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aira:start [2024/01/16 08:09] – [Schedule Winter 2023] sbk | aira:start [2024/04/25 12:10] – [Schedule Summer 2024] sbk | ||
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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. | 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:// | The program will be published at [[https:// | ||
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Scientific coordination: | Scientific coordination: | ||
+ | ===== Schedule Summer 2024 ===== | ||
+ | * **[DOCTORAL TRACK] 2024.04.25**: | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access) | ||
+ | * Presentation slides: {{ |Download}} | ||
+ | * **[DOCTORAL TRACK] 2024.04.18** | ||
+ | * Farnoud Ghasemi [[# | ||
+ | * Michał Bujak [[# | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[https:// | ||
+ | * Presentation slides: {{: | ||
+ | * **[RESEARCH TRACK] 2024.04.04**: | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[https:// | ||
+ | * Presentation slides: {{|Download}} | ||
+ | * **[RESEARCH TRACK] 2024.03.28**: | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access) | ||
+ | * Presentation slides: {{|Download}} | ||
+ | * **[DOCTORAL TRACK] 2024.03.21**: | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[https:// | ||
+ | * Presentation slides: {{ : | ||
+ | * **[RESEARCH TRACK] 2024.03.14**: | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[https:// | ||
+ | * Presentation slides: {{ : | ||
===== Schedule Winter 2023 ===== | ===== Schedule Winter 2023 ===== | ||
+ | * **[RESEARCH TRACK] 2024.02.01**: | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[https:// | ||
+ | * Presentation slides: {{ |Download}} | ||
+ | * **[RESEARCH TRACK] 2024.01.25**: | ||
+ | * **LOCATION: C-2-10 (This meeting will be in hybrid mode: on-site and online)** | ||
+ | * Meeting link: [[https:// | ||
+ | * Recording: [[https:// | ||
+ | * Presentation slides: {{ : | ||
* **[RESEARCH TRACK] 2024.01.18**: | * **[RESEARCH TRACK] 2024.01.18**: | ||
* Meeting link: [[https:// | * Meeting link: [[https:// | ||
- | * Recording: [[|View]] (if you are not UJ employee, ask Szymon Bobek for access) | + | * Recording: [[https:// |
- | * Presentation slides: {{ |Download}} | + | * Presentation slides: {{ : |
- | * **[RESEARCH TRACK] 2024.01.11**: | + | * **[RESEARCH TRACK] 2024.01.11**: |
* Meeting link: [[https:// | * Meeting link: [[https:// | ||
* Recording: [[https:// | * Recording: [[https:// | ||
- | * Presentation slides: {{ |Download}} | + | * Presentation slides: {{: |
* **[RESEARCH TRACK] 2023.12.21**: | * **[RESEARCH TRACK] 2023.12.21**: | ||
* Meeting link: [[https:// | * Meeting link: [[https:// | ||
* Recording: [[https:// | * Recording: [[https:// | ||
- | * Presentation slides: {{ |Download}} | + | * Presentation slides: {{ : |
* **[DOCTORAL TRACK] 2023.12.14**: | * **[DOCTORAL TRACK] 2023.12.14**: | ||
* Meeting link: [[https:// | * Meeting link: [[https:// | ||
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===== Presentation details ===== | ===== Presentation details ===== | ||
+ | |||
+ | ==== 2024-04-25 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **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, | ||
+ | |||
+ | **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, | ||
+ | </ | ||
+ | <WRAP clear></ | ||
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+ | |||
+ | |||
+ | ==== 2024-04-18 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **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 clear></ | ||
+ | |||
+ | |||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **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, | ||
+ | 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 clear></ | ||
+ | |||
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+ | |||
+ | |||
+ | ==== 2024-04-04 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **Title**: Evolutionary methods in automatic | ||
+ | |||
+ | **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 | ||
+ | </ | ||
+ | <WRAP clear></ | ||
+ | |||
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+ | |||
+ | |||
+ | ==== 2024-03-28 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **Title**: Complex Collective Systems | ||
+ | |||
+ | **Abstract**: | ||
+ | The topic of the presentation will be collective aspects of | ||
+ | complex systems. | ||
+ | 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, | ||
+ | |||
+ | |||
+ | |||
+ | **Biogram**: | ||
+ | Professor of technical sciences in the discipline of computer | ||
+ | science - specialization: | ||
+ | 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, | ||
+ | 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 clear></ | ||
+ | |||
+ | |||
+ | |||
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+ | |||
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+ | |||
+ | |||
+ | |||
+ | ==== 2024-03-21 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **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 | ||
+ | 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, | ||
+ | |||
+ | |||
+ | </ | ||
+ | <WRAP clear></ | ||
+ | |||
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+ | |||
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+ | |||
+ | |||
+ | |||
+ | ==== 2024-03-14 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **Title**: Formal Representation and Synthesis of Local Search Neighborhoods | ||
+ | |||
+ | **Abstract**: | ||
+ | Local Search algorithms are a popular approach to solving difficult optimization problems. Their performance, | ||
+ | |||
+ | |||
+ | |||
+ | **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, | ||
+ | |||
+ | |||
+ | </ | ||
+ | <WRAP clear></ | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | ==== 2024-02-01 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **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, | ||
+ | |||
+ | Having supervised/ | ||
+ | |||
+ | In 2010, he was honored with the "2010 Samuel Berkowitz Long-Term Outcomes Study Award" for the article " | ||
+ | |||
+ | For over 20 years, he has maintained a specialized orthodontic practice in Poland. | ||
+ | |||
+ | |||
+ | </ | ||
+ | <WRAP clear></ | ||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | |||
+ | ==== 2024-01-25 ==== | ||
+ | <WRAP column 15%> | ||
+ | {{ : | ||
+ | </ | ||
+ | |||
+ | <WRAP column 75%> | ||
+ | |||
+ | **Speaker**: | ||
+ | |||
+ | **Title**: Online Anomaly Explanations - case study of Metro do Porto | ||
+ | |||
+ | **Abstract**: | ||
+ | Data-driven Predictive Maintenance is becoming increasingly important in many industries. This approach involves using machine learning algorithms on historical and real-time data from various system parts to detect anomalies and possible defects in equipment before they lead to failure. Black-box models based on deep-learning techniques are popular due to their high predictive accuracy. However, as these systems become more complex with many interacting components, it's crucial to ensure the trustworthiness of these models through explainability. | ||
+ | |||
+ | In this talk, we will present a two-layer data-driven predictive maintenance framework. The first layer uses autoencoders for fault detection, while the second employs an online rule-learning algorithm to explain anomalies. We will showcase this framework in a case study on a train from Metro do Porto, Portugal, demonstrating how it fulfils the requirements for early detection of failures and provides explanations for those anomalies. | ||
+ | |||
+ | |||
+ | |||
+ | **Biogram**: | ||
+ | Rita P. Ribeiro is an Assistant Professor at the Department of Computer Science at the Faculty of Sciences of the University of Porto (FCUP) and a Senior Researcher at the Laboratory of Artificial Intelligence and Decision Support (LIAAD) at the Institute of Systems Engineering and Computing, Technology and Science (INESCTEC). Her main research interests focus on learning problems in imbalanced domains, anomaly detection, evaluation issues in learning tasks and application problems related to social good and environmental impact. She has been involved in several research projects concerning ecological problems, fraud detection and predictive maintenance applications. She is a member of the program committee of several international conferences, | ||
+ | |||
+ | |||
+ | |||
+ | </ | ||
+ | <WRAP clear></ | ||
+ | |||
+ | |||
+ | |||
+ | |||
==== 2024-01-18 ==== | ==== 2024-01-18 ==== |