KEYNOTE SPEAKERS:
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Professor Ricardo Baeza-Yates, IEEE Fellow & ACM Fellow,
website: http://www.baeza.cl/ |
SPEECH TITLE: "Bias on Search and Recommender Systems"
SUMMARY:
In this presentation we cover all biases that affect search and recommender systems. They include biases on the data, the algorithms as well as the user interaction, in particular the ones related to relevance feedback loops (e.g., ranking and personalization). In each case we cover the main concepts and when known, the techniques to ameliorate them, as well as biases that might be product of the evaluation methods used. This presentation is partially based on Bias on the Web, Communications of ACM, June 2018.
ABOUT THE SPEAKER:
Ricardo Baeza-Yates is, since June 2016, CTO of NTENT, a semantic search technology company based in California, USA. He is also the Director of Data Science Programs at Northeastern University, Silicon Valley campus, since August 2017. He is also part-time professor at Universitat Pompeu Fabra in Barcelona and Universidad de Chile in Santiago. Before, he was VP of Research at Yahoo Labs, based in Barcelona, Spain, and later in Sunnyvale, California, from January 2006 to February 2016. Until 2004 he was the founding director of the Center for Web Research at the University of Chile. He is co-author of the best-seller Modern Information Retrieval textbook published by Addison-Wesley in 1999 and 2011 (2nd ed), that won the ASIST 2012 Book of the Year award. From 2002 to 2004 he was elected to the Board of Governors of the IEEE Computer Society and between 2012 and 2016 was elected for the ACM Council. Since 2010 is a founding member of the Chilean Academy of Engineering. In 2009 he was named ACM Fellow and in 2011 IEEE Fellow, among other awards and distinctions. He obtained a Ph.D. in CS from the University of Waterloo, Canada, in 1989, and his areas of expertise are web search and data mining, information retrieval, data science and algorithms in general.
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Professor Pavel Pevzner Chair and Distinguished Professor of Computer Science and Engineering,
website: https://bioalgorithms.ucsd.edu/ |
SPEECH TITLE: "Open Algorithmic Problems in Immunogenomics"
SUMMARY:
Rapid development of DNA sequencing technologies opened new avenues for analyzing adaptive immune system through deep interrogation of antibody repertoires. I will describe some unanticipated features of the immune system revealed by recent studies of antibody repertoires and will discuss emerging algorithmic problems in immunogenomics that relate to information theory, clustering, evolutionary tree reconstruction, and analysis of variations in the immune system across human population. Solving these problem will open a door toward an emerging field personalized immunogenomics that links genomic variations with patient’s ability to mount an effective antibody response against an infection.
This is a joint work with Yana Safonova (UCSD), Vinnu Bharvaj (UCSD), Stefano Bonissone (Digital Proteomics), and Andrey Bzikadze (UCSD).
ABOUT THE SPEAKER:
Pavel Pevzner is Ronald R. Taylor Professor of Computer Science and Engineering and Director of the NIH Center for Computational Mass Spectrometry at University of California, San Diego. He holds Ph.D. from Moscow Institute of Physics and Technology, Russia. He was named Howard Hughes Medical Institute Professor in 2006. He was elected the Association for Computing Machinery Fellow in 2010, the International Society for Computational Biology Fellow in 2012, the European Academy of Sciences member (Academia Europaea) in 2016, and the American Association for Advancement in Science (AAAI) Fellow in 2018. He was awarded a Honoris Causa (2011) from Simon Fraser University in Vancouver, the Senior Scientist Award (2017) by the International Society for Computational Biology, and the Kanellakis Theory and Practice Award from the Association for Computing Machinery. Dr. Pevzner authored textbooks "Computational Molecular Biology: An Algorithmic Approach", "Introduction to Bioinformatics Algorithms" (with Neal Jones), “Bioinformatics Algorithms: an Active Learning Approach” (with Phillip Compeau), and “Learning Algorithms Through Programming and Puzzle Solving.” He co-developed the Bioinformatics and Data Structure and Algorithms online specializations on Coursera (each with over 400,000 enrolled students so far) as well as the Algorithms Micro Master Program at edX.
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Dr. Mufti Mahmud Senior Lecturer, Computing at the Nottingham Trent University, UK
website: www.ntu.ac.uk/staff-profiles/science-technology/mufti-mahmud |
SPEECH TITLE: "Towards Artificially Intelligent Devices for Healthcare and Rehabilitation"
SUMMARY:
The world is witnessing a rapid increase of the elderly population. This has brought unprecedented challenge in delivering healthcare, specially to the population with neurological disorders. In case of the prevalent ones, such as Alzheimer's and Parkinson’s Disease, along with regular medication, technological intervention is a unique way to assist them in their daily lives. While the world is falling short in providing manual support to these people, thanks to Artificial Intelligence (AI) based innovative solutions, the management of such people with neurological disorder is now possible. The talk will shed lights on how persons with such incurable diseases can be assisted in their daily lives through AI-enabled devices.
