Artificial Intelligence in Research and Applications Seminar (AIRA)
GEIST is happy to announce, that we are launching an open, online seminar on Artificial Intelligence in Research and Applications (AIRA). It will be a weakly event with various guests from many different AI-related research fields as well as industry and business areas.
Please save your Thursdays between 3:30-5:00 PM Warsaw Time
The program will be published at https://aira.geist.re
Scientific secretary Szymon Bobek
Scientific coordination: Grzegorz J. Nalepa
- 2022.01.13 dr inż. Szymon Bobek: Challenges in Explainable Artificial Intelligence for Industry 4.0
- 2021.12.16 Timoleon Kipouros, PhD: Visual Analytics for Aerospace and Healthcare Systems Design
- Presentation slides: TBA
- 2021.12.09 Bartosz Soból: Recent developments of machine learning in experimental particle physics
- 2021.12.02 prof. dr hab. inż. Grzegorz J. Nalepa: Artificial Intelligence in Industry 4.0: Data, Models, and Knowledge
- 2021.11.25 Marcin Tutajewski: Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networks
- 2021.11.18 dr inż. Krzysztof Kutt: AI with psychology -- a few words on affective adaptation and personalisation of intelligent systems
- 2021.11.04 prof. Andrzej Grabowski: Telepresence and simulators - modern use of Virtual Reality in robotics and training with possible application of AI
- 2021.10.28 prof. Marcin Woźniak: Smart environment – AI meets IoT
- 2021.10.21 Bartłomiej Małkus: Financial modeling with applications of machine learning and explainable AI.
- 2021.10.14 Challenges and Opportunities for human-centred AI: A dialog between Yoshua Bengio (Turing Award) and Ben Shneiderman, moderated by Virginia Dignum.
- Meeting link: Zoom link
Speaker: Szymon Bobek, Assistant Professor @ the Jagiellonian University
Title: Challenges in Explainable Artificial Intelligence for Industry 4.0
Abstract: Advances in artificial intelligence trigger transformations that make more and more companies entering Industry 4.0 era. These transformations require building infrastructures for gathering and analysing large volumes of data. In many cases, the analysis involves conformance checking between the discovered patterns and the expert knowledge that is in possession of a company. In this talk, I will show how explainable artificial intelligence methods (XAI) can be used to help experts analyse the results of clustering methods. I will also discuss challenges in using existing XAI algorithms for generating explanations for users without data-science experience.
Biogram: Szymon Bobek holds a position of an assistant professor at the Jagiellonian University in Krakow, Poland, Faculty of Physics, Astronomy and Applied Computer Science He received his degree PHD at AGH UST in 2016 in the field of Computer Science (Artificial Intelligence) and science then he works as a member of GEIST research team. His work includes most recently hybrid models for explainable AI. He is author of over 70 research papers in international journals, books and conferences and reviewed research papers for a number of international journals. He participated in national and international research projects as an investigator and cooperated with several companies in applied projects, especially with massive big data processing using machine learning methods. He is in active cooperation with Cambridge University in the area of XAI application in healthcare applications and with AFFCAI (affcai.geist.re) group in the area of application of XAI to processing biomedical signals for Affective computing. Recently, he has been leading a team that developed a series of original tools in the area of eXplainable AI for the applications in industrial AI. He is a co-organizer of Practical Applications of Explainable Artificial Methods special session @ DSAA2021 conference (https://praxai.geist.re).
Speaker: Timos Kipouros, Senior Research Associate @ the University of Cambridge
Title: Visual Analytics for Aerospace and Healthcare Systems Design
Abstract: In this seminar, a systems engineering approach for the trade-off analysis of multiple criteria will be demonstrated with examples in aerospace and healthcare systems. Using a novel multidimensional data visualisation technique, the simultaneous analysis of the trade-offs between multiple stakeholder priorities and decision criteria is possible. Artificial intelligence, computational models, optimisation simulations and data analysis are utilised extensively, and multidimensional visualisation, as will be shown, can play a key role exploiting our fantastic pattern recognition ability in discovering relational information in such datasets and sequentially guiding complex design decisions in socio-technical systems.
Biogram: Timos Kipouros is a Senior Research Associate in the Engineering Design Centre at the University of Cambridge, and a Senior Lecturer at the University of Cranfield on Computational Engineering Design Optimisation. He received a 5-year Diploma in Mechanical and Aeronautical Engineering from the University of Patras, Greece, in 2002, and his PhD in Multi-Objective Aerodynamic Design Optimisation from the University of Cambridge, Jesus College, in 2006. He was also awarded a graduate certificate on Architecture and Systems Engineering from MIT in 2017. Since 2006, he worked as a Research Associate and then as Senior Research Associate in Cambridge where he pioneered the development of a method for post-analysis of optimisation data and engineering design processes using a highly advanced interactive Parallel Coordinates approach. The method has been extended to support interactive computational design, and robust decision-making in the areas of aerospace and healthcare. He has more than 100 publications in international peer reviewed journals, conferences and industrial workshops and has supervised 21 PhD and 60 MSc projects.
