19^th Mexican International Conference on Artificial Intelligence

Ljiljana Trajkovic is a Professor in the School of Engineering Science at Simon Fraser University, Burnaby, British Columbia, Canada.

Machine Learning Techniques for Detecting Network Anomalies

Abstract: The Internet, social networks, power grids, gene regulatory networks, neuronal systems, food webs, social systems, and networks emanating from augmented and virtual reality platforms are all examples of complex networks. Collection and analysis of data from these networks is essential for their understanding. Traffic traces collected from various deployed communication networks and the Internet have been used to characterize and model network traffic, analyze network topologies, and classify network anomalies. Data mining and statistical analysis of network data have been employed to determine traffic loads, analyze patterns of users' behavior, and predict future network traffic while spectral graph theory has been applied to analyze network topologies and capture historical trends in their development. Machine learning techniques have proved valuable for predicting anomalous traffic behavior and for classifying anomalies and intrusions in communication networks. Applications of these tools help understand the underlying mechanisms that affect behavior, performance, and security of computer networks.

Bio: She received the Ph.D. degree in electrical engineering from the University of California at Los Angeles. Her research interests include high-performance communication networks, control of communication systems, computer-aided circuit analysis, and design, and theory of nonlinear circuits and dynamical systems.

Dr. Trajkovic serves as IEEE Division X Delegate/Director (2019–2020). She served as Senior Past President (2018–2019) of the IEEE Systems, Man, and Cybernetics Society and as 2007 President of the IEEE Circuits and Systems Society.

Dr. Trajkovic is a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society (2020–2021). She is a Life Fellow of the IEEE.
 

Pedro Larrañaga, Technical University of Madrid, UPM, España.

Bayesian Networks in Neuroscience

Abstract: During the talk, the recent use of Bayesian networks in challenging real world applications developed inside the Human Brain Project will be presented. These neuroscience applications will cover problems at different scales: from neuroanatomy questions, such as interneuron classification and spine clustering, to diagnosis of neurodegenerative Parkinson and Alzheimer diseases.

Bio: Pedro Larrañaga is Full Professor in Computer Science and Artificial Intelligence at the Technical University of Madrid (UPM) since 2007. He received the MSc degree in mathematics (statistics) from the University of Valladolid and the PhD degree in computer science from the University of the Basque Country.

He has published more than 200 papers in high impact factor journals and has supervised 31 PhD theses. He is fellow of the European Association for Artificial Intelligence since 2012 and fellow of the Academia Europaea since 2018. He has been awarded the 2013 Spanish National Prize in Computer Science, the prize of the Spanish Association for Artificial Intelligence in 2018 and the Amity Research Award in Machine Learning in New Delhi, in 2020.

His research interests are primarily in the areas of probabilistic graphical models, metaheuristics for optimization, data mining, classification models, and real applications, like biomedicine, bioinformatics, neuroscience, industry 4.0 and sports.

The role of artificial intelligence in the transformation of the banking sector

Abstract: In the past ten years, we have witnessed the evolution of the concept of artificial intelligence towards a concept that has captured the collective imagination with its promises of transformation. Machine learning and its flagship applications: Alpha Go, the autonomous car, image recognition, machine translation, etc., have created expectations in terms of the transformative potential of certain sectors of economic activity. Historically, the banking sector has been a kind of ocean liner that has struggled to change direction. In terms of technological innovation, banks have been overtaken by the fintech ecosystem in terms of agility. As banks try to catch up, it's important to take a hard look at the promise of artificial intelligence and what it means for the banking industry. In this presentation, we will discuss the transformation potential that artificial intelligence brings to the sector, but also the challenges that this transformation represents. We will discuss these questions around some examples of the application of machine learning in banking and capital markets.

Bio: He holds a Ph.D. on Statistics from Concordia University. He held a position as an Assistant Professor at York University in Toronto from where he moved to Montreal where he is a professor in mathematics at the University of Montreal since 2005. He serves on the Board of Directors of Koïos Intelligence and as scientific advisor of Kapitalwise, two emerging companies in the field of AI in finance and insurance.

He is an experienced scientist and leader in the field of Artificial Intelligence in banking who often gets called upon for his knowledge and experience in creating business value through AI. He has experience in accompanying businesses through their digital transformation. Based on his scientific expertise, he seizes the threads of opportunity that wind through an organization and helps transform them into value propositions, He remains a member of the scientific community which gives him a valuable perspective and credibility as he links up and aligns technical data science teams with product owners/managers into a coherent AI transformation strategy.

As Chief AI Scientist at National Bank of Canada, the sixth largest commercial bank in Canada, Manuel oversaw the scientific component of the AI transformation initiative of twenty billion dollar plus banking institution.He successfully contributed to the strategy and operationalization of an ambitious initiative that seeks to transform the bank’s processes and services through artificial intelligence. In particular, his involvement in the worldwide discussion on AI Ethics and his commitment with responsible AI deployments gives him a unique perspective that has become an asset for the ongoing AI transformation initiative since a comprehensive AI governance and ethics remain key to successful deployments. His work with National Bank was featured in an interview piece in Forbes on-line magazine in May 2020.

He continues to conduct his research and teaching activities at the University of Montreal where he is the director of the FinML network, whose mandate is to train the next AI-enabled generation of finance and banking professionals. He has also established himself as a well- known presence in the Montreal FinTech ecosystem where he is a scientific advisor of the Holt Accelerator, one of the leading accelerators in Montreal targeting seed start-up’s, as well as one co-founder of the OCF Group, a venture building group that leverages Montreal AI talent in order to help start-up’s to go from series A to series B through machine learning technology. He is regularly called upon deliver talks and share his experience on AI transformation in banking and on AI Ethics and Governance in Finance as well as to organize tailored executive workshops for financial institutions engaged in their own AI transformation initiative.

