Differences

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

--- aira:start [2026/04/13 06:55] – [Schedule Spring 2026] mzk
+++ aira:start [2026/06/21 08:05] (current) – [Schedule Spring 2026] mtm
@@ Line 21: / Line 21: @@
 ===== Schedule Spring 2026 =====
+  * **[RESEARCH TRACK] 2026.06.18**: Elżbieta Sroka,  assistant professor @ Jagiellonian University, [[#section20260618|Exploring the use of artificial intelligence in digital cultural heritage research in european research centres.]]
+    * Meeting link: [[https://teams.microsoft.com/meet/355163613268564?p=EFfNrzZY2iaUuHn8OR|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQASrWSYnTz1RqHEehWmxIxvAXSok1ayAAu4-zQC2vwtQ08?e=zMyadc|View]]
+    * Presentation slides:  TDA
+  * **[PHD TRACK] 2026.06.11**: Sabri Manai,  PhD Candidate @ Jagiellonian University, [[#section20260611|Transparent and Adaptive AI for Human-Guided Decision Support.]]
+    * Meeting link: [[https://teams.microsoft.com/meet/351900951227620?p=Qqx0dV7zoNZpztQhB1|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQBfPoiQ1E7DRrskykUzslmoAWUM6quArL0DOTPXGfj8xQU?e=Eg6GHs|View]]
+    * Presentation slides:  {{:aira:slides-sabri-manai-2026-06-11.pdf|Download}}
+  * **[RESEARCH TRACK] 2026.05.28**: Soheila Molaei,  senior researcher @ University of Oxford, [[#section20260528|From Graphs to Graph Neural Networks: Foundations and Applications in Healthcare]]
+    * Meeting link:[[https://teams.microsoft.com/meet/377493387003987?p=TVBcvsqGs42CdT7U9T|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQC8c4XxDZvTQ70h5GCoIrRwAYBNQyagBm3DaEdsBovDMNQ?e=8fwmHj|View]]
+    * Presentation slides: {{:aira:slides-soheila-molaei-2026-05-28.pdf|Download}}
+  * **[RESEARCH TRACK] 2026.05.21**: Jan Argasiński,  assistant professor @ Jagiellonian University, [[#section20260521|Computational Neuroscience at Sano (Centre for Computational Medicine): Current Research and a Spotlight on “A Tract Density Biomarker for Survival Prediction in Glioblastoma”]]
+    * Meeting link:[[https://teams.microsoft.com/meet/325587359264791?p=u7BhZIs7GL9MEk6U2J|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQCsLO1arlhfTrr7jI00i90eAQxEquR7e1UpPO6YKLrYbDI?e=g0rCPL|View]]
+    * Presentation slides:  {{:aira:slides-jan-argasinski-2026-05-21.pdf|Download}}
+  * **[RESEARCH TRACK] 2026.05.14**: Grzegorz Korcyl,  assistant professor @ Jagiellonian University, [[#section20260514|Innovative data processing methods on field programmable gate arrays (FPGAs)]]
+    * Meeting link:[[https://teams.microsoft.com/meet/373763391192108?p=pFLRPFxMHuCjgEGpfY|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQC_U4aUF3QqSrlH-oUgaQykASQMD0HRn4AVp3Xk6CFuFgk?e=s6Z97b|View]]
+    * Presentation slides:  {{:aira:slides-grzegorz-korcyl-2026-05-14.pdf|Download}}
   * **[RESEARCH TRACK] 2026.04.16**: Patrick Altmeyer,  Researcher @ Delft University of Technology, [[#section20260416|Explaining Models or Modelling Explanations? Counterfactual Explanations and Algorithmic Recourse for Trustworthy AI]]
     * Meeting link:[[https://teams.microsoft.com/meet/372057591033533?p=u4Kxst3Eu3DsxRwxw8|MS Teams]]
-    * Recording:  TDA
+    * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQBnzx56wup2SqdgH7vcGMsTAVLulmE_zJmARUq1q1R49og?e=HHKlf1|View]]
-    * Presentation slides:  TDA
+    * Presentation slides: {{:aira:slides-patrick-altmeyer-2026-04-16.pdf|Download}}
   * **[PHD TRACK] 2026.04.09**: Dmytro Polishchuk,  PhD Candidate @ Jagiellonian University, [[#section20260409|A Time-Aware GitHub Mining Framework for Empirical Software Quality Studies.]]
