Sanghamitra Bandyopadhyay "Multiobjective Optimization and Multimodality: Algorithms and Applications"
Prof. Sanghamitra Bandyopadhyay did her B Tech, M Tech and Ph.D. in Computer Science from Calcutta University, IIT Kharagpur and Indian Statistical Institute respectively. She then joined the Indian Statistical Institute as a faculty member, and became the Director in 2015. Since 2020 she is continuing in her second tenure as the Director of the Institute. Her research interests include computational biology, soft and evolutionary computation, artificial intelligence and machine learning. She has authored/co-authored several books and numerous articles in journals, book chapters, and conference proceedings and has a citation h-index of 57. Prof. Bandyopadhyay has worked in many Institutes and Universities worldwide. She is the recipient of several awards including the Shanti Swarup Bhatnagar Prize in Engineering Science, TWAS Prize, Infosys Prize, JC Bose Fellowship, Swarnajayanti fellowship, INAE Silver Jubilee award, INAE Woman Engineer of the Year award (academia), IIT Kharagpur Distinguished Alumni Award, Humboldt Fellowship from Germany, Senior Associateship of ICTP, Italy, young engineer/scientist awards from INSA, INAE and ISCA, and Dr. Shanker Dayal Sharma Gold Medal and Institute Silver from IIT, Kharagpur, India. She is a Fellow of the Indian National Science Academy (INSA), National Academy of Sciences, India (NASI), Indian National Academy of Engineers (INAE), Institute of Electrical and Electronic Engineers (IEEE), The World Academy of Sciences (TWAS), International Association for Pattern Recognition (IAPR) and West Bengal Academy of Science and Technology. She serves as a member of the Science, Technology and Innovation Advisory Council of the Prime Minister of India (PM-STIAC). In 2022, she has been selected for the conferment of the Padma Shri award,the fourth highest civilian award of the Government of India.
Multi-objective optimization problems (MOPs) are ones that require simultaneous optimization of multiple conflicting objectives to attain the state of Pareto-optimality, where improving solutions in terms of one objective leads to deterioration in terms of one or more of the other objectives. MOPs galore in diverse real-life situations, and several algorithms have been developed for solving them. Multi-modal MOPs (MMMOPs) are those problems where a many-to-one mapping exists from solution space to objective space. As a result, multiple subsets of the Pareto-optimal set could independently generate the same Pareto-Front. The discovery of such equivalent solutions across the different subsets is essential during decision-making to facilitate the analysis of their non-numeric, domain-specific attributes.
In this talk, we will first provide a brief introduction to MOPs and an algorithm for solving them, followed by an application to the real-life problem of drug design. This will be followed by a discussion on the basic concept of multi-modality in MOPs. We then identify a problem of the existing approaches for solving MMMOPs, which is referred to as the crowding illusion problem. A method for solving MMMOPs with a graph Laplacian-based Optimization using Reference vector assisted Decomposition (LORD) will thereafter be discussed. The talk will conclude with the mention of an application of MMMOPs to the problem of building energy optimization.
Włodzisław Duch "New developments in EEG analysis for diagnosis, biofeedback and brain-computer interfaces"
Link to CV: http://www.is.umk.pl/~duch/cv/cv.html
New approaches to extract useful information from EEG are the key to use this technique for diagnosis, biofeedback and brain-computer interfaces. Techniques based on event related potentials, motor imagery and steady state visually evoked potentials (SSVEP) are still dominating the field, but most interesting developments are in investigation of neurodynamics, observing information flow through the brain connectomes. Many new mathematical techniques have been proposed, but development of methods useful in clinical practice is still a great challenge. I will review most interesting new approaches in this areas and summarize our own attempts in analysis of frequency-based fingerprints, new spatial filters, recurrence-based nonlinear features.
