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--- aira:start [2024/03/21 15:16] – [Schedule Summer 2024] sbk
+++ aira:start [2024/05/20 10:26] (current) – [Schedule Summer 2024] sbk
@@ Line 14: / Line 14: @@
 ===== Schedule Summer 2024 =====
+  * **[RESEARCH TRACK] 2024.05.23**: Jowita Guja [[#20240523| On translating VR into philosophy. Experiences from the EduVRLab Laboratory]]
+      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1716197447658?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: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+      * Presentation slides: {{ |Download}}
+  * **[DOCTORAL TRACK] 2024.05.16**: Mateusz Bułat [[#20240516| Image analysis for specialised therapy support]]
+      * 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: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+      * Presentation slides: {{ |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]]
+    * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/ER6j9GMyzdFEm7NAQ8AAOIkBjNsMOP2Dwtn7i_I9sunc9w?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=o17d0R|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+    * Presentation slides: {{ :aira:slides-20240509-jason-jung.pdf |Download}}
+  * **[DOCTORAL TRACK] 2024.04.25**: Bortłomiej Małkus [[#20240425| Interpretable Time Series Classification With Prototypical Parts]]
+      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1713853200005?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/EXQKuYkW3VFMr94bDgX3kR8BEDi5SYRLog-xkyoNA0ONJA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=T6Cnc7|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+      * Presentation slides: {{ |Download}}
+  * **[DOCTORAL TRACK] 2024.04.18**
+    * Farnoud Ghasemi [[#20240418| Performance Optimization of the Platforms in Two-sided Mobility Market]] and
+    * Michał Bujak [[#20240418| Optimising network efficiency in the epidemic scenario]]
+      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1712917407313?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/ERZYnhG5-AxDlO4VNduu7HABpd5dgrDq48OwvVoN51KaMA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=58V83J|View 1st]] [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/ETU22uGwNR1HttjQlT6NV20BRIuwqdr3ci2BugNHxcSc0Q?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=MU7gVk|View 2nd]] (if you are not UJ employee, ask Szymon Bobek for access)
+      * Presentation slides: {{:aira:slides-20240418-farnoud-ghasemi.pdf|Download 1st}} {{ :aira:slides-20240418-michal-bujak.pdf |Download 2nd}}
+  * **[RESEARCH TRACK] 2024.04.04**: Barbara Strug [[#20240404| Evolutionary methods in automatic  floor layout generation ]]
+    * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1712065963983?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/EXOuAVHZ8b5Lo2YuEDBT4v0BSrXrtbXULPj9cRXDKInXwg?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=7XUsYo|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+    * Presentation slides: {{ :aira:slides-20240404-barbara-strug.pdf |Download}}
+  * **[RESEARCH TRACK] 2024.03.28**: Jarosław Wąs [[#20240328| Complex Collective Systems]]
+    * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1711361594803?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: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+    * Presentation slides: {{|Download}}
   * **[DOCTORAL TRACK] 2024.03.21**: Maciej Szelążek [[#20240321| Using ML and XAI for decision support in Business Intelligence analysis]]
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1710834494210?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/EXnHujEGfFNBv8fKxFovZWsB6_EgZ-9OpaaaMWxzuSTW8g?e=rQ9U09&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-    * Presentation slides: {{ |Download}}
+    * Presentation slides: {{ :aira:slides-20240321-maciej-szelazek.pdf |Download}}
   * **[RESEARCH TRACK] 2024.03.14**: Mateusz Ślażyński [[#20240314| Formal Representation and Synthesis of Local Search Neighborhoods]]
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1710282318235?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 290: / Line 320: @@
 ===== Presentation details =====
+==== 2024-05-23 ====
+<WRAP column 15%>
+{{ :aira:jowita-guja-foto.jpeg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Dr hab Jowita Guja, Professor @ AGH University of Science and Technology
+**Title**: On translating VR into philosophy. Experiences from the EduVRLab Laboratory
+**Abstract**:
+Until a few years ago, VR technology seemed a narrow niche, inaccessible to the average viewer. The rapid development, increase in accessibility and popularity we are now seeing are revealing new potential for immersive technologies. Perhaps they are capable of transforming our lives to a degree comparable to how smartphones have changed them? Based on the EduVRLab Laboratory's research and implementation projects, we will look at the philosophical problems that this perspective reveals to us and also look at Virtual Reality as an area that we can creatively manage while changing the face of philosophy itself.
