-10-30 ====
+<WRAP column 15%>
+{{ :aira:peter-vanDam-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Peter van Dam,  Associate Professor @ Jagiellonian University
+**Title**: Inverse problem in electrocardiography: modeling the ECG.
+**Abstract**:
+The electrocardiogram (ECG) measured on the body surface shows beat-by-beat the electrical functioning of the heart. This ECG consists of a number of electrical time signals that are caused by the currents produced by the myocardial cells. These ECG signals require clinical interpretation, which in the In current clinical practice is close to an art. Modeling the ECG, i.e. have a model of the heart that simulates the electrical processes of the myocardium and calculates the ECG signals on the body surface, can help to understand these waveforms in a cause -effect relationship. This is the major subject of my current and past research, which I will introduce during this talk.
+**Biogram**:
+Dr hab. Peter van Dam is a scientist and lecturer specializing in cardiac modeling, electrocardiographic diagnostics, and the development of innovative educational and clinical tools based on heart signal analysis and 3D imaging. In 2025, he obtained his postdoctoral degree (habilitation) in medical sciences at the Jagiellonian University Medical College, presenting a scientific achievement entitled “Localization of PVC (Premature Ventricular Contraction): Evaluation and Validation of Inverse ECG Modeling.” He is also a laureate of the prestigious NCN OPUS grant, focused on inverse modeling of repolarization heterogeneity to assess arrhythmogenic risk.
+His academic path began with studies in electronic engineering at MBO-College Gouda (1981–1985) and Hogeschool Utrecht (1985–1990). He also studied philosophy (Rijksuniversiteit Utrecht, 1991–1992) and physics (Universität Oldenburg, 1992–1993). Between 1997–2004, he earned a master’s degree in biology and physics at the Open University in Heerlen, where his thesis involved developing a simulation of the P-wave in ECG and IEGM (2004). In 2010, he defended his PhD at Radboud University Nijmegen under the supervision of Prof. Adriaan van Oosterom, presenting the dissertation “The shortest path to cardiac activation.”
+Peter van Dam’s professional experience spans both academia and the medical industry. He began his career in 1997 as a senior scientist at Vitatron/Medtronic. Since 2009, he has been affiliated with Peacs BV (Chief Scientific Officer). In 2020, he became CEO of ECG-Excellence, focusing on developing innovative diagnostic and educational tools based on heart signal analysis and 3D modeling.
+In academia, he has worked at Radboud University (2011–2017, validating ECGI methods in an animal model), at UCLA (2013–2016, developing arrhythmia mapping methods), and currently conducts research and teaching at UMC Utrecht, UT Twente, and Hochschule Luzern Technik & Architektur. Since 2023, he has also collaborated with the Jagiellonian University Medical College, Center for Digital Medicine and Robotics, where he serves as Visiting Professor at the Department of Radiology. Since 2024, he has also been a specialist at the Laboratory of Functional and Virtual Medical 3D Imaging (3D-FM) at the University Hospital in Kraków.
+His research focuses on: inverse ECG modeling and arrhythmia source localization; the use of 3D imaging and simulation in cardiology; developing modern educational tools for students and physicians; integrating engineering and clinical expertise in the diagnosis of cardiac rhythm disorders.
+Dr hab. Peter van Dam sees great potential in interdisciplinary collaboration between universities and university hospitals worldwide. His goal is to create and implement groundbreaking 3D ECG technologies that not only enable more precise diagnostics but also open new possibilities for more effective treatment of cardiac arrhythmias. By combining clinical and engineering perspectives, he strives to set new standards in modern cardiology.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-10-23 ====
+<WRAP column 15%>
+{{ :aira:marek-pedziwiatr-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Marek Pędziwiatr,  PhD @ Jagiellonian University
+**Title**: Eye tracking as a bridge between psychology and computer science.
+**Abstract**:
+We move our eyes around three times per second. While we are rarely aware of these movements, they play a crucial role in shaping how we see the world: they determine what visual input reaches the brain and is processed by it (e.g., memorized). Understanding the process of (involuntarily) deciding where to look is one of the key problems in modern experimental psychology, and it has substantial practical significance for domains such as automated content-aware image cropping. Studies aimed at solving this problem typically rely on recording eye movements of individuals viewing visual materials (e.g. images) and relating these recordings to the outputs of image-processing algorithms that attempt to predict which image regions would attract human gaze. In my talk, I will provide an overview of this field and use examples from my work to showcase the potential of combining methods from experimental psychology and computer science.