 Rykaczewski, K, Nikadon, J, Duch, W, Piotrowski, T. (2021). SupFunSim: spatial filtering toolbox for EEG. Neuroinformatics 19, 107–125
 M.K. Komorowski, K. Rykaczewski, T. Piotrowski, K. Jurewicz, J. Wojciechowski, A. Keitel, J. Dreszer, W. Duch (2021) ToFFi - Toolbox for Frequency-based Fingerprinting of Brain Signals (submitted to Neurocomputing).
Tingwen Huang "Efficient Computational Approaches and Applications to Some Optimization Problems in Smart Grid"
Prof. Tingwen Huang's research focuses on dynamics of nonlinear systems including neural networks, complex networks and multi-agent and their applications to smart grids and cybersecurity. He is a Highly Cited Researcher by Clarivate Analytics, formerly Thomson Reuters.
He is very actively involving in professional service. He serves/served as the Past-President (2021), President (2020), President-Elect (2019) for Asia Pacific Neural Network Society, as an associate editor for a dozen international journals, as a guest editor for 12 special issues publishing in 9 leading journals.
He is a Member of the European Academy of Sciences and Arts, an Academician of the International Academy for Systems and Cybernetic Sciences, a Fellow of IEEE and AAIA (Asia-Pacific Artificial Intelligence Association).
In a smart grid context, a demand response strategy of electric vehicle charging is modelled by a stochastic game, where a big data analytic framework is proposed for controlling the electric vehicle charging behaviours. We will also look at Plug-In Electric Vehicles (PEVs) Charging: Feeder Overload Control problem. Moreover, a two-stage stochastic game theoretical model is proposed for energy trading problem in a multi-energy microgrid system. Concerning the privacy, a research branch of reinforcement learning (RL) that dominates distributed learning for years will be presented by making the first attempt to apply RL-based algorithms in the energy trading game among smart microgrids where no information concerning the distribution of payoffs is a priori available and the strategy chosen by each microgrid is private to opponents, even trading partners. To solve this challenge, a new energy trading framework based on the repeated game that enables each microgrid to individually and randomly choose a strategy with probability to trade the energy in an independent market so as to maximize his/her average revenue.
Janusz Kacprzyk "Title TBA"
Full member, Polish Academy of Sciences
Member, Academia Europaea
Member, European Academy of Sciences and Arts
Member, European Academy of Sciences
Member, International Academy for Systems and Cybernetic Sciences (IASCYS)
Foreign member, Bulgarian Academy of Sciences
Foreign member, Finnish Society of Sciences and Letters
Foreign member, Royal Flemish Academy of Belgium for Sciences and the Arts (KVAB)
Foreign member, Spanish Royal Academy of Economic and Financial Sciences (RACEF)
Systems Research Institute, Polish Academy of Sciences
Ul. Newelska 6, 01-447 Warsaw, Poland
Janusz Kacprzyk is Professor of Computer Science at the Systems Research Institute, Polish Academy of Sciences, WIT – Warsaw School of Information Technology, and Chongqing Three Gorges University, Wanzhou, Chinqgqung, China, and Professor of Automatic Control at PIAP – Industrial Institute of Automation and Measurements. He is Honorary Foreign Professor at the Department of Mathematics, Yli Normal University, Xinjiang, China. He is Full Member of the Polish Academy of Sciences, Member of Academia Europaea, European Academy of Sciences and Arts, European Academy of Sciences, Foreign Member of the: Bulgarian Academy of Sciences, Spanish Royal Academy of Economic and Financial Sciences (RACEF), Finnish Society of Sciences and Letters, and Flemish Royal Academy of Belgium of Sciences and the Arts (KVAB). He was awarded with 5 honorary doctorates. He is Fellow of IEEE, IET, IFSA, EurAI, IFIP and SMIA.
His main research interests include the use of modern computation computational and artificial intelligence tools, notably fuzzy logic, in systems science, decision making, optimization, control, data analysis and data mining, with applications in mobile robotics, systems modeling, ICT etc.