@@ Line 555: / Line 581: @@
+==== 2026-06-18 ====
+<WRAP column 15%>
+{{ :aira:elzbieta-sroka-foto.jpg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Elżbieta Sroka,  assistant professor @ Jagiellonian University
+**Title**: Exploring the use of artificial intelligence in digital cultural heritage research in european research centres.
+**Abstract**:
+This presentation provides an overview of the current stage of research investigating the use of artificial intelligence (AI) in digital cultural heritage research within european research centres and cultural heritage institutions, conducted as part of the CHExRISH project. The study explores how AI is being adopted and applied in digital cultural heritage research, focusing on methods, tools, workflows, research infrastructures, and institutional practices. The study is based on fieldwork conducted in selected European countries and includes research visits, expert interviews, and observations. The fieldwork has so far covered research and cultural heritage institutions in eight European countries. The presentation outlines the research design, methodological approach, and data collection methods employed in the study, as well as the scope of the research and the types of institutions involved. It also provides an overview of the institutions visited to date. As the analysis of the empirical material is still ongoing, the presentation does not seek to offer final conclusions. Instead, it presents preliminary observations from the fieldwork, discusses emerging themes, and outlines the next stages of the research, including planned data analysis and future dissemination activities.
+**Biogram**:
+Elżbieta Sroka, PhD, certified UX designer, assistant professor at the Jagiellonian University in Krakow, Poland, Faculty of Physics, Astronomy and Applied Computer Science, at the Department of Human-Centred Artificial Intelligence and also Senior Specialist at Łukasiewicz Research Network – Institute of Artificial Intelligence and Cybersecurity 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>
+==== 2026-06-11 ====
+<WRAP column 15%>
+{{ :pub:about_us:smi.jpeg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Sabri Manai,  PhD Candidate @ Jagiellonian University
+**Title**: Transparent and Adaptive AI for Human-Guided Decision Support.
+**Abstract**:
+This talk presents Sabri's research into making AI systems more transparent, trustworthy, and useful for human decision-making. Across three lines of work, he develops explainable recommendations tailored to multiple stakeholders, grounds industrial anomaly detection in domain knowledge through knowledge graphs, and introduces counterfactual-guided hyperparameter optimisation with a conversational interface, showing that interactive explanation measurably outperforms static alternatives.
+Together these works reveal a common thread and a shared limitation: current explainable AI systems are built around one-shot outputs. They tell users why a decision was made, but offer no principled response when users push back. The second part of the talk examines this open problem: how AI systems should handle disagreement, adapt to feedback, and be evaluated not on perceived trust but on whether they genuinely improve human decisions.
+**Biogram**:
+Sabri Manai is a PhD candidate in Technical Computer Science at Jagiellonian University in Kraków, where his research focuses on explainable AI and pattern detection in multimodal data. His work investigates how human feedback and domain knowledge can improve the transparency and reliability of AI systems.
+He holds a Master’s degree in Software Systems Engineering from the Universitat Politècnica de València and a Bachelor’s degree in Computer Science from the South Mediterranean University in Tunis. Previously, he worked on AI-driven urban analytics within the Valencia Smart City project at Idrica and contributed to mobile development and cloud integration at Peaksource.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2026-05-28 ====
+<WRAP column 15%>
+{{ :aira:soheila_molaei_foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Soheila Molaei,  Senior researcher @ University of Oxford
+**Title**: From Graphs to Graph Neural Networks: Foundations and Applications in Healthcare
+**Abstract**:
+In this talk, I will introduce the foundations of graph theory and graph neural networks, starting from intuitive examples of real-world graphs such as social networks, molecules, road networks, and biomedical interaction networks. I will explain why graph-structured data challenges standard machine learning assumptions, and how GNNs use message passing and representation learning to model complex relationships. The talk will then cover common GNN tasks, including node classification, link prediction, and graph classification, before introducing key models such as graph convolutional networks and graph attention networks. I will conclude with applications in healthcare, including medical graphs, drug discovery, and patient modelling.