Speaker: Bartosz Soból, PhD Candidate @ Jagiellonian University
Title: Recent developments of machine learning in experimental particle physics
Abstract: With modern physics experiments comes the need to process the growing amount of data. Collaborations behind the largest particle physics experiments at LHC estimate their detectors’ data throughput to notably exceed 1 TB/s during upcoming data-taking sessions. This implies that terabytes of multidimensional data will have to be continuously collected, filtered, and processed into the fully reconstructed tracks of each particle passing through the detector. Additionally, in order to be meaningful, every new measurement has to be compared with its Monte Carlo simulated equivalent. In this talk, I will show how machine learning, hardware-accelerated graph, and generative neural networks can help in these two critical areas of computation in experimental particle physics research: track reconstruction and event simulation.
Biogram: Bartosz Soból is a first-year PhD student in Technical Computer Science at Jagiellonian University. He holds a BSc in Computer Mathematics and MSc in Computer Science from Jagiellonian University. Currently, he is a member of PANDA (FAIR, GSI) collaboration where he conducts research on particle tracking algorithms and heterogeneous online processing of experimental data. His professional interests include high performance computing, software optimization for heterogeneous systems and CPU-GPU-FPGA interoperability.
Speaker: Prof. dr hab. inż. Grzegorz J. Nalepa, Professor @ Jagiellonian University
Title: Artificial Intelligence in Industry 4.0: Data, Models, and Knowledge
Abstract: Industry 4.0 is a paradigm shift, or the next industrial revolution, according to some analysts. It is also closely related with the Internet of Things (IoT), Cyber Physical System (CPS), and with information and communications technology (ICT) in general. One of the aspects of I4.0 is the use of extensive monitoring equipment including sensor networks for monitoring of industrial assets. The premise of I4.0 is that the gathered sensor data may serve as means to support decision making in the industrial setting. However, a proper analysis and use of this data poses a number of challenges. First of all, the characteristics of this data make it a typical Big Data with big volume, velocity, variety. Furthermore, typical analytical methods are not applicable. This is why specific data mining methods must be used, with specific machine learning algorithms properly selected and configured. However, in most of the real-life cases the use of machine learning is not enough. Extensive domain knowledge has to be taken into account to prepare as well as interpret the mining process of the industrial data. This requires a development of complex hybrid AI-based approaches combining ML methods with model and knowledge-based ones. Finally, I4.0 imposes number of specific requirements and tasks to be solved by these approaches. For last three years, the GEIST research group has been involved in two international research project funded by the CHIST-ERA scheme. In the most recent one, the XPM project, we are developing novel methods for predictive maintenance tasks in I4.0 using methods of eXplainable AI (XAI). In the first one, the PACMEL project, we have been developing novel methods for high level knowledge-driven analysis of sensor data, as well as conformance checking of business and industrial processes. This short presentation will introduce the challenges as well as results of both projects.
Biogram: Grzegorz J. Nalepa (GJN.re) is a full professor at the Jagiellonian University, formerly at the AGH University of Science and Technology, in Krakow, Poland. He is an engineer with degrees in computer science - artificial intelligence, and philosophy. He also works as an independent expert and consultant in the area of AI (KnowAI.eu). He co-authored over two hundred research papers in international conferences and journals. He has been involved in tens of projects, including R+D projects with number of companies. He authored a book “Modeling with Rules using Semantic Knowledge Engineering” (Springer 2018). In 2012 he received the scientific award of POLITYKA weekly for the most promising scientific achievements in technical sciences in Poland. In 2018 he received a prize for the outstanding monograph in computer science from the Committee of Computer Science of the Polish Academy of Sciences. In 2020 he founded to Jagiellonian Human-Centered AI Laboratory (JAHCAI). His recent interests include applications of AI in Industry 4.0 and business, explainable AI, affective computing, context awareness, as well as intersection of AI with law.
Speaker: dr inż. Krzysztof Kutt, Assistant Professor @ Jagiellonian University
Title: AI with psychology – a few words on affective adaptation and personalisation of intelligent systems
Abstract: Does your smartphone understand how you feel? When you are irritated does it just go silent and stop annoying you? Such technology is not there yet, but the expansion of intelligent systems into new fields of activity means that they are spending more and more time with us. And that raises the need to implement not only algorithms tailored to solve tasks, but also to understand what the user is feeling. This talk will highlight the key findings of research conducted in this area by the AfCAI team (https://afcai.re/): How to understand emotions? How to collect data? How to train models? Why is this technology not yet working and what can be done to improve its effectiveness?
