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--- aira:start [2025/11/18 10:01] – [Schedule Autumn 2025] mtm
+++ aira:start [2025/12/04 16:30] (current) – [2025-12-04] mzk
@@ Line 22: / Line 22: @@
 ===== Schedule Autumn 2025 =====
-  * **[PHD TRACK] 2025.11.20**: Syed Muhammad Hamza Zaidi, PhD Candidate @  Otto von Guericke University Magdeburg, [[#section20251120|Localizing the invisible: Graph Neural Networks for Biomedical Signals.]]
+  * **[RPHD 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/39537797907603?p=jjjJD0gjg5LcGl6eL9|MS Teams]]
+    * 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 .]]
@@ Line 32: / Line 42: @@
     * 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:  TDA
+    * 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 Lukas Sykora @ Prague University of Economics and Business, [[#section20251106|LLM-based feature generation from text for interpretable machine learning.]]
+  * **[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:  TDA
+    * 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.]]
@@ Line 47: / Line 60: @@
     * 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:  TDA
+    * 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.]]
@@ Line 182: / Line 195: @@
       * 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]]
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-==== 2025-11-20 ====
+==== 2025-12-11 ====
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-{{ :aira:zaidi-foto.png?200| }}
+{{ :aira:anna-lippolis-foto.png?200| }}
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-**Speaker**: Syed Muhammad Hamza Zaidi, PhD Candidate @  Otto von Guericke University Magdeburg
+**Speaker**: Anna Sofia Lippolis, PhD Candidate @ University of Bologna
-**Title**: Localizing the invisible: Graph Neural Networks for Biomedical Signals.
+**Title**: Enhancing Knowledge Engineering with LLMs.
 **Abstract**:
-The respiratory system can be significantly affected by thoracic injuries, which may lead to complications such as lung dysfunction. Immediate diagnosis, along with the precise location of these injuries is crucial as it allows targeted medical interventions, reduces unnecessary treatments and accelerates patient recovery. Traditional diagnostic tools like X-rays and CT scans offer high resolution imaging but rely on radiation exposure and hospital visits, making continuous monitoring impractical.
+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.
-This talk introduces a wearable sensor network coupled with Temporal Graph Neural Networks (TGCNs) to detect and localize breathing abnormalities non-invasively and in real time. By modeling the thorax as a graph of 14 IMU-based sensor nodes, the system captures both spatial dependencies and temporal dynamics of chest movement. The proposed model pinpoints the exact location and severity of breathing irregularities, such as shallow or asymmetric breathing patterns while maintaining high interpretability through spatially grounded predictions.
-The presentation will cover the underlying method, synthetic to real data strategy and the vision of a smart chest wearable system for continuous at home monitoring. Applications range from post-injury recovery and chronic disease management to early warning systems in preventive healthcare.
 **Biogram**:
-Zaidi is a PhD researcher at Otto-von-Guericke University Magdeburg, working at the Knowledge Management & Discovery Lab (KMD) under the supervision of Prof. Myra Spiliopoulou. His research focuses on graph neural networks, wearable sensor systems and feature importance for biomedical applications.
+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.
-He has developed a temporal graph neural network framework to detect and localize respiratory abnormalities using chest-mounted IMU sensor networks. Beyond respiratory monitoring, his research spans spatio-temporal deep learning, health signal processing and interpretable machine learning methods aimed at bridging the gap between AI research and clinical practice.
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+==== 2025-12-04 ====
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+{{ :aira:bartlmiej-malkus-foto.jpeg?200| }}
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+**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.
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+==== 2025-11-27 ====
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+{{ :aira:aleksander-mendyk-foto.png?200| }}
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+**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>
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 ==== 2025-11-13 ====