+**Biogram**:
+Marek Pędziwiatr is a vision scientist interested in how we make sense of what we see. In particular, he studies human eye movements during picture viewing. He completed a PhD in psychology at Cardiff University (UK). Afterwards, Marek worked as a postdoctoral researcher at Queen Mary University of London. Then, he returned to Krakow, where, before moving to the UK, he obtained undergraduate degrees in Control Engineering and Robotics (BSc and MSc) and Cognitive Science (BSc), and joined the Centre for Brain Research at Jagiellonian University.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-10-16 ====
+<WRAP column 15%>
+{{ :aira:karol-dobiczek-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Karol Dobiczek,  PhD Candidate @ Jagiellonian University
+**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's degree from the Delft University of Technology in 2024 and his Bachelor's thesis from the same university in 2022. His work focuses on explainable and interpretable machine learning, mostly on the use of counterfactual explanations. Karol is currently a Software Engineer at Qualtrics and a Research Software Engineer at the MI2 team at the Warsaw University of Technology.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-06-26 ====
+<WRAP column 15%>
+{{ :aira:artur-miroszewski-foto.jpeg?200| }}
+</WRAP>
+<WRAP column 75%>
+**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.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-06-05 ====
+<WRAP column 15%>
+{{ :aira:elzbieta-sroka-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**:  Elżbieta Sroka,  PhD @ Łukasiewicz Research Network - EMAG Institute of Innovative Technologies
+**Title**: Interaction Design in Consideration of User Research and UX Specialist Perspectives: Artificial intelligence in UX Design, users experience in digital library and the needs of digital humanities researchers, reception of signed avatars by Deaf users.
+**Abstract**:
+The presentation presents the results of research in the field of Human-Computer Interaction (HCI), with a special focus on the needs of different user groups and the application of artificial intelligence (AI) tools in both the design and research processes. The areas discussed include: the use of AI in UX design and designers' perspectives; the experiences of digital library users, especially in the context of orientation in the field, navigation, and information retrieval; the needs of digital collection researchers and their expectations regarding AI-supported solutions; and the accessibility of research tools for Deaf people and the reception of sign avatars communicating in Polish Sign Language (PJM) in the Deaf community.
+**Biogram**:
+Elżbieta Sroka, PhD, certified UX designer, Senior Specialist at the Łukasiewicz Research Network – Institute of Innovative Technologies EMAG in Katowice, Poland.
+She obtained her doctoral degree in 2018 from the University of Silesia in Katowice, based on a dissertation focused on the digitization of social life documents in Polish digital libraries.
+Her research interests include research users information behavior, user experience (UX) design, and digital humanities, as well as applications of artificial intelligence—particularly in the context of human–AI interaction, the impact of AI on UX, and the use of AI in the study of digital collections. She also conducts research in the areas of digital accessibility, information management and information retrieval.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-05-29 ====
+<WRAP column 15%>
+{{ :aira:Bogdan_Gulowaty_foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Bogdan Gulowaty,  PhD @ Wrocław University of Science and Technology
+**Title**: Building transparent classification models.
+**Abstract**:
+As AI and ML technologies advance and become deeply integrated into daily life, the need for transparent and interpretable models grows increasingly urgent. AI systems must be understandable, trustworthy, and ethical, especially in critical healthcare, finance, and legal sectors. The rise of XAI seeks to address these challenges by providing explanations of model decisions, making AI systems more transparent. However, much of the progress in XAI has focused on deep neural networks, leaving other complex models like ensemble methods needing to be explored more regarding their interpretability.
+The thesis aims to fill that gap by developing novel methods to improve the transparency and interpretability of ensemble classifiers while ensuring these models maintain competitive predictive performance and build inherently transparent models. The central hypothesis of the thesis is that it is possible to construct such transparent or explainable models that perform as well as black-box models in a wide range of classification tasks. The work focuses on three primary methods designed to either explain or replace complex models with transparent alternatives: NOTE, optimal-centroids and quad-split algorithms.
+**Biogram**:
+Finished his PhD at Wroclaw University of Technology in 2025. Full time Software Engineer. Enthusiast of mountaineering, various sports activities and motorcycling. Born in Bolesławiec, currently lives in Wrocław.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-05-22 ====
+<WRAP column 15%>
+{{ :aira:Jacek_Karolczak_foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Jacek Karolczak,  PhD Candidate @ Poznań University of Technology
+**Title**: Explainable AI: Moving from numbers to meaningful insights via prototype-based explanations.
+**Abstract**:
+The increasing complexity of machine learning models has heightened the demand for their explainability. Most existing work in explainable artificial intelligence (xAI) focuses on techniques like pixel attribution or feature importance, especially Shapley values. However, these types of explanations are often criticized for being hard to interpret—not just for laypersons, but even for machine learning experts. In contrast, the XAI 2.0 manifesto advocates for concept-based explanations, such as prototypes - representative instances. This talk will introduce the problem and survey existing approaches, with a focus on recent developments in prototype-based explainability. It will also present the author’s own work on prototype-based concept drift detection, which maintains intrinsic interpretability. Finally, open challenges and directions for future work in prototype-based explainability will be discussed.
