Abreu Faro Auditorium, Instituto Superior Técnico, University of Lisbon, Portugal.
|9:00||Instituto Superior Técnico Welcome Prof. Arlindo Oliveira (President of Instituto Superior Técnico)|
|9:30||General Video Game AI with Statistical Forward Planning Prof. Simon Lucas||Statistical forward planning algorithms provide a simple and general way to provide competent AI controllers for a variety of games. Example algorithms include Monte Carlo Search, Monte Carlo Tree Search, and Rolling Horizon Evolution. They require that the game state can be copied and advanced (stepped forward) rapidly, a condition that is met in many cases. In this talk I will give a brief overview of the algorithms, providing key insights in to why they work, and then demonstrate the ability of rolling horizon evolution to play a variety of video games surprisingly well without the need for any prior training. This paves the way for automated play testing and analysis as a tool to assist games design, as well as providing decision support for complex real-world planning problems. I’ll demonstrate how agent-based testing can be used to tune game design parameters very quickly using the model-based and sample-efficient N-Tuple Bandit Evolutionary Algorithm.|
|10:10||Deep Learning for Medical Image Analysis Prof. Pau-Choo (Julia) Chung||Recent advancement of image understanding with deep learning neural networks has brought great attraction to those in image analysis into the focus of deep learning networks. While researchers on video/image analysis have jumped on the bandwagon of deep learning networks, medical image analyzers certainly is the coming followers. The characteristics of medical images are extremely different from those of photos and video images. The application of medical image analysis is also much more critical. For achieving the best effectiveness and feasibility of medical image analysis with deep learning approaches, several issues have been taken cared of. In this talk we will give a brief overview of the recent development of deep learning models and their applications in medical image analysis. Several issues in regard of the data preparation, techniques, and clinic applications will also be discussed.|
|11:20||Big Data Challenges and Deep Learning - Applications to Astronomy Prof. Pablo A. Estevez||Astronomy is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new sky surveys. To cope with a change of paradigm to data-driven science new computational intelligence, machine learning and statistical approaches are needed. In this talk I will introduce the general context and the big data challenges that astronomy is facing. I will present two applications using deep learning. The first application is the automated real-time detection of supernovae in astronomical images. We developed a convolutional neural net to discriminate between true transients and bogus events. We introduced rotational invariance and used a visualization approach to interpret the results of the convolutional neural network. The second application is to classify different classes of astronomical objects based on a recurrent convolutional neural network, which uses directly sequences of images in an online fashion. To train the classifier we simulated sequences of images using realistic observational conditions. To test the results we used real data from the HiTS survey. The fact that our classifier works well on real images having been trained on simulated ones, encourages us to use the proposed method to train classifiers for the very large telescopes under construction such as the Large Synoptic Survey Telescope even before getting the first light.|
|14:00||Similarities and fuzzy modelling in data mining Prof. Bernadette Bouchon-Meunier||Fuzzy logic provides interesting tools for data mining, mainly because of its ability to represent imperfect information, for instance by means of imprecise categories or aggregation methods. This ability is of crucial importance when databases are complex, large, and contain heterogeneous, imprecise, vague, uncertain, incomplete data. We focus on the use of similarities involved in many steps of the process of fuzzy data mining, such as clustering, construction of prototypes, fuzzy querying, for instance, and we highlight their diversity and richness.|
|14:40||Recognition Technology: A modern perspective of Lofti Zadeh's vision Prof. James M. Keller||In 1998, Lotfi Zadeh, the creator of fuzzy set theory and fuzzy logic, coined the term Recognition Technology, saying that it refers to current or future systems that have the potential to provide a "quantum jump in the capabilities of today’s recognition systems". Recognition Technology will include systems that incorporate three advances: new sensors, novel signal processing and soft computing for decision making. He anticipated this new blend of hardware and data science. That vision has come to pass. I will discuss these three aspects of recognition technology through two quite different case studies that I am involved in: explosive hazard detection and eldercare technology. They are both recognition systems. The former has a goal of detecting objects, explosive hazards, to help save lives while the latter focuses on recognizing human activities to allow older adults to live independently with a higher quality of life. While the sensors applied to these problems are dissimilar, they share many of the signal processing and pattern recognition approaches. This talk is my tribute to Professor Zadeh who passed away recently at the age of 96.|