He authored 7 books, (co)edited more than 150 volumes, (co)authored more than 650 papers, including ca. 100 in journals indexed by the WoS. His bibliographic data are: Google Scholar: citations: 30596; h-index: 77, Scopus: citations: citations: 9111; h-index: 41, Web of Science: citations: 7228; h-index: 37. He is listed in 2020 ”World’s 2% Top Scientists” by Stanford University, Elsevier (Scopus) and ScieTech Strategies and published in PLOS Biology Journal.
He is the editor in chief of 7 book series at Springer, and of 2 journals, and is on the editorial boards of ca. 40 journals.. He is President of the Polish Operational and Systems Research Society and Past President of International Fuzzy Systems Association.
Nikola Kasabov "Deep Learning, Deep Knowledge Representation and Knowledge Transfer with Brain-Inspired Neural Network Architectures"
Director, Knowledge Engineering and Discovery Research Institute,
Auckland University of Technology, Auckland, New Zealand, firstname.lastname@example.org,
Advisory/Visiting Professor Shanghai Jiao Tong University, Robert Gordon University UK
Professor Nikola Kasabov is Fellow of IEEE, Fellow of the Royal Society of New Zealand, Fellow of the INNS College of Fellows, DVF of the Royal Academy of Engineering UK and the Scottish Computer Association. He is the Founding Director of the Knowledge Engineering and Discovery Research Institute (KEDRI), Auckland and Professor at the School of Engineering, Computing and Mathematical Sciences at Auckland University of Technology. Kasabov is the 2019 President of the Asia Pacific Neural Network Society (APNNS) and Past President of the International Neural Network Society (INNS). He is member of several technical committees of IEEE Computational Intelligence Society and Distinguished Lecturer of IEEE (2012-2014). He is Editor of Springer Handbook of Bio-Neuroinformatics, Springer Series of Bio-and Neurosystems and Springer journal Evolving Systems. He is Associate Editor of several journals, including Neural Networks, IEEE TrNN, Tr CDS, Information Sciences, Applied Soft Computing. Kasabov holds MSc and PhD from TU Sofia, Bulgaria. His main research interests are in the areas of neural networks, intelligent information systems, soft computing, bioinformatics, neuroinformatics. He has published more than 620 publications. He has extensive academic experience at various academic and research organisations in Europe and Asia, including: TU Sofia Bulgaria; University of Essex UK; University of Otago, NZ; Advisory Professor at Shanghai Jiao Tong University, Visiting Professor at ETH/University of Zurich and Robert Gordon University UK. Prof. Kasabov has received a number of awards, among them: Doctor Honoris Causa from Obuda University, Budapest; INNS Ada Lovelace Meritorious Service Award; NN Best Paper Award for 2016; APNNA ‘Outstanding Achievements Award’; INNS Gabor Award for ‘Outstanding contributions to engineering applications of neural networks’; EU Marie Curie Fellowship; Bayer Science Innovation Award; APNNA Excellent Service Award; RSNZ Science and Technology Medal; 2015 AUT Medal; Honorable Member of the Bulgarian and the Greek Societies for Computer Science. More information of Prof. Kasabov can be found on the KEDRI web site: http://www.kedri.aut.ac.nz
The talk argues and demonstrates that the third generation of artificial neural networks, the spiking neural networks (SNN), can be used to design brain-inspired architectures that are not only capable of deep learning of temporal or spatio-temporal data, but also enabling the extraction of deep knowledge representation from the learned data. Similarly to how the brain learns time-space data, these SNN models do not need to be restricted in number of layers, neurons in each layer, etc. as it is the case with the traditional deep neural network architectures. When the SNN model is designed to follow a brain template, knowledge transfer between humans and machines in both directions becomes possible through the creation of brain-inspired BCI. The presented approach is illustrated on an exemplar SNN architecture NeuCube (free software and open source available from www.kedri.aut.ac.nz/neucube) and case studies of brain and environmental data modelling and knowledge representation using incremental and transfer learning algorithms These include predictive modelling of EEG and fMRI data measuring cognitive processes and response to treatment, AD prediction, BCI, human-human and human-VR communication, hyper-scanning and other. More details can be found in the recent book: Time-Space, Spiking Neural Networks and Brain-Inspired Artificial Intelligence, Springer,2019, https://www.springer.com/gp/book/9783662577134.
