Differences

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

--- aira:start [2024/01/19 15:37] – [Schedule Winter 2023] sbk
+++ aira:start [2024/04/26 07:32] (current) – [Schedule Summer 2024] sbk
@@ Line 4: / Line 4: @@
 AIRA is a weekly event (with some breaks between semesters and holidays) devoted to recent results in AI research presented by invited guests from many AI-related fields as well as applications of AI methods and tools in areas of science, industry and business.
-**Please save your Thursdays between 3:30-5:00 PM Warsaw Time**
+**Please save your Thursdays between 3:15-4:45 PM Warsaw Time**
 The program will be published at [[https://aira.geist.re]] in advance
@@ Line 13: / Line 13: @@
 Scientific coordination: [[https://gjn.re|Grzegorz J. Nalepa]]
+===== Schedule Summer 2024 =====
+  * **[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: {{ :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]]
+    * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/EXsLL583hYNFqhXT7dulv3cBXdQCU_wWgMbyIH2Hnm93cA?e=pAL2NG&nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+    * Presentation slides: {{ :aira:slides-20240314-mateusz-slazynski.pdf |Download}}
 ===== Schedule Winter 2023 =====
+  * **[RESEARCH TRACK] 2024.02.01**: Piotr Fudalej [[#20240201| Artificial intelligence at the dentist - patient or assistant?]]
+    * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1706517969034?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/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]]
     * **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]]
-    * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access)
+    * Recording: [[https://ujchmura.sharepoint.com/:v:/t/Section_495645_1/ESnVSy6-EVZFiIlG-8j8kqgBQOBMuO5PhUKrGqj5SJA9iw?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=vleBh7|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-    * Presentation slides: {{ |Download}}
+    * Presentation slides: {{ :aira:slides-20240125-rita-ribeiro.pdf  |Download}}
   * **[RESEARCH TRACK] 2024.01.18**: Jakub Gomułka [[#20240118| The Wittgenstein Ontology Project]]
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1705392485911?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/EVB1chK_sUNOmSoJwJaFibwBekR5kpf79fnNeQYgmeskWA?nav=eyJyZWZlcnJhbEluZm8iOnsicmVmZXJyYWxBcHAiOiJTdHJlYW1XZWJBcHAiLCJyZWZlcnJhbFZpZXciOiJTaGFyZURpYWxvZy1MaW5rIiwicmVmZXJyYWxBcHBQbGF0Zm9ybSI6IldlYiIsInJlZmVycmFsTW9kZSI6InZpZXcifX0%3D&e=WwbynX|View]] (if you are not UJ employee, ask Szymon Bobek for access)
-    * Presentation slides: {{ |Download}}
+    * Presentation slides: {{ :aira:slides-20240118-jakub-gomulka.pdf |Download}}
   * **[RESEARCH TRACK] 2024.01.11**: Arkadiusz Tomczyk[[#20240111| Interpretable components and graph neural networks]]
     * Meeting link: [[https://teams.microsoft.com/l/meetup-join/19%3aJF1L0935A7eV6s8R9brG5MMYONrqy4XmxPSYLeRMCGM1%40thread.tacv2/1704183973615?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 277: / Line 308: @@
 ===== Presentation details =====
+==== 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 ====
+<WRAP column 15%>
+{{ :aira:maciej-szelazek-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Maciej Szelążek, PhD Candidate @ AGH University of Krakow
+**Title**: Using ML and XAI for decision support in Business Intelligence analysis.
+**Abstract**:
+The presentation will provide an overview of the author’s doctorate research on practical use of ML and explainability techniques as a support of the decision-making chain.
+Real life applications require compliance with established standards. These include both good practices developed within the company as well as quality certifications like ISO.
+The author will present different perspectives  on issues related to quality control analysis of production processes, or in wider terms - Business Intelligence.
+Subjects covered include techniques for developing key process indicators to address analytical challenges, in relation to XAI scores and external sources of knowledge.