|15:40||Evolutionary Multi-Objective Optimization: Past, Present, and Future Prof. Carlos A. Coello Coello||During the last thirty years, there has been an increasing interest in using heuristic search algorithms based on natural selection (the so-called "evolutionary algorithms") for solving a wide variety of problems. As in any other discipline, research on evolutionary algorithms has become more specialized over the years, giving rise to a number of sub-disciplines. This talk deals with one of the emerging sub-disciplines that has become very popular due to its wide applicability: evolutionary multi-objective optimization (EMOO). EMOO refers to the use of evolutionary algorithms (or evenother biologically-inspired heuristics) to solve problems with two or more (often conflicting) objectives. Unlike traditional (single-objective) problems, multi-objective optimization problems normally have more than one possible solution. Thus, traditional evolutionary algorithms (e.g., genetic algorithms) need to be modified in order to deal with such problems.|
|16:20||Dealing with the open world classification problem with neural networks Prof. Nikhil R. Pal||Multi-Layer Perceptron (MLP) classifiers are extensively used in pattern recognition but sometimes they make decisions, when they should not. For example, if a test sample is far from the training data used to design the MLP, the trained MLP may (usually will) make a decision with a high confidence. Similarly, an MLP may be trained to distinguish between four kinds of childhood cancer, but if a test sample represents a normal patient or a colon cancer patient, the MLP will classify it into one of the four classes for which it was trained. So, as such, MLP cannot deal with the “open world” nature of the problem. Such problems exist with many other learning systems. We want to address these problems by equipping the network not to make any judgment when it should not. We have developed an algorithm to get a practical solution to this problem. We shall estimate the domain of the training data ("sampling window"). We shall show an asymptotic optimal property of our estimate along with some other interesting results. Then to train the network we shall use some samples randomly drawn from outside the sampling window and label them as coming from a class called, “Don’t know”. We shall illustrate our method with benchmark data sets. As a by-product we shall also demonstrate how MLP can be equipped with an incremental learning ability.|
9:30 General Video Game AI with Statistical Forward Planning
Prof. Simon Lucas . @mail . presentation
Simon Lucas is a professor of Artificial Intelligence and Head of the School of Electronic Engineering and Computer Science at Queen Mary University of London where he also heads the Game AI Research Group. He holds a PhD degree (1991) in Electronics and Computer Science from the University of Southampton. He is the founding Editor-in-Chief of the IEEE Transactions on Computational Intelligence and AI in Games and co-founded the IEEE Conference on Computational Intelligence and Games. His research involves developing and applying computational intelligence techniques to build better game AI, use AI to design better games, provide deep insights into the nature of intelligence and work towards Artificial General Intelligence. He is a fellow of the Alan Turing Institute.
10:10 Deep Learning for Medical Image Analysis
Prof. Pau-Choo (Julia) Chung . @mail . presentation
Pau-Choo (Julia) Chung (S’89-M’91-SM’02-F’08) received the Ph.D. degree in electrical engineering from Texas Tech University, USA, in 1991. She then joined the Department of Electrical Engineering, National Cheng Kung University (NCKU), Taiwan, in 1991 and has become a full professor in 1996. She served as the Head of Department of Electrical Engineering (2011-2014), the Director of Institute of Computer and Communication Engineering (2008-2011), the Vice Dean of College of Electrical Engineering and Computer Science (2011), the Director of the Center for Research of E-life Digital Technology (2005-2008), and the Director of Electrical Laboratory (2005-2008), NCKU. She was elected Distinguished Professor of NCKU in 2005 and received the Distinguished Professor Award of Chinese Institute of Electrical Engineering in 2012. She also served as Program Director of Intelligent Computing Division, Ministry of Science and Technology (2012-2014), Taiwan. She was the Director General of the Department of Information and Technology Education, Ministry of Education (2016-2018). She is currently the Vice President for Members Activities, IEEE CIS. Dr. Chung’s research interests include computational intelligence, medical image analysis, video analysis, and pattern recognition. Dr. Chung participated in many international conferences and society activities. She served as the program committee member in many international conferences. She served as the Publicity Co-Chair of WCCI 2014, SSCI 2013, SSCI 2011, and WCCI 2010. She served as an Associate Editor of IEEE Transactions on Neural Network and Learning Systems(2013-2015) and currently is and Associate Editor of IEEE Transactions on Biomedical Circuits and Systems. Dr. Chung was the Chair of IEEE Computational Intelligence Society (CIS) (2004-2005) in Tainan Chapter, the Chair of the IEEE Life Science Systems and Applications Technical Committee (2008-2009). She served on two terms of ADCOM member of IEEE CIS (2009-2011, 2012-2014), the Chair of IEEE CIS Women in Engineering (2014), and the Chair of CIS Distinguished Lecturer Program (2012-2013). She is a Member of Phi Tau Phi honor society and is an IEEE Fellow since 2008. Currently she is serving as the Vice President for Members Activities of IEEE CIS.