+**Biogram**:
+Dr hab Jowita Guja, Prof. AGH -- Philosopher and cultural studies scholar, PhD in Cultural and Religious Studies. Her research interests include virtual reality, philosophical anthropology and the analysis of popular culture He heads the Department of Information Technology and Media at the
+Faculty of Humanities at the AGH University of Science and Technology, where he teaches, among other things, cognitive science, cultural theory, contemporary literature and the design and use of VR and AR technologies.
+She is among the founders of the EduVRLab Virtual Reality Research Laboratory at AGH in Krakow. As of 2019, she serves as its director. She is co-author of the experimental app 'VRSophy: The Cave' which is a VR adaptation of a Platonic text.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-05-09 ====
+<WRAP column 15%>
+{{ :aira:jason-jung-foto.jpeg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Jason J. Jung, Full Professor @ Chung-Ang University, Korea
+**Title**: Deep Learning for Anomaly Detection in Multivariate Time Series Approaches, Applications, and Challenges
+**Abstract**:
+Anomaly detection has recently been applied to various areas, and
+several techniques based on deep learning have been proposed for the
+analysis of multivariate time series. In this talk, I will talk about
+how to classify the anomalies into three types, namely abnormal time
+points, time intervals, and time series, and review the
+state-of-the-art deep learning techniques for the detection of each of
+these types. Long short-term memory and autoencoders are the most
+commonly used methods for detecting abnormal time points and time
+intervals. In addition, some studies have implemented dynamic graphs
+to examine relational features between the time series and detect
+abnormal time intervals. However, anomaly detection still faces some
+limitations and challenges, such as the explainability of anomalies.
+Many studies have focused only on anomaly detection methods but failed
+to consider the reasons for the anomalies. Therefore, increasing the
+explainability of anomalies is an important research topic in anomaly
+detection.
+**Biogram**:
+Jason J. Jung is a Full Professor in Chung-Ang University, Korea,
+since September 2014. Before joining CAU, he was an Assistant
+Professor in Yeungnam University, Korea since 2007. Also, he was a
+postdoctoral researcher in INRIA Rhone-Alpes, France in 2006, and a
+visiting scientist in Fraunhofer Institute (FIRST) in Berlin, Germany
+in 2004. He received the B.Eng. in Computer Science and Mechanical
+Engineering from Inha University in 1999. He received M.S. and Ph.D.
+degrees in Computer and Information Engineering from Inha University
+in 2002 and 2005, respectively. His research topics are knowledge
+engineering on social networks by using many types of AI
+methodologies, e.g., data mining, machine learning, and logical
+reasoning. Recently, he has been working on intelligent schemes to
+understand various social dynamics in large scale social media.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-04-25 ====
+<WRAP column 15%>
+{{ :aira:bartlmiej-malkus-foto.jpeg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Bartłomiej Małkus, PhD Candidate @ Jagiellonian University
+**Title**: Interpretable Time Series Classification With Prototypical Parts
+**Abstract**:
+Time series data is one of the most popular data modality in critical domains such as industry and medicine. The demand for algorithms that not only exhibit high accuracy but also offer interpretability is crucial in such fields, as decisions made there bear significant consequences. Prototypical parts network, like ProtoPNet gained significant interest in the field of image analysis. Although they offer competitive accuracy and ante-hoc explainability, their application to the field of multivariate time series classification is relatively weakly research area. The presentation will demonstrate a novel approach to interpretable classification of multivariate time series data, through substantial enhancements and adaptation of ProtoPNet architecture. Our method is tailored to tackle the unique challenges of time series analysis, including capturing dynamic patterns and varying feature significance.
+**Biogram**:
+Bartłomiej Małkus is a PhD candidate at the Jagiellonian University in Technical Computer Science since 2021. He received BSc and MSc degrees in Computer Science on AGH University of Science and Technology. His field of interests are interpretable AI techniques applied to time series analysis and neurosymbolic AI. Commercially, he works in Persistent Systems on cloud, data lake and data warehouse solutions.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-04-18 ====
+<WRAP column 15%>
+{{ :aira:farnoud-ghasemi-foto.png?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Farnoud Ghasemi, PhD Candidate @ Jagiellonian University
+**Title:** Performance Optimization of the Platforms in Two-sided Mobility Market
+**Abstract:**
+The presentation will focus on analyzing two-sided mobility markets involving platforms such as Uber and Lyft with agent-based modeling. The MoMaS framework will be introduced, which models two-sided mobility markets as complex systems with intricate, non-linear interactions among the involved parties (including travelers, drivers, and platforms). Eventually, the integration of Reinforcement Learning into the proposed framework will be discussed explaining how RL-based platform strategies can improve platform performance.
+**Biogram:**
+Farnoud is currently a PhD student within the Faculty of Mathematics and Computer Science at the Jagiellonian University. His PhD research under the supervision of Dr. Rafal Kucharski, focuses on studying behavioural dynamics of two-sided mobility using agent-based microsimulation. He received his Bachelor’s degree in Civil Engineering at the University of Tabriz and he completed his MSc degree in Transport Systems at the Sapienza University of Rome.
+</WRAP>
+<WRAP clear></WRAP>
+<WRAP column 15%>
+{{ :aira:michal-bujak-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Michał Bujak, PhD Candidate @ Jagiellonian University
+**Title:** Optimising network efficiency in the epidemic scenario
+**Abstract:**
+We consider the problem of reducing virus spreading in the system network (graph) while keeping the utility of the whole system at the maximal level. To balance the above two opposite goals, we propose Deep Epidemic Efficiency Network (DEEN), an unsupervised clustering method, which optimises graph efficiency in an epidemic scenario using Graph Convolutional Neural Networks and a novel loss function. Given the desired virus transmission, it constructs a graph for which the predefined transmission rate is not exceeded and utility function is maximised. We show that proposed method successfully solves three real-life problems: ride-pooling service in New York City, economic exchange between regions in Poland, and information sharing via peer-to-peer network.
+In particular, by dividing 150 New York taxi travellers into four groups our method increases epidemic threshold more than twofold at the cost of reducing utility only by 13%, significantly outperforming benchmark methods. The model can be instrumental in future pandemic outbreaks when we need to balance between maintaining efficiency and preventing the spread of the virus.
+**Biogram:**
+A third-year phd student of technical computer science at the Jagiellonian University. He has a background in applied mathematics with a focus on the probability theory. Currently, he is a part of a team working on transportation problems in the theoretical framework. His main research area is network science (both analytical and AI-based approaches). Out of the academia, he has experience in quantitative analysis for the major global investment banks.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-04-04 ====
+<WRAP column 15%>
+{{ :aira:barbara-strug-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Barbara Strug, Professor @ Jagiellonian University
+**Title**: Evolutionary methods in automatic  floor layout generation
+**Abstract**:
+The problem of floor layout design involves the automatic generation of space arrangements within a predefined geometric area. This presentation will provide an overview of the research carried out in the Department of Design and Computer Graphics in recent years. In particular, it will cover different representations of designs, appropriate specialized evolutionary operators, and fitness evaluation methods.
+**Biogram**:
+Barbara Strug received her PhD in Computer Science from the Institute of Fundamental Technological Research of the Polish Academy of Sciences (IPPT PAN) in Warsaw in 2002 and DSc (habilitation) in Computer Science from AGH in 2014. She is an associated professor at the Institute of Applied Computer Science, Jagiellonian University. Her research interest include computer aided design  evolutionary computations and graph  based learning, graph mining and transformations. She is an author of over 90 papers and attended numerous conferences.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-03-28 ====
+<WRAP column 15%>
+{{ :aira:jaroslaw-was-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Jarosław Wąs, Full Professor @ AGH University of Krakow
+**Title**: Complex Collective Systems
+**Abstract**:
+The topic of the presentation will be collective aspects of
+complex systems.  Some specifics models of complex systems wwill be
+presented such as: crowds, skiers, vehicle traffic in an urban
+environment and autonomous vehicle traffic. These models are applied and
+developed within our international projects and cover practical aspects
+of collective intelligence. The projects used methods such as: agent
+technologies, cellular automata, rough and fuzzy sets, etc.
+**Biogram**:
+Professor of technical sciences in the discipline of computer
+science - specialization: artificial intelligence and computational
+intelligence. He's working at the Applied Computer Science department of
+AGH. He is interested in modeling and simulation of complex systems. In
+particular, his area of interest is data-driven modeling and the use of
+the agent-based modeling paradigm. He is interested in the applications
+of advanced algorithms and artificial intelligence in engineering, as
+well as in areas such as IoT, ambient intelligence and computational
+intelligence. To date, he has supervised 5 PhDs in Computer Science and
+AI.
+</WRAP>
+<WRAP clear></WRAP>
 ==== 2024-03-21 ====