Utterance-level Dialogue Understanding

Abstract: The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialog understanding. Complete utterance-level understanding often requires context understanding, defined by nearby utterances. In recent years, a number of approaches have been proposed for various utterance-level dialogue understanding tasks. Most of these approaches account for the context for effective understanding. In this talk, I will explore and quantify the role of context for different aspects of a dialogue, namely emotion, intent, and dialogue act identification, using state-of-the-art dialog understanding methods as baselines. Specifically, I will discuss various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines. This provides us with insights into the fundamental contextual controlling factors of different aspects of a dialogue. Such insights can inspire more effective dialogue understanding models, and provide support for future text generation approaches. The implementation pertaining to this talk is available at https://github.com/declare-lab/ dialogue-understanding.

Bio: Soujanya Poria an assistant professor of Computer Science at the Singapore University of Technology and Design (SUTD), Singapore. He holds a Ph.D. degree in Computer Science from the University of Stirling, UK. Soujanya was a recipient of the prestigious early career research award called 'NTU Presidential Postdoctoral Fellowship' in 2018 which offers him a research grant worth US$150,000. Before taking up the presidential fellowship position at the NTU, he was a scientist at the A*STAR and the Temasek Laboratory, NTU. He is also PI of multiple academic and industrial grants with the amount totaling to US$ 1,500,000.

He has co-authored more than 100 papers, published in top-tier conferences and journals such as ACL, EMNLP, AAAI, NAACL, Neurocomputing, Computational Intelligence Magazine, etc. At present, he is also affiliated with A*STAR, Singapore as a senior scientist. He was an area co-chair at ACL 2020, NAACL 2019, EMNLP 2020, 2019, and a publicity chair at *SEM 2019. He served as a senior PC member at AAAI 2019, IJCAI 2019, and often serve as a PC member in reputed conferences such as ACL, EMNLP, IJCAI, NAACL. He has given several invited talks at venues like CICLing 2018 which is a large international NLP conference, SocialNLP 2019. He has Google Scholar citations of more than 8000 and my h-index is 44. Recently, he has been awarded the IEEE CIM outstanding paper award. Three of his papers are listed as Web of Science highly cited papers in the field of Computer Science. Recently, in an article published in the Journal of Information Sciences, he has been listed as one of the most prolific and impactful researchers from 2000 to 2016 in the field of sentiment analysis.

Artificial Intelligence and Natural Minds

Abstract: Connections between Artificial Intelligence and other sciences go back and forth. On the one hand, AI has tried to replicate human capabilities and behavior, making use of studies in Neurosciences, Linguistics and Cognition; on the other hand, these in turn have been benefited from the computational models created based on their theories, as in Computational Linguistics, for example.

Contrary to some fearful theories, the study of Artificial Intelligence in conjunction with recent discoveries allow us to shed important light on the human mind, and help us investigate what makes us human. In this talk we will address some attempts that have been made to create Computational Models of the mind, their psychological implications, and some philosophical issues.

Bio: Full-time professor at the Center for Computing Research of the National Polytechnic Institute, Mexico. He is a member of the National System of Researchers Level II.

Research interest. Computational Linguistics, Lexical Semantics and Learning Algorithms.

Human-Centered Computing: Transdisciplinary science of the human embodiment in intelligent computing

Abstract: Human-Centered Computing (HCC) is a transdisciplinary science linking the recently popular intelligent technologies (Artificial Intelligence, Computational Intelligence, Soft Computing, Machine Learning, Pattern Recognition, Data Mining) with all sensations of a human embodiment. HCC will usually integrate two or more techniques to tackle challenging problems or find a practical application, having the human or human sense or reach expansion, or to compensate for human impediments as a central theme: image and signal processing (to enrich the human visual system), networking and collaborative systems (the human as a social being), security, physiology/psychology, sentiment analysis, smart recommendation systems, science of preferences, up to aspects of political economy and complex systems theory. In this talk, after a short account of the historical development of the discipline, it will be illustrated why a single technique isn't sufficient to solve a number of tasks and why their integration is needed. The question whether there is a "human way" of computing, different from what a typical computer is doing these days, will find a positive and intuitive answer. Apart from those examples, recalling Gestalt psychology, as well as neural paradigms like Hierarchical-Temporal-Memory (HTM) preceding popular neural architectures these days, will help to make the links, independence and need for a new discipline here even more obvious. Based on such more theoretical backing, we can find a number of potential or already existing applications, esp. with regard to human assessment of ranking, social network models, thermal imaging, among others.

Bio: Mario Köppen is a Professor in the Graduate School for Creative, Informatics at the Kyuhsu Institute of Technology, Fukuoka, Japan. He has published more than 150 peer-reviewed papers in conference proceedings, journals and books and was active in the organization of various conferences as chair or member of the program committee, incl. the WSC on-line conference series on Soft Computing in Industrial Applications, and the HIS conference series on Hybrid Intelligent Systems. He is founding member of the World Federation of Soft Computing, and also Associate Editor of the Applied Soft Computing journal. In 2006, he became JSPS fellow at the Kyushu Institute of Technology in Japan, and in 2008 Professor at the Network Design and Reserach Center (NDRC) and 2013 Professor at the Graduate School of Creative Informatics of the Kyushu Institute of Technology, where he is conducting now research in the fields of multi-objective and relational optimization, digital convergence and multimodal content management.

Research interest. Mario Köppen is an expert in Pattern Recognition, Image Processing, Soft Computing, Computational Intelligence, Security Technologies, (Multi-Objective) Optimization, and Algorithm Theory.