11:20 Big Data Challenges and Deep Learning - Applications to Astronomy
Prof. Pablo A. Estevez . @mail . presentation
Pablo A. Estévez received his professional title in electrical engineering (EE) from Universidad de Chile, in 1981, and the M.Sc. and Dr.Eng. degrees from the University of Tokyo, Japan, in 1992 and 1995, respectively. He is a Full Professor with the Electrical Engineering Department, University of Chile, and former Chairman of the EE Department in the period 2006-2010. Prof. Estévez is one of the founders of the Millennium Institute of Astrophysics (MAS), Chile, which was created in January 2014. He is currently leading the Astroinformatics/Astrostatistics group at MAS. He has been an Invited Researcher with the NTT Communication Science Laboratory, Kyoto, Japan; the Ecole Normale Supérieure, Lyon, France, and a Visiting Professor at the Pantheon-Sorbonne University, Paris, France, and the University of Tokyo, Tokyo, Japan. Prof. Estévez is an IEEE Fellow. He served as President of the IEEE Computational Intelligence Society (CIS) for the term 2016-2017, and currently holds the position of Past President. Prof. Estévez served as conference chair of the International Joint Conference on Neural Networks (IJCNN), held in July 2016, in Vancouver, Canada, and general co-chair of the 2018 IEEE World Congress on Computational Intelligence, IEEE WCCI 2018, held in Rio de Janeiro, Brazil, in July 2018. He has been bestowed recently with the 2019 IEEE CIS Meritorious Service Award. His current research interests include big data, deep learning, neural networks, self-organizing maps, data visualization, feature selection, information theoretic-learning, time series analysis, and advanced signal and image processing. One of his main topics of research is the application of machine learning and computational intelligence techniques to frontier research in astrophysics and biomedical engineering.
14:00 Similarities and fuzzy modelling in data mining
Prof. Bernadette Bouchon-Meunier . webpage
Bernadette Bouchon-Meunier is a director of research emeritus at the National Centre for Scientific Research, the former head of the department of Databases and Machine Learning in the Computer Science Laboratory of the University Pierre et Marie Curie-Paris 6 (LIP6). She is the Editor-in-Chief of the International Journal of Uncertainty, Fuzziness and Knowledge-based Systems, the (co)-editor of 27 books, and the (co)-author of five. She has (co)-authored more than 400 papers on approximate and similarity-based reasoning, as well as the application of fuzzy logic and machine learning techniques to decision-making, data mining, risk forecasting, information retrieval, user modelling, sensorial and emotional information processing. She is currently the IEEE Computational Intelligence Society Vice-President for Conferences, the IEEE France Section Vice-President for Chapters and the IEEE France Section Computational Intelligence Chapter Vice-Chair. She is an IEEE Life Fellow and an International Fuzzy Systems Association Fellow. She received the 2012 IEEE Computational Intelligence Society Meritorious Service Award, the 2017 EUSFLAT Scientific Excellence Award and the 2018 IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award.
