Kasper Hornbæk received his M.Sc. and Ph.D. in Computer Science from the University of Copenhagen, in 1998 and 2002, respectively. Since 2014 he has been a professor in computer science at the University of Copenhagen. His core research interests is human-computer interaction, including usability research, shape-changing interfaces, large displays, body-based user interfaces, and information visualization. He serves on the editorial board of ACM Transactions on Human-computer Interaction and Human-Computer Interaction. In 2020, he was induced to the CHI Academy.

Theory is supposedly central to scholarship. Yet, the field of human-computer interaction (HCI) is confused about what theory is and what to do with it. In this talk I will present some data on what we do with theory in HCI. I will also discuss some ideas on what we instead should do with theory. They include conceptual analysis, building and testing propositions, and comparing theories.

Georgia Koutrika is a Research Director at Athena Research Center in Greece. She has more than 15 years of experience in multiple roles at HP Labs, IBM Almaden, and Stanford. Her work emerges at the intersection of data management, natural language processing and deep learning and focuses on intelligent and interactive data exploration, conversational data systems, and user-driven data management. Her work has been incorporated in commercial products, described in 14 granted patents and 26 patent applications in the US and worldwide, and published in more than 100 papers in top-tier conferences and journals.

Georgia is an ACM Senior Member and IEEE Senior Member. She is a member of the VLDB Endowment Board of Trustees, member of the PVLDB Advisory Board, member of the ACM-RAISE Working Group, co-Editor-in-chief for VLDB Journal, PC co-chair for VLDB 2023, co-EiC of Proceedings of VLDB (PVLