Differences

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

--- aira:start [2024/05/07 11:35] – [Schedule Summer 2024] sbk
+++ aira:start [2024/09/06 08:05] (current) – gjn
@@ Line 8: / Line 8: @@
 The program will be published at [[https://aira.geist.re]] 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 secretary [[https://szymon.bobek.re|Szymon Bobek]]
-Scientific coordination: [[https://gjn.re|Grzegorz J. Nalepa]]
+===== Schedule Autumn 2024 =====
+AIRA will restart on 10.10.2024, TBC, stay tuned
 ===== Schedule Summer 2024 =====
-  * **[RESEARCH TRACK] 2024.05.09**: Jason J. Jung [[#20240509| Deep Learning for Anomaly Detection in Multivariate Time (Series: Approaches, Applications, and Challenges) ]]
+  * **[RESEARCH TRACK] 2024.06.27**: José Palma,Juan Botía, Antonio Guillén-Teruel  [[#20240627| Context-Aware learning models: CALM-Project and Concept Drift in Imbalanced Problems]]
-    * Meeting link: [[|MS Teams]]
+      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1719232789438?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)
+      * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-    * Presentation slides: TBA
+      * Presentation slides: {{:aira:slides-jose-palma-20240617.pdf |Download I}} {{ :aira:slides-antonio-guillen-20240627.pdf |Download II}}
+  * **[RESEARCH TRACK] 2024.06.06**: Żaneta Kubic [[#20240606| European Cultural Heritage in Virtual Worlds – why and how : introduction to the IMPULSE project]]
+      * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1717407321901?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/EfkSlUmXWbRGvq0xS6bBZkcBrrRI2bumOitE0ou0VBKC2w?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=3EfKGG|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+      * Presentation slides: {{ :aira:slides-zaneta-kubic-20240606.pdf |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: [[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}}
+  * **[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}}
+      * Presentation slides: {{ :aira:slides-bartek-malkus-20240425.pdf|Download}}
   * **[DOCTORAL TRACK] 2024.04.18**
     * Farnoud Ghasemi [[#20240418| Performance Optimization of the Platforms in Two-sided Mobility Market]] and
@@ Line 36: / Line 51: @@
     * 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]]
+  * **[DOCTORAL TRACK] 2024.03.21**: Maciej Szelążek [[#20240321| Using ML and XAI for decision support in Business Intelligence analysis]] (//Project CHIST-ERA XPM//)
     * 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)
@@ Line 49: / Line 64: @@
     * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EXh23IFS_PVOqhLmFmDgxK0Bl0rv3Fhz7Zb0YXjh9E_gjA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=CAO7EL|View]] (if you are not UJ employee, ask Szymon Bobek for access)
     * Presentation slides: {{ |Download}}
-  * **[RESEARCH TRACK] 2024.01.25**: Rita P. Ribeiro [[#20240125| Online Anomaly Explanations - case study of Metro do Porto]]
+  * **[RESEARCH TRACK] 2024.01.25**: Rita P. Ribeiro [[#20240125| Online Anomaly Explanations - case study of Metro do Porto]] (//Project CHIST-ERA XPM//)
     * **LOCATION: C-2-10 (This meeting will be in hybrid mode: on-site and online)**
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1705678609907?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 86: / Line 101: @@
     * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EfmgG-90BqRDkZ6MeGtQo00B7O_Ob4LVhTWH35Ybnicp0w?e=jDOWQh&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D|View]] (if you are not UJ employee, ask Szymon Bobek for access)
     * Presentation slides: [[https://ujchmura-my.sharepoint.com/:b:/g/personal/szymon_bobek_uj_edu_pl/ETeLeVJ-nhdOhNXFok7MwBsBMpUtU2yQr1PMZDBXvauKLg?e=fyfYGR|Download]]
-  * **[DOCTORAL TRACK] 2023.10.26**: Jakub Jakubowski[[#20231026| Synthetic Data Generator for Predictive Maintenance in Steel Manufacturing ]]
+  * **[DOCTORAL TRACK] 2023.10.26**: Jakub Jakubowski[[#20231026| Synthetic Data Generator for Predictive Maintenance in Steel Manufacturing ]] (//Project CHIST-ERA XPM//)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1697801210427?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/ERQNsbo0SxxDpRL7S3heIgsB4XbUnLmb0wQnnU8ZrlVRcA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZyIsInJlZmVycmFsQXBwUGxhdGZvcm0iOiJXZWIiLCJyZWZlcnJhbE1vZGUiOiJ2aWV3In19&e=crc7i5|View]] (if you are not UJ employee, ask Szymon Bobek for access)
@@ Line 101: / Line 116: @@
 ===== Schedule Summer 2023 =====
-  * **[RESEARCH TRACK] 2023.06.29**: Sepideh Pashami [[#20230629| Domain Adaptation for Predictive Maintenance]]
+  * **[RESEARCH TRACK] 2023.06.29**: Sepideh Pashami [[#20230629| Domain Adaptation for Predictive Maintenance]] (//Project CHIST-ERA XPM//)
     * **LOCATION: C-2-10** (This meeting will be in hybrid mode: on-site and online)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1687248545347?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/EWq81Fq6gW9Pq49RKHA99_0BAzdBFtCX5SKzUM0LoP-ULQ?e=1NzMwJ&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZyIsInJlZmVycmFsQXBwUGxhdGZvcm0iOiJXZWIiLCJyZWZlcnJhbE1vZGUiOiJ2aWV3In19|View]] (if you are not UJ employee, ask Szymon Bobek for access)
     * Presentation slides: TBA
-  * **[TUTORIAL] 2023.06.26**: Sepideh Pashami [[#20230626| Causal Inference and its Connection to Machine Learning]]
+  * **[TUTORIAL] 2023.06.26**: Sepideh Pashami [[#20230626| Causal Inference and its Connection to Machine Learning]] (//Project CHIST-ERA XPM//)
     * **LOCATION: A2-02** (This meeting will be in hybrid mode: on-site and online)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1687174755583?context=%7b%22Tid%22%3a%22eb0e26eb-bfbe-47d2-9e90-ebd2426dbceb%22%2c%22Oid%22%3a%22c96e4eee-96f3-4b0f-88ef-fe65310f5f55%22%7d|MS Teams]]
     * Presentation slides: TBA
-  * **[DOCTORAL TRACK] 2023.06.22**: Michał Kuk [[#20230622| Time-Series Complexity into Understandable Prototypes: A Generic Approach to Machine Learning Explanations in Industrial Processes]]
+  * **[DOCTORAL TRACK] 2023.06.22**: Michał Kuk [[#20230622| Time-Series Complexity into Understandable Prototypes: A Generic Approach to Machine Learning Explanations in Industrial Processes]] (//Project CHIST-ERA XPM//)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1687177218502?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/Efo_-lqip_hAmna_WWF0Xk4BkIYRknKwrfqDftzGYv9-YA?e=0u1o0e&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZyIsInJlZmVycmFsQXBwUGxhdGZvcm0iOiJXZWIiLCJyZWZlcnJhbE1vZGUiOiJ2aWV3In19|View]] (if you are not UJ employee, ask Szymon Bobek for access)
@@ Line 130: / Line 145: @@
     * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EXl1m5mFysRGnsCtoB_wu4ABiCBOZ0_Fhr9g_RFj7r8PpQ?e=dcAyfn|View]] (if you are not UJ employee, ask Szymon Bobek for access)
     * Presentation slides: {{ :aira:slides-2023-05-18-artur-miroszewski.pdf |Download}}
-  * **[RESEARCH TRACK] 2023.05.11**: Joao Gama [[#20230511|Current Trends in Learning from Data Streams]]
+  * **[RESEARCH TRACK] 2023.05.11**: Joao Gama [[#20230511|Current Trends in Learning from Data Streams]] (//Project CHIST-ERA XPM//)
     * **LOCATION**: **WFAIS,  room: C-2-10** (This meeting will be in hybrid mode: on-site and online)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1683554659065?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 139: / Line 154: @@
     * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/Ec-5lXechUhJkGNxaXYRxO4Bg0u2kFPp_TOt9LGnsHXitw?e=TOCQMq|View]] (if you are not UJ employee, ask Szymon Bobek for access)
     * Presentation slides: {{ :aira:slides-20230420-mateusz-bulat.pdf |Download}}
-  * **[DOCTORAL TRACK] 2023.04.13**: Edyta Kuk [[#20230413|Machine Learning-based industrial process control with explainable decision support]]
+  * **[DOCTORAL TRACK] 2023.04.13**: Edyta Kuk [[#20230413|Machine Learning-based industrial process control with explainable decision support]] (//Project CHIST-ERA XPM//)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1680887771431?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/ER93jxzOOpZOkHRTyynVik4BILCKnaPcH15zjobekv-PMg?e=Z9zrDc|View]] (if you are not UJ employee, ask Szymon Bobek for access)
@@ Line 202: / Line 217: @@
     * Recording [[|View]] (if you are not UJ empoyee, ask [[szymon.bobek@uj.edu.pl|Szymon Bobek]] for access)
     * Presentation slides: TBA
-  * **[DOCTORAL TRACK] 2022.10.20**: Maciej Mozolewski [[#20221020|Human-in-the-loop approaches to XAI]] (//GEIST//)
+  * **[DOCTORAL TRACK] 2022.10.20**: Maciej Mozolewski [[#20221020|Human-in-the-loop approaches to XAI]] (//Project CHIST-ERA XPM//) (//GEIST//)
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1666007765036?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/Ed_mPywg63tJhddeUwrvejwBs_ywi0D3ZwsDGlZPJj128w?e=PFQGPm|View]] (if you are not UJ employee, ask [[szymon.bobek@uj.edu.pl|Szymon Bobek]] for access)
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 ===== Presentation details =====
+==== 2024-06-27 ====
+<WRAP column 15%>
+{{ :aira:jose-palma-foto.jpeg?width=200| }}
+{{ :aira:juan-botia-foto.jpeg?width=200| }}
+{{ :aira:antonio-guillen-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Title I**: Context-Aware learning models: CALM-Project by José Palma, Juan Botía, University of Murcia
+**Abstract**:
+In a pandemic scenario, there are a number of technical challenges that need to be addressed in order to be prepared for future pandemics. These challenges relate to the whole life cycle of development. First, since the beginning of a pandemic, it is crucial not only to have reliable sources of data, but also to make this data accessible. In this sense, we need to integrate data from different sources, of different types, most of which have not been collected for modelling purposes, and to monitor the growth of such data with new sources. Second, although the use of predictive models has proved useful, the variable nature of the distributions, not only in scenarios with different samples but throughout the evolution of a pandemic, makes it difficult for such models to be effective. The COVID19 pandemic demonstrated how the predictive models deteriorated as different waves emerged. In addition, the reason for this was different each time: changes in government policies, the appearance of mutations leading to changes in symptoms, their severity and the effect of previous pathologies, and finally the effect of different vaccination campaigns and the type of vaccine, all lead to changes in the concept being modelled, known as concept drift.
+These are the main challenges that CAML project tries to approach. The initial hypothesis of this project is that the development of advanced techniques to detect and characterise concept drift in pandemic scenarios and on different types of data, combining different representation schemes, will not only provide tools to anticipate and react to model degradation, but will also allow the development of techniques to generate medical and preventive knowledge that will help improve the quality of care and, ultimately, better manage situations of extreme stress in the health system.
+**Title II**:  Concept Drift in Imbalanced Problems by Antonio Guillén-Teruel, University of Murcia
+**Abstract**:
+We address the challenge of concept drift in imbalanced datasets, which is common in various real-world applications. We build upon the IPIP (Identical Partitions for Imbalance Problems) method, which effectively generates balanced subsets by subsampling the majority class to ensure representation of all minority class instances. This method enhances the performance of ensemble learning models in imbalanced scenarios.
+Additionally, we introduce the UIC (Unbiased Integration Coe) metric, designed to reduce bias caused by the imbalance ratio in datasets. The UIC metric integrates multiple biased measures, inversely weighted by their correlation with the minority class proportion, resulting in a more unbiased evaluation metric.
+Our work extends these methods to address concept drift, particularly in the context of passive learning. Concept drift occurs when the underlying data distribution changes over time, a phenomenon observed in dynamic datasets like those tracking COVID-19 patient outcomes. We propose adapting the IPIP method to handle concept drift by updating the model with new data chunks over time, ensuring that minority class instances are adequately represented in each update.
+Furthermore, we explore the application of the UIC metric to problems involving concept drift in imbalanced data. By adapting UIC for these scenarios, we aim to provide a more reliable measure of model performance over time, despite the evolving data distributions.
+Through extensive experiments on both simulated and real-world datasets, including a COVID-19 patient cohort, we demonstrate the effectiveness of our approach. Our findings suggest that the combination of IPIP and UIC, adapted for concept drift, offers a robust framework for tackling imbalanced data in non-stationary environments. Future work will focus on developing R packages to implement these methods, facilitating their application in various practical settings.
+**Biograms**:
+**José Palma** received a B.S. degree from the University of Las Palmas de Gran Canaria, Spain, in 1990 and a Ph.D. degree from the University of Murcia, Spain, in 1999, both in Computer Science. He has been an Associate Professor of Computer Science in the Department of Information Engineering and Communications and the School of Computer Science at the University of Murcia since 2000, but has been teaching in this department as an Associate Professor since 1996. Prior to joining the University of Murcia, he worked for 6 years in the Department of Computer Science and Systems at the University of Las Palmas de Gran Canaria. He has authored/co-authored more than 90 journal articles, book chapters and congress papers. His research activity focuses on Artificial intelligence in the areas of intelligent data analysis and machine learning,  and specially in medical domains.
+Currently, José Palma is the head of the AIKE research group (Artificial Intelligence and Knowledge Engineering) . He is also a senior member of the IEEE Engineering in Medicine and Biology Society.
+**Juan A. Botía**, is Professor in Computer Science and Artificial Intelligence at University of Murcia (UMU), Spain since September 2018. He also holds an Honorary Senior Research Fellow position at the Institute of Neurology (IoN), University College London (UCL), UK since July, 2017. He has a PhD in Computational Science and Artificial Intelligence (AI) from UMU (March, 2002). His expertise combines a deep knowledge about Artificial Intelligence, the experience of 10 years in the development of bioinformatic pipelines and the application of machine learning on the molecular biology domain. During the last 10 years, he has actively participated in 43 scientific papers indexed at Journal Citation Reports. Within that period he has supervised and completed 7 PhD theses. He is currently supervising 4 PhD students (4 at University of Murcia, 1 at University College London). He has experience of more than 15 years teaching machine learning related subjects and six years of teaching data analysis for bioinformatics.
+**Antonio Guillén-Teruel** graduated in Mathematics from the University of Murcia in 2020. In 2021 he got a Masters degree in Big Data from the same university and started his Ph.D studies in Informatics. The following year he got a Masters degree in Advanced Mathematics at the University of Murcia. His research focuses on imbalanced problems in Machine Learning (ML), including both in regression and classification problems, as well as the study of concept drift in medical domains for imbalanced datasets.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-06-06 ====
+<WRAP column 15%>
+{{ :aira:zaneta-kubic-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Żaneta Kubic, Jagiellonian University
+**Title**: European Cultural Heritage in Virtual Worlds – why and how : introduction to the IMPULSE project
+**Abstract**:
+The IMPULSE project was initiated in response to the European Commission's inquiry into the future of digitized cultural heritage in Europe and digitalization processes. A consortium of 11 partners, representing universities and research institutions, non-profit organizations, and entrepreneurs, is conducting research and development to create frameworks contributing to the development of the European Collaboration Cloud for Cultural Heritage. By creating prototypes for using cultural objects in virtual worlds for university education and artistic research, scientists will have a unique opportunity to establish frameworks for utilizing digitized heritage, particularly addressing audience needs, standards, interoperability, and legal frameworks. The project received funding under the Horizon Europe program (Cluster 2) totalling almost 3.4 million euros, starts on February 2024 and will continue until 2027. During the seminar session the main goals, roadmap and broader context of the project will be presented.
+**Biogram**:
+PhD. Data specialist. Project coordinator of Horizon Europe project IMPULSE: IMmersive digitisation: uPcycling cULtural heritage towards new reviving StratEgies (101132704). Senior documentalist in Jagiellonian University Museum. Member of the National Smart Specialisations 12 for Creative Industries Working Group (Polish Ministry of Economic Development and Technology). Una Europa Ambassador and member of the Self Steering Committee on Data Science and Artificial Intelligence.
+Żaneta specializes in introducing innovations in public institutions, with a particular focus on data management and the implementation of science and art collection management systems. Żaneta integrates cultural heritage with the high technologies and has extensive experience implementing IT solutions that support efficient collections management and facilitate access to cultural information and content. Her efforts aim to promote innovative practices in the cultural sector and utilize modern tools for the protection and promotion of cultural heritage.
+</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 ====