14:40 Recognition Technology: A modern perspective of Lofti Zadeh's vision
Prof. James M. Keller . @mail . presentation
James M. Keller received the Ph.D. in Mathematics in 1978. He holds the University of Missouri Curators’ Distinguished Professorship in the Electrical and Computer Engineering and Computer Science Departments on the Columbia campus. He is also the R. L. Tatum Professor in the College of Engineering. His research interests center on computational intelligence: fuzzy set theory and fuzzy logic, neural networks, and evolutionary computation with a focus on problems in computer vision, pattern recognition, and information fusion including bioinformatics, spatial reasoning in robotics, geospatial intelligence, sensor and information analysis in technology for eldercare, and landmine detection. His industrial and government funding sources include the Electronics and Space Corporation, Union Electric, Geo-Centers, National Science Foundation, the Administration on Aging, The National Institutes of Health, NASA/JSC, the Air Force Office of Scientific Research, the Army Research Office, the Office of Naval Research, the National Geospatial Intelligence Agency, the Leonard Wood Institute, and the Army Night Vision and Electronic Sensors Directorate. Professor Keller has coauthored around 500 technical publications. Jim is a Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE), a Fellow of the International Fuzzy Systems Association (IFSA), and a past President of the North American Fuzzy Information Processing Society (NAFIPS). He received the 2007 Fuzzy Systems Pioneer Award and the 2010 Meritorious Service Award from the IEEE Computational Intelligence Society (CIS). He has been a distinguished lecturer for the IEEE CIS and the ACM. Jim finished a full six year term as Editor-in-Chief of the IEEE Transactions on Fuzzy Systems, followed by being the Vice President for Publications of the IEEE Computational Intelligence Society from 2005-2008, then as an elected CIS Adcom member, and is in another stint as VP Pubs. He was the IEEE TAB Transactions Chair as a member of the IEEE Periodicals Committee, and is a member of the IEEE Publication Review and Advisory Committee from 2010 to 2017. Among many conference duties over the years, Jim was the general chair of the 1991 NAFIPS Workshop, the 2003 IEEE International Conference on Fuzzy Systems, and co-general chair of the 2019 IEEE International Conference on Fuzzy Systems.
15:40 Evolutionary Multi-Objective Optimization: Past, Present, and Future
Prof. Carlos A. Coello Coello . wepage . presentation
Carlos Artemio Coello Coello received a PhD in Computer Science from Tulane University (USA) in 1996. His research has mainly focused on the design of new multi-objective optimization algorithms based on bio-inspired metaheuristics, which is an area in which he has made pioneering contributions. He currently has over 450 publications which, according to Google Scholar, report over 43,400 citations (with an h-index of 82). He has received several awards, including the National Research Award (in 2007) from the Mexican Academy of Science (in the area of exact sciences), the 2009 Medal to the Scientific Merit from Mexico City's congress, the Ciudad Capital: Heberto Castillo 2011 Award for scientists under the age of 45, in Basic Science, the 2012 Scopus Award (Mexico's edition) for being the most highly cited scientist in engineering in the 5 years previous to the award and the 2012 National Medal of Science in Physics, Mathematics and Natural Sciences from Mexico's presidency (this is the most important award that a scientist can receive in Mexico). He is also the recipient of the prestigious 2013 IEEE Kiyo Tomiyasu Award, "for pioneering contributions to single- and multiobjective optimization techniques using bioinspired metaheuristics" and of the 2016 The World Academy of Sciences in “Engineering Sciences”. Since January 2011, he is an IEEE Fellow. He is also Associate Editor of several journals including the two most prestigious in his area: IEEE Transactions on Evolutionary Computation and Evolutionary Computation. He is currently Vicepresident for Member Activities of the IEEE Computational Intelligence Society and Full Professor with distinction at the Computer Science Department of CINVESTAV-IPN in Mexico City, Mexico.
16:20 Dealing with the open world classification problem with neural networks
Prof. Nikhil R. Pal . webpage . presentation
Nikhil R. Pal is a Professor in the Electronics and Communication Sciences Unit of the Indian Statistical Institute. His current research interest includes brain science, computational intelligence, machine learning and data mining. He was the Editor-in-Chief of the IEEE Transactions on Fuzzy Systems for the period January 2005-December 2010. He has served/been serving on the editorial /advisory board/ steering committee of several journals including the International Journal of Approximate Reasoning, Applied Soft Computing, International Journal of Neural Systems, Fuzzy Sets and Systems, IEEE Transactions on Fuzzy Systems and the IEEE Transactions on Cybernetics. He is a recipient of the 2015 IEEE Computational Intelligence Society (CIS) Fuzzy Systems Pioneer Award, He has given many plenary/keynote speeches in different premier international conferences in the area of computational intelligence. He has served as the General Chair, Program Chair, and co-Program chair of several conferences. He was a Distinguished Lecturer of the IEEE CIS (2010-2012, 2016-2018.) and was a member of the Administrative Committee of the IEEE CIS (2010-2012). He has served as the Vice-President for Publications of the IEEE CIS (2013-2016). He is serving as the President of the IEEE CIS (2018-2019). He is a Fellow of the National Academy of Sciences, India, Indian National Academy of Engineering, Indian National Science Academy, International Fuzzy Systems Association (IFSA), The World Academy of Sciences, and a Fellow of the IEEE, USA.