+**Biogram**:
+Soheila Molaei is a senior researcher in the Department of Engineering Science at the University of Oxford. Her research focuses on artificial intelligence and machine learning, particularly graph neural networks, federated learning, neuro-symbolic AI, and learning from multimodal and heterogeneous data, with applications in complex real-world and healthcare domains.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2026-05-21 ====
+<WRAP column 15%>
+{{ :aira:jan-argasinski-foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Jan Argasiński,  assistant professor @ Jagiellonian University
+**Title**: Computational Neuroscience at Sano (Centre for Computational Medicine): Current Research and a Spotlight on “A Tract Density Biomarker for Survival Prediction in Glioblastoma”
+Abstract:
+Computational neuroscience provides powerful tools to better understand brain structure and function, and to translate this knowledge into clinically relevant biomarkers for neurological disease. In this seminar, I will briefly survey ongoing projects in brain modelling, advanced neuroimaging analysis, and machine learning for neurological and psychiatric disorders, with a particular emphasis on how these methods bridge basic science and clinical practice. The second part of the talk will spotlight the development of a tract density–based biomarker aimed at predicting survival in patients with glioblastoma, illustrating the full pipeline from diffusion MRI processing and tractography through feature extraction to predictive modelling and validation. By showcasing both the broader research landscape at Sano and this focused case study, the seminar will highlight how computational approaches can support prognosis, treatment planning, and ultimately more personalized care in neuro-oncology.
+Biogram:
+Jan Argasiński, PhD, is a researcher at the Department of Human-Centered Artificial Intelligence, Faculty of Physics, Astronomy and Applied Computer Science, Jagiellonian University in Kraków. He also leads the Computational Neuroscience Group at Sano – Centre for Computational Medicine. His research focuses on computational neuroscience, neuromorphic computing, affective computing, serious games, and VR/AR.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2026-05-14 ====
+<WRAP column 15%>
+{{ :aira:grzegorz-korcyl-foto.jpeg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Grzegorz Korcyl,  assistant professor @ Jagiellonian University
+**Title**: Innovative data processing methods on field programmable gate arrays (FPGAs)
+**Abstract**:
+Significant computational capabilities of modern FPGAs, combined with high-level methodologies for developing their configuration, open new areas where unique features of this technology can be exploited, such as real-time processing, deterministic latency, reconfigurability, and low power consumption. As part of my research, I decided to explore the capabilities of this technology across data with various characteristics, develop a set of best-practice data processing techniques, and verify them using real-world use cases originating from large scale physics-experiments, medical imaging and computer networks.
+**Biogram**:
+Expert in the field of Field Programmable Gate Arrays (FPGA) technology with many years of experience acquired while working in international research projects. Ph.D. in technical sciences in the discipline of computer science obtained for the design and implementation of the data acquisition system for the HADES experiment detector system, which has also been used in dozens of other applications. Popularizer of FPGA technology by organizing conferences and training program in this field on a national scale. Since 2018 conducts research on the use of FPGAs in subjects related to processing massive amount of streamlined data such as in High Performance Computing, low and fixed latency networking. Technical coordinator of the Data Acquisition System in the PANDA experiment.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2026-04-16 ====
+<WRAP column 15%>
+{{ :aira:patrick-altmeyer-foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Patrick Altmeyer,  Researcher @ Delft University of Technology
+**Title**: Explaining Models or Modelling Explanations? Counterfactual Explanations and Algorithmic Recourse for Trustworthy AI
+**Abstract**:
+Counterfactual explanations (CE) and algorithmic recourse (AR) have emerged as promising approaches towards explaining opaque machine learning models and empowering individuals affected by them. This seminar will explore unexpected challenges and new opportunities in this context and demonstrate how counterfactuals can be used to improve the trustworthiness of models . It will summarize some of the main findings of Patrick's Ph.D. research: https://www.patalt.org/thesis/. The slides will be made available on the website ahead of the seminar.
+**Biogram**:
+Patrick is a trained economist, computer scientist, and researcher. In his research, he has challenged long-standing paradigms in explainable AI, developed novel methods to make AI more trustworthy, and questioned hyperbolic claims about AGI. Patrick is also experienced an open-source software developer and the core developer and maintainer of Trustworthy Artificial Intelligence in Julia (Taija).
+</WRAP>
+<WRAP clear></WRAP>
 ==== 2026-04-09 ====