ABOUT THE SPEAKER:
Mufti Mahmud is a Senior Lecturer of Computing at the Nottingham Trent University, UK. He received PhD degree in Information Engineering from University of Padova – Italy, in 2011. A recipient of the Marie-Curie postdoctoral fellowship, he served at various positions in the industry and academia in India, Bangladesh, Italy, Belgium, and the UK since 2003. An expert in computational intelligence and big data technologies, Dr. Mahmud aims to develop secure and intelligent tools to advance healthcare access in low-resource settings. Dr. Mahmud serves as Associate Editor to the Cognitive Computation, IEEE Access, Big Data Analytics, and Brain Informatics journals. A senior member of IEEE and ACM, he is currently serving as Vice Chair of the Intelligent System Application Technical Committee of IEEE CIS, Member of the IEEE CIS Task Force on Intelligence Systems for Health and the IEEE R8 Humanitarian Activities Subcommittee, and Project Liaison Officer of the IEEE UK and Ireland SIGHT committee. Dr. Mahmud is also serving as Local organising chair of IEEE-WCCI2020; General Chair of BI2020 and BI2021; and Programme Chair of IEEE-CICARE2020.
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Professor Jeanna Matthews Founding co-chair of the ACM Technology Policy Subcommittee on AI and Algorithm Accountability,
website: people.clarkson.edu/~jmatthew/ |
SPEECH TITLE: "Algorithmic Accountability and the Securing of our Decision-Making Landscape"
SUMMARY:
Increasingly big decisions about the lives of individuals are being made in a partnership between human decision makers and computer systems. Algorithmically moderated platforms are making profound impacts on our personal and public relationships such as how we find a job, how we get our news, how we drive from place to place, sometimes even how we find a spouse. This is fundamentally changing the landscape of our societal decision-making processes - from hiring decisions, to decisions about news amplification, to criminal justice decisions - and making them vulnerable to new types of attacks and influences. To build the world we want, we need algorithms and platforms to be accountable and transparent. I will discuss the role of algorithmic accountability in securing these decision-making processes, using examples from my current work in criminal justice software, media manipulation and quantifying machine learning bias.
ABOUT THE SPEAKER:
Jeanna Matthews is a professor of computer science at Clarkson University and an affiliate at Data and Society. She has published work in a broad range of systems topics from virtualization and cloud computing to social media security and distributed file systems. She has been a four-time presenter at DEF CON on topics including security vulnerabilities in virtual environments (2015 and 2016), adversarial testing of criminal justice software (2018) and trolling (2018). She is an ACM Distinguished Speaker, a Fulbright Specialist, founding co-chair of the ACM Technology Policy Subcommittee on Artificial Intelligence and Algorithm Accountability and a member of the ACM Technology Policy Committee. She has been a member of the ACM Council (2015-present), chair of the ACM Special Interest Group Governing Board ( 2016-2018), the chair of the ACM Special Interest Group on Operating Systems (SIGOPS) from 2011 to 2015 and the author of several popular books ("Computer Networking: Internet Protocols in Action" and "Running Xen: A Hands on Guide to the Art of Virtualization"). Her current work focuses on securing societal decision-making processes and supporting the rights of individuals in a world of automation. She received a 2018-2019 Brown Institute Magic Grant to research differences in DNA software programs used in the criminal justice system. Jeanna received her Ph.D. in Computer Science from the University of California at Berkeley in 1999, a B.S. in Mathematics and Computer Science from Ohio State University in 1994 and a B.A. in Spanish from the State University of New York at Potsdam in 2016.
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Professor Mark Last Head of Ben-Gurion University Data Science Research Center,
website: in.bgu.ac.il/en/engn/sise/mlast/Pages/default.aspx |
SPEECH TITLE: "Mining Multi-label, Multi-target, and Evolving Data with Info-Fuzzy Networks"
SUMMARY:
Born about 50 years ago, decision trees have survived the hype of support vector machines, deep neural networks, and other creative ways to generate 'black-box" models from data. They are still appreciated for their remarkable ability to provide an interpretable flowchart-like representation of a nonlinear decision-making process. However, popular decision-tree learning algorithms often produce over-complex models, which may be too sensitive to small, random variations in the training data. Moreover, those algorithms are limited by several nonrealistic assumptions like stationarity of the incoming data, immediate availability of all predictive features, and single-class labeling of each data instance. My tutorial will start with a brief introduction to Info-Fuzzy Networks (IFN), a robust information-theoretic methodology for inducing compact, stable, and accurate decision trees. Then I will describe in detail several IFN extensions for mining multi-target, multi-label, and dynamic data streams including M-IFN (Multi-target Info-Fuzzy Network) and OLIN (On-Line Information Network). Another extension of IFN, aimed at evolving classification of event data streams, where some features of a monitored entity can be delayed or updated over time, will also be discussed. The application potential of the presented info-fuzzy algorithms will be demonstrated via the results of benchmarking experiments as well as detailed case studies in medical, transportation, cyber security, and other domains.