Zbigniew Michalewicz "Advanced AI-based business applications for transforming data into decisions"
Zbigniew Michalewicz received the Master of Science degree from the Technical University of Warsaw, Warsaw, Poland, in 1974; the Ph.D. degree from the Institute of Computer Science, Polish Academy of Sciences, Warsaw, in 1981, and the D.Sc. degree in Computer Science from the Polish Academy of Science in 1997. He is currently Emeritus Professor of Computer Science at the University of Adelaide, Australia. He is also a Professor with the Institute of Computer Science, Polish Academy of Sciences, the Polish-Japanese Institute of Information Technology, Warsaw, and the State Key Laboratory of Software Engineering, Wuhan University, Wuhan, China. He is also associated with the Structural Complexity Laboratory, Seoul National University, South Korea. Zbigniew Michalewicz is the Chief Scientific Officer at Complexica (www.complexica.com), a leading provider of software applications that harness the power of Artificial Intelligence and big data to improve the effectiveness of sales & marketing activities. For many years his research interests were in the field of evolutionary computation. He published several books, including a monograph Genetic Algorithms + Data Structures = Evolution Programs (3 editions, a few translations, over 20,000 citations, source: Google Scholar), and over 300 technical papers in journals and conference proceedings that are cited widely (50,000 citations, source: Google Scholar). Other books include Adaptive Business Intelligence and How to Solve It: Modern Heuristics (both published by Springer, Berlin, 2006 and 2004, respectively), Puzzle-based Learning (Hybrid Publishers, Melbourne, 2008), Winning Credibility: A Guide for Building a Business from Rags to Riches (Hybrid Publishers, Melbourne, 2007), where he described his business experiences over the last years.
Zbigniew Michalewicz was one of the editors-in-chief of the Handbook of Evolutionary Computation and the general chairman of the First IEEE International Conference on Evolutionary Computation held in Orlando, June 1994. In December 2013 Zbigniew was awarded (by the President of Poland, Mr. Bronislaw Komorowski) the Order of the Rebirth of Polish Polonia Restituta – the second highest Polish state decoration civilian for outstanding achievements in the field of education, science, sports, culture, arts, economy, national defence, social activities, the civil service and the development of good relations with other countries.
The talk would cover a few AI-based business applications for transforming data into decisions, based on work done for three companies (NuTech Solutions, SolveIT Software, and Complexica) over the last 20 years. A few general concepts (Adaptive Business Intelligence, Travelling Thief Problem, Larry – the Digital Analyst) would be discussed and illustrated by a few examples. The final part of the talk would present Complexica’s approach for increasing revenue, margin, and customer engagement through automated analysis.
