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

--- aira:start [2026/03/13 11:03] – [2026-03-12] mzk
+++ aira:start [2026/06/21 08:05] (current) – [Schedule Spring 2026] mtm
@@ Line 22: / Line 22: @@
 ===== Schedule Spring 2026 =====
-  * **[PHD TRACK] 2026.01.08**: Maciej Mozolewski, PhD Candidate @ Jagiellonian University, [[Beyond Heatmaps: Explaining Time Series with Post-hoc Attribution Rules and Counterfactuals.]]
+  * **[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/33976511999142?p=J72QcmpdpxsUSETlIe|MS Teams]]
+    * Meeting link: [[https://teams.microsoft.com/meet/355163613268564?p=EFfNrzZY2iaUuHn8OR|MS Teams]]
-    * Recording:  TBA
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQASrWSYnTz1RqHEehWmxIxvAXSok1ayAAu4-zQC2vwtQ08?e=zMyadc|View]]
-    * Presentation slides:  TBA
+    * 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: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQBnzx56wup2SqdgH7vcGMsTAVLulmE_zJmARUq1q1R49og?e=HHKlf1|View]]
+    * 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.]]
+    * Meeting link: [[https://teams.microsoft.com/meet/357521097755345?p=DozT4DQGJzfZH4PXfD|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQBZU45fdhPPTJ780XqynOeuAVb4v5B4fIOgb0KD1EKkI5U?e=aGWY1Z|View]]
+    * Presentation slides:  TDA
+  * **[PHD TRACK] 2026.03.26**: Mateusz Bułat,  PhD Candidate @ Jagiellonian University, [[#section20260326|Formal Grammar Transducers in medical image analysis and segmentation correction]]
+    * Meeting link:[[https://teams.microsoft.com/meet/349382193259763?p=arFNsNSL0nyoywz4lI|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQAeWFGK6mVbT5yfQdF9HrLmAXl55pp41keoHp8rmGsv7lM?e=uYof0z|View]]
+    * Presentation slides:  {{:aira:slides-mateusz-bulat-2026-03-26.pdf|Download}}
+  * **[PHD TRACK] 2026.03.19**: Maciej Mozolewski,  PhD Candidate @ Jagiellonian University, [[#section20260319|Beyond Heatmaps: Explaining Time Series with Post-hoc Attribution Rules and Counterfactuals]]
+    * Meeting link:[[https://teams.microsoft.com/meet/33976511999142?p=J72QcmpdpxsUSETlIe|MS Teams]]
+    * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQClgAkSalrIQY9WZA3zoYEGAYpG_AE0hbLjYOMDK1lQ-74?e=k2FNg9|View]]
+    * Presentation slides:  {{:aira:slides-maciej-mozolewski-2026-03-19.pdf|Download}}
   * **[RESEARCH TRACK] 2026.03.12**: Aleksandra Nabożny,  Assistant Professor @  Akademia Leona Koźmińskiego, [[#section20260312|What is and how to classify credibility of online health information?]]
     * Meeting link:[[https://teams.microsoft.com/meet/3692206790088?p=w5o4kDyNtT0vCV2INH|MS Teams]]
     * Recording:  [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/IQBrCjQaK0qpQqfuxU71nfp2Aa3dTo5YvAzqr6LZjtCknrg?e=8fEfFn|View]]
-    * Presentation slides:  TDA
+    * Presentation slides:  {{:aira:slides-aleksandra-nabozny-2026-03-12.pdf|Download}}
   * **[RESEARCH TRACK] 2026.03.05**: Jaromir Savelka,  researcher associate @ Carnegie Mellon University, [[#section20260305|Large Language Models and Empirical Legal Studies.]]
@@ Line 540: / 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 ====
+<WRAP column 15%>
+{{ :aira:dmytro-polishchuk-foto.png?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Dmytro Polishchuk,  PhD Candidate @ Jagiellonian University
+**Title**: A Time-Aware GitHub Mining Framework for Empirical Software Quality Studies.