ABOUT THE SPEAKER:
Mark Last is a Full Professor at the Department of Software and Information Systems Engineering, Ben-Gurion University of the Negev, Israel and the Head of the Data Science Research Center at Ben-Gurion University. He obtained his Ph.D. degree from Tel Aviv University, Israel in 2000. Prof. Last has published over 200 peer-reviewed papers and 11 books on data mining, text mining, and cyber security. He currently serves as an Editorial Board Member of Data Mining and Knowledge Discovery. Previously, he has served as an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics - Part C (2004 – 2012), Pattern Analysis and Applications (2007- 2016), and IEEE Transactions on Cybernetics (2013-2019). His current research interests are focused on data stream mining, cross-lingual text mining, cyber intelligence, and medical informatics.
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Professor Bychkov Igor Academician of Russian Academy of Sciences,
website: baikalfoundation.ru/en/about-the-foundation/staff/igor-bychkov/ |
SPEECH TITLE: "Supercomputer Engineering for Supporting Decision-making on Energy Systems Resilience"
SUMMARY:
We propose a new approach to creating a subject-oriented distributed computing environment. Such an environment is used to support decision-making in solving relevant problems of ensuring energy systems resilience. The proposed approach is based on supercomputer engineering including high-performance computing, intelligent computation planning and resource allocation, big data processing, and geo-information servicing. Evaluation of decision-making alternatives is carrying out through applying combinatorial modeling and multi-criteria selection rules. The Orlando Tools framework is used as the basis for an integrated software environment. It implements a flexible modular approach to the development of scientific applications (distributed applied software packages).
ABOUT THE SPEAKER:
Igor Bychkov – Academician of Russian Academy of Sciences, professor, Ph.D., director of the Matrosov Institute for System Dynamics and Control Theory of Siberian Branch of Russian Academy of Sciences, scientific leader of the Irkutsk Scientific Center of Siberian Branch of Russian Academy of Sciences. He is a member of a number scientific and expert councils, editorial boards of scientific journals. He is an expert for the Russian Foundation for Basic Research, Russian Scientific Foundation, Russian Academy of Sciences. He leads a number of national and international research projects. His main interests include artificial intelligence, geoinformation systems, WEB-technologies, systems of intelligent data analysis, mathematical modelling, cloud computing.
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Professor Marat Rakhmatullaev Tashkent University of Information Technology, Uzbekistan
website: www.linkedin.com/in/marat-rakhmatullaev-b8181837/ |
SPEECH TITLE: "Expert system with fuzzy logic for protecting scientific information resources"
SUMMARY:
The purpose of the research is to create effective methods and tools to protect the databases of the scientific and educational resources from unauthorized access in libraries and library networks using fuzzy logic methods. As a model and solution method, a fuzzy model of second-type correspondences is proposed, which makes it possible to comprehensively solve the problem of identifying threats in the event of a situation, as well as provide recommendations on how to eliminate them. For the creation of a knowledge database, experts are involved who determine the functions of belonging to “Situation-Threat-Methods of eliminating threats”. The research results are used in the expert system the corporate network of electronic libraries to protect scientific and educational information from unauthorized access.
ABOUT THE SPEAKER:
Dr. Marat Rakhmatullaev is a professor of Tashkent University of Information Technology. He received PhD in 1984 and DC in 1994. 2006-2007 Fulbright scholarship. Library Information Science. Supervisor of 9 PhD students. Research interests are intelligence, expert systems, big data, fuzzy logic, information security of information library corporate networks, and systems. Editor-in-chief of two scientific and methodological journals "Information resources for innovative development" and " Prospects of high education". Author of more than 20 scientific projects on ICT,190 scientific papers, and 12 books. Professor Rakhmatullaev was the coordinator of TEMPUS (2009) and ERASMUS+(2018) projects. He was awarded the “Friendship” medal of the President of Uzbekistan for the contribution to science, education and international cooperation (2011) and the Kyrgyz Republic medal for his contribution to education and the library sphere (2019). Member of the specialized Council of TUIT for the defense of dissertations. Under his supervision 2 Doctoral and 7 PhD theses were successfully defended.