+**Biogram**:
+Jacek Karolczak is a PhD student at Poznan University of Technology, where he also earned BSc and MSc in Artificial Intelligence. His research focuses on improving the interpretability of machine learning models, particularly in dynamic environments where data continuously evolves. He believes the world of explainable AI (xAI) is shifting beyond feature-importance explanations, embracing high-level concept-based interpretations that better align with the language and reasoning of end users.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-05-15 ====
+<WRAP column 15%>
+{{ :aira:Anastasiya_Pechko_foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Anastasiya Pechko,  PhD Candidate @ Jagiellonian University
+**Title**: Adaptive Modular Housing Design for Crisis Situations.
+**Abstract**:
+Today, the world is facing many challenges, and one of the most pressing is the humanitarian crisis caused by the war in Ukraine. A large number of people have been displaced and left without homes. That's why the need for scalable, adaptable housing solutions is highly relevant. This talk presents a new way of using computational tools—specifically, an adapted version of the Wave Function Collapse (WFC) algorithm—to design modular housing estates. The applied heuristics allow the solutions o meet specific project requirements, generating various modular settlement designs that consider functionality and social aspects. The talk will include examples of generated modular arrangements, highlighting the potential of this approach in real-world applications.
+**Biogram**:
+Anastasiya Pechko is a first-year PhD student in Technical Computer Science with a master's degree in computer game science (2023). She is a member of the Neu3D research group led by Dr. Przemysław Spurek and is also involved in the project "Effective Rendering of 3D Objects Using Gaussian Splatting in an Augmented Reality Environment" under the FIRST TEAM FENG programme of the Foundation for Polish Science. Her research focuses on computer-aided design and neural rendering—particularly Gaussian Splatting. In addition, Anastasiya has a deep interest in the synergy between art and algorithms, with a particular fascination for complex, emergent behaviors in digital systems.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-05-08 ====
+<WRAP column 15%>
+{{ :aira:gianluca-guglielmo-foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Gianluca Guglielmo,  PhD @ Tilburg University
+**Title**: From Video Games to Real-life ”Games”: The Emergence of Real-life Expertise in (Serious) Video Games.
+**Abstract**:
+Can real-life behaviors carry over into digital environments like serious and video games? Do we make decisions and respond to events in simulated settings the same way we do in the real world? These are the underlying questions that Gianluca Guglielmo will explore in this talk. Gianluca Guglielmo is a researcher who recently obtained his PhD in Cognitive Science and Artificial Intelligence at Tilburg University (Netherlands). In this seminar presentation, Gianluca will present some results of his PhD project, which was conducted in collaboration with Port of Rotterdam. During this project, he focused on using serious games to address some future challenges that the Port of Rotterdam will face in the future. Such challenges include the green transition, in line with the European Green Deal goals, as well as identifying new potential employees. These objectives are based on the idea that serious games, like video games, over safe simulated environments where specific skills can emerge and be developed. Throughout his PhD, Gianluca applied methods drawn from cognitive science, data science, and game studies. He combined non-invasive techniques to track physiological responses with machine learning algorithms to evaluate the effectiveness of a fordable and scalable approaches that can be used both in research with limited resources and in business contexts. The methods proposed may be applied not only to serious and video games but also to other screen-based tasks across different domains. The results presented in this seminar presentation will provide evidence that skills acquired in real life also manifest in serious games and that this transferability overs a solid foundation for using serious games to simulate future developments within companies and to help identify
+the experts of tomorrow."
+**Biogram**:
+Gianluca Guglielmo is an Italian researcher born and raised in Milan. He recently earned his PhD in Cognitive Science and Artificial Intelligence at Tilburg University. His PhD project, conducted in collaboration with the Port of Rotterdam, focused on decision-making, expertise, and the use of serious games. The project aimed to demonstrate how serious games can be powerful tools not only for communicating innovative business concepts to stakeholders but also for identifying and selecting new employees.
+Academically, Gianluca has a diverse and interdisciplinary background. He began with a Bachelor's degree in Philosophy at Università Statale di Milano, where he specialized in
+philosophy of science writing a thesis focused on game theory and evolutionary game theory. His interest in philosophy reflects his strong belief that the way we define a problem
+fundamentally shapes how we investigate it and the research outcomes we obtain. He then pursued a Master's in Cognitive Science and Neuroscience through a joint program at
+Università Statale di Milano and Maastricht University, majoring in psychopharmacology. During this time, he joined a research team at San Paolo Hospital in Milan, working on the
+clinical use of ketamine as a fast-action antidepressant for patients with major depressive disorder. His contributions helped produce the first Italian case series on this treatment, which also formed the basis of his master’s thesis. Before starting his PhD, Gianluca completed a second Master’s in Cognitive Science and
+Artificial Intelligence. During this period, he worked as a student assistant on a project employing video games to study moral decision-making. This research culminated in a
+conference paper titled "The Temperature of Morality: A Behavioral Study on the Efect of Moral Decisions on Facial Thermal Variations in Video Games."
+</WRAP>
+<WRAP clear></WRAP>
+==== 2025-04-24 ====
+<WRAP column 15%>
+{{ :aira:natalia-wojak-strzelecka-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**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.
+</WRAP>
+<WRAP clear></WRAP>
 ==== 2025-04-03 ====