Ujjwal Maulik "AI and Data Science: Path Traversed and Ahead"
Dr. Ujjwal Maulik is a Professor in the Dept. of Comp. Sc. and Engg., Jadavpur University since 2004. He was also the former Head of the same Department. He also held the position of the Principal in charge and the Head of the Dept. of Comp. Sc. and Engg., Kalayni Govt. Engg. College. Dr. Maulik has worked in many universities and research laboratories around the world as visiting Professor/ Scientist including Los Alamos National Lab., USA in 1997, Univ. of New South Wales, Australia in 1999, Univ. of Texas at Arlington, USA in 2001, Univ. of Maryland at Baltimore County, USA in 2004, Fraunhofer Institute for Autonome Intelligent Systems, St. Augustin, Germany in 2005, Tsinghua Univ., China in 2007, Sapienza Univ., Rome, Italy in 2008, Univ. of Heidelberg, Germany in 2009, German Cancer Research Center (DKFZ), Germany in 2010, 2011 and 2012, Grenoble INP, France in 2010, 2013 and 2016, University of Warsaw in 2013 and 2019, University of Padova, Italy in 2014 and 2016, Corvinus University, Budapest, Hungary in 2015 and 2016, University of Ljubljana, Slovenia in 2015 and 2017, International Center for Theoretical Physics (ICTP), Trieste, Italy in 2014, 2017 and 2018. He is the recipient of Alexander von Humboldt Fellowship during 2010, 2011 and 2012 and Senior Associate of ICTP, Italy during 2012-2018. He is the Fellow of Indian National Academy of Engineers (INAE), India, National Academy of Science India (NASI), International Association for Pattern Recognition (IAPR), USA and The Institute of Electrical and Electronics Engineers (IEEE), USA. He is also the Distinguish Member of the ACM. He is Distinguish Speaker of IEEE as well as ACM. His research interest include Machine Learning, Data Science, Bioinformatics, Multi-objective Optimization, Social Networking, IoT and Autonomous Car. In these areas he has published ten books, more than three hundred fifty papers, filed several patents and guided twenty two doctoral students. He is mentoring a couple of Start-Ups in the area - AI for Healthcare. His other interest include Sports and Classical Music.
In this lecture first we will describe fundamental and current trends in Artificial Intelligence (AI), and Data Science. In this regard we will demonstrate applications of different machine learning algorithms including Deep Learning and Graph Neural Network in real life application like Intelligence Car and healthcare. We will discuss the important of explainable and trusted AI. Finally we will also discuss issues and challenges related to Big Data.
Witold Pedrycz "From Data to Information Granules: Quantitative and Qualitative Facets of AI"
Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland
Granular Computing is about representing knowledge by means of information granules, constructing information granules, their processing, and realizing communication and interpretation carried out in the framework of information granules.
Information granules are abstract constructs that bring together individual entities because of their closeness, similarity, or resemblance. The level of abstraction makes a description of the problem manageable and problem solving strategies feasible and efficient.
We offer a rationale behind emergence of information granules, offer examples and present a variety of frameworks (sets, intervals, fuzzy sets, probabilities, rough sets, random sets, intuitionistic sets…) using which they are formally represented.
Main motivating factors are advocated. General ways of designing and evaluating information granules are discussed. A role of a variety of clustering techniques treated as a prerequisite for the formation of information granules is demonstrated. The evaluation of the quality of information granules is case ion the granulation-degranulation scheme.
We deliver a comprehensive approach to the development of information granules by means of the principle of justifiable granularity; here various construction scenarios are discussed. In the sequel, we look at the generative and discriminative aspects of information granules supporting their further usage in the formation of granular models. A symbolic manifestation of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data and relationships among data. The principle provides a way to build an information granule such that it is legitimate from the perspective of coverage (experimental legitimacy of the granule) and its semantics (meaning). Along with the generic construct, discussed are various augmentations of the principle. We carefully look at the generative and discriminative aspects of information granules supporting their further usage in the formation of granular artifacts. The considerations are carried out following a general knowledge representation scheme:
data -› numeric prototypes -› information granules -› symbols
Furthermore, a symbolic characterization of information granules is put forward and analyzed from the perspective of semantically sound descriptors of data. Their linguistic summarization is offered as well. The diversity of information granules is also captured by more advanced constructs of information granules of higher type and higher order.
Some selected topics of data analytics in which information granularity is visible such as (i) imputation, (ii) time series prediction, (ii) data stream analysis, (iii) imputation, (iv) association analysis and associative memories, and (v) transfer learning are formulated and discussed.
The tutorial is made self-contained; all required prerequisite material will be made an initial part of the presentation.