+**Biogram**:
+Maciej Szelążek, MSc (maciej.szelazek@agh.edu.pl) is a PhD student at the AGH UST in Krakow, Poland, Department of Applied Computer Science. He received his MSc degree in Automation and Metrology from AGH UST in 2010. He worked as an data analyst in the Office of Statistical Process Control (SPC) Arcelor Mittal Poland. Participate in creation and development of an analytical system based on a central database integrating distributed data sources, reporting system and Statistica data mining software. He conducted big data multidimensional analyses related to searching for bottlenecks, logistics, cost optimization and limiting the variability of industry processes. He was involved in Process-aware Analytics Support based on Conceptual Models for Event Logs - PACMEL project.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-03-14 ====
+<WRAP column 15%>
+{{ :aira:mateusz-slazynski-foto.jpeg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Mateusz Ślażyński, Research Assistant @ AGH University of Krakow
+**Title**: Formal Representation and Synthesis of Local Search Neighborhoods
+**Abstract**:
+Local Search algorithms are a popular approach to solving difficult optimization problems. Their performance, however, depends strongly on the so-called fitness landscape, as defined by a cost function and the chosen neighborhood operators. The presentation will provide an overview of the author’s doctorate research on finding well-formed fitness landscapes automatically based on declarative models of discrete optimization problems. After introducing the necessary context, the author will propose a formal representation of the neighborhood operators and an evolutionary algorithm designed to synthesize operators satisfying given fitness criteria.
+**Biogram**:
+Mateusz Ślażyński has recently obtained his doctorate in Computer Science from the AGH University of Krakow; trying to bridge the gap between declarative models and meta-heuristic methods. He specializes in Operational Research, extending classical solutions with Reinforcement Learning and Automated Algorithm Design techniques. Having a background in philosophy, his interests also include probabilistic argumentation, philosophy of mind and artificial intelligence. Outside the academia, he is working as a consultant, implementing solvers for vehicle routing and warehouse management problems.
+</WRAP>
+<WRAP clear></WRAP>
+==== 2024-02-01 ====
+<WRAP column 15%>
+{{ :aira:piotr-fudalej-foto.jpg?width=200| }}
+</WRAP>
+<WRAP column 75%>
+**Speaker**: Piotr Fudalej, Professor @  Medical College of Jagiellonian University
+**Title**: Artificial intelligence at the dentist - patient or assistant?
+**Abstract**:
+This presentation will be inspirational. I will demonstrate what the process of planning and delivering orthodontic treatment looks like. Then I will describe where, from the point of view of a clinician treating orthodontic patients, there is a need to use machine learning algorithms to improve treatment planning, speed up therapy, or reduce the risk of adverse effects associated with dental (orthodontic) treatment.
+**Biogram**:
+Piotr Fudalej completed his studies of dentistry at Warsaw Medical University (Poland) and pursued biology at the University of Warsaw. Specializing in orthodontics, he earned degrees from the University of Washington in Seattle and obtained two doctorates: one in Poland in 1999 and another in the Netherlands in 2011 at Radboud University Nijmegen. Presently, he holds the position of professor and heads the Department of Orthodontics at Jagiellonian University in Kraków (Poland). Additionally, he serves as an adjunct professor at the University of Bern in Switzerland.
+Having supervised/co-supervised 15 doctoral students from the Netherlands, Switzerland, Poland, and the Czech Republic, Piotr Fudalej has contributed significantly to orthodontic research. His research output includes over 90 articles published in peer-reviewed international scientific journals. As the President-Elect of the European Orthodontic Society, he will organize the 100th EOS Congress in Krakow in 2025.
+In 2010, he was honored with the "2010 Samuel Berkowitz Long-Term Outcomes Study Award" for the article "Dental arch relationship in children with complete unilateral cleft lip and palate following Warsaw (one-stage repair) and Oslo protocols." In 2022, he received "The 2022 Dewell Award for Best Clinical Research" for the article "A comparative assessment of failures and periodontal health between 2 mandibular lingual retainers in orthodontic patients. A 2-year follow-up, single practice-based randomized trial."
+For over 20 years, he has maintained a specialized orthodontic practice in Poland.
+</WRAP>
+<WRAP clear></WRAP>
 ==== 2024-01-25 ====