+**Abstract**:
+This research focuses on designing an automated system to assess the quality of GitHub repositories based on a predefined quality model. The proposed approach evaluates repositories using a range of metrics, including commit history and its associated metadata (such as size, timestamps, and descriptions), code coverage, pull request review duration, issue resolution time, the number of open issues, code churn, and code complexity.
+In addition, the system may incorporate further quality indicators, potentially drawing on established frameworks such as ISO/IEC 25010:2011, to provide a more comprehensive and standardized evaluation.
+**Biogram**:
+Software engineer with a background in the telecom domain, specializing in network management systems and Software Defined Networking (SDN). Experienced in performance engineering, including byte code instrumentation, and has worked across various technologies, including IoT. Previously contributed to major companies like Ericsson, Cisco Systems, and Playtech.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2026-03-26 ====
+<WRAP column 15%>
+{{ :aira:mateusz-bulat-foto.JPG?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Mateusz Bułat,  PhD Candidate @ Jagiellonian University
+**Title**: Formal Grammar Transducers in medical image analysis and segmentation correction
+**Abstract**:
+Syntactic Pattern Recognition is data analysis approach stemming from formal grammars, formal languages and syntax analyser development. It’s particularly effective in analysing structures, both those found in natural world and in human-made artifacts. To this day many studies used SPR methods in diagnosis of medical subjects, like hearing impairments in neonates or in commercial field, like for electricity consumption forecast.
+Current PHD candidate’s research focuses on medical image analysis for patients with oligodendroglioma brain cancer. Goal of the endeavour is to support medics job in detecting and contouring cancer changes in brain. The glioma images acquired by MRI means are 2d scans. The segmentation would therefore benefit from a method that would correct it to represent a coherent 3d structure.
+**Biogram**:
+After working for a short time supporting eCRF (electronic Case Report Form) for various medical trials, Mateusz continues education as a first year PhD student at Technical Computer Science, Jagiellonian University. He holds both Bachelor's and Master's degrees in Computer Science, Jagiellonian University.
+He professionally worked on 3D medical image presentation, medical protocol analysis, Clinical Trial information flow and Adverse Event report forms. His current interests include medical image analysis, formal grammar application methods and hybrid AI systems.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2026-03-19 ====
+<WRAP column 15%>
+{{ :aira:mtm-new-foto.jpeg?200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Maciej Mozolewski,  PhD Candidate @ Jagiellonian University
+**Title**: Beyond Heatmaps: Explaining Time Series with Post-hoc Attribution Rules and Counterfactuals
+**Abstract**:
+While complex machine learning models excel in time series classification, standard explanation methods often produce raw numeric feature attributions that fail to provide actionable insights for domain experts. To address this, I introduce PHAR (Post-hoc Attribution Rules), a method that transforms numeric attributions into structured, human-readable rules. By employing rule fusion, this approach localizes decision-relevant segments and expresses them as clear logical conditions directly on the raw series. To scale this logic from individual predictions to global model understanding, I present the Open-Box framework. Acting as an explainer-agnostic global surrogate, it aggregates local rules and resolves overlaps, allowing us to successfully extract tacit domain knowledge that experts might otherwise miss. Complementing these symbolic approaches, I also discuss a prototype-driven framework for generating sparse counterfactual explanations, which highlights the minimal, physiologically plausible signal modifications required to alter a prediction. By visually aligning logical conditions and prototypical examples with temporal patterns, this integrated ecosystem elevates time series explanations from simple scores to actionable knowledge, effectively bridging the gap between complex models and expert decision support.
+**Biogram**:
+Maciej Mozolewski is a PhD Researcher at Jagiellonian University and a member of the GEIST research group led by Prof. Grzegorz J. Nalepa. His work centers on human-centered explainable AI (XAI) for dynamic data, including multivariate time series and explanation visualization. He focuses on post-hoc methods that bridge the gap between complex machine learning models and human-intelligible explanations, specifically through symbolic rules and counterfactuals. With nearly a decade of experience as a software engineer and data scientist, he combines his technical background with an M.A. in Psychology to ground his academic research in real-world human decision-making.
+</WRAP>
+<WRAP clear></WRAP>
 ==== 2026-03-12 ====