Biogram: Krzysztof Kutt, PhD, is an assistant professor at the Jagiellonian University. He received BSc and MSc degrees in Computer Science at AGH-UST. In 2018 he defended his PhD thesis on methods and tools for collaborative knowledge engineering at AGH-UST. He also received MA degree in Psychology from Jagiellonian University. Currently, as a computer scientist and a psychologist, he is trying to combine these two disciplines together to create something new and better. His research activities focus on the knowledge engineering (knowledge graphs, data semantization), affective computing (collecting and processing sensory and contextual data related to emotions) and ways of user interaction with information systems (including BCI and Neurofeedback systems).
Speaker: Dr hab. inż. Andrzej Grabowski, profesor CIOP-PIB
Title: Telepresence and simulators – modern use of Virtual Reality in robotics and training with possible application of AI
Abstract: Research on virtual reality has been conducted for over half a century. At that time, the development of technology meant that the possibilities of immersion in a virtual environment were much greater, and the potential spectrum of practical application was becoming broader and broader. Additionally, the decreasing cost makes using these technologies more and more justified also from an economic point of view. Three different VR applications will be presented: remote control of mobile robots using the telepresence concept, training a group of firefighters, and building simulators of self-propelled mining machines. The possibilities of improving training simulations by using AI to modify the course of the training scenario in real-time based on the actions taken by the trainee will also be indicated.
Biogram: Prof. Andrzej Grabowski, Ph. D, D. Sc., professor at CIOP-PIB, Head of Virtual Reality Laboratory in Department of Safety Engineering of Central Institute for Labour Protection - National Research Institute. A graduate of the Faculty of Physics at the Warsaw University of Technology. In his work, he conducts research on the use of virtual reality in various fields, including training, cognitive functions and abilities, telepresence, and support for upper limb rehabilitation. He works on the development of VR techniques. For example, in the laboratory he manages, simulators of various types of vehicles and machines, remotely controlled mobile robots, wireless VR gloves with force feedback, and AI-enhanced vision-based safety systems are developed
Speaker: dr hab. inż. Marcin Woźniak, Professor @ Silesian University of Technology
Title: Smart environment – AI meets IoT
Abstract: Smart environments are constantly introduced in various fields of our life to support us in daily duties, work and entertainment. Sensors are used to help detect dangerous situations in human behavior like sudden changes of body pose. We also implement such solutions in control of electric devices. New models and methods are developed to support data processing and decision making processes in these environments. The development of modern computing enabled using Artificial Intelligence with connection to Internet of Things (IoT). The talk is to show and discuss latest advances and ideas in model of data processing and automatic control of processes and devices used in smart environments.
Biogram: Marcin Woźniak received the M.Sc. degree in applied mathematics, the Ph.D. degree and the D.Sc. degree in computational intelligence. M. Woźniak is currently an Associate Professor with the Faculty of Applied Mathematics, Silesian University of Technology. He is a Scientific Supervisor in editions of "The Diamond Grant" and "The Best of the Best" programs for highly talented students from the Polish Ministry of Science and Higher Education. He participated in various scientific projects at Polish, Italian and Lithuanian universities and projects with applied results at IT industry. He was a Visiting Researcher with universities in Italy, Sweden, and Germany. He has authored/coauthored over 200 research papers in international conferences and journals. His current research interests include neural networks with their applications together with various aspects of applied computational intelligence accelerated by evolutionary computation and federated learning models. In 2017 he was awarded by the Polish Ministry of Science and Higher Education with a scholarship for an outstanding young scientist and in 2021 he received award from the Polish Ministry of Science and Higher Education for research achievements. In 2020 M. Woźniak was presented among “TOP 2% Scientists in the World” by Stanford University for his career achievements. Dr. Woźniak was the Editorial Board member or an Editor for Sensors, IEEE ACCESS, Measurement, Frontiers in Human Neuroscience, PeerJ CS, International Journal of Distributed Sensor Networks, Computational Intelligence and Neuroscience, Journal of Universal Computer Science, etc., and a Session Chair at various international conferences and symposiums, including IEEE Symposium Series on Computational Intelligence, IEEE Congress on Evolutionary Computation, etc.
Speaker: Bartłomiej Małkus – PhD candidate @ Jagiellonian University
Title: Financial modeling with applications of machine learning and explainable AI.
Abstract: Financial modeling is a general term that can cover many different processes carried out throughout the financial system. One of the broad definitions of financial model is that it is anything that is meant to calculate, forecast, or estimate financial numbers. Its applications can be found in all kinds of financial institutions, like banks, investment funds, insurance companies, but also non-financial ones, like regular companies, which forecast incomes, outcomes, cash flows etc. The presentation will focus on and explain some of its more specific fields, like financial instruments pricing (derivatives in particular) or risk modeling, tell about the purpose of these and present challenges arising in them. Next, it will show applications of machine learning and explainable AI in the mentioned processes and fields, obstacles which come with applying them and current solutions. The presentation will also cover available financial data sources that may be used for research purposes.
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 and is currently pursuing MSc in Financial Markets on Cracow University of Economics. His field of interest is application of AI techniques to financial modelling. Commercially, he works in IBM on on-premises data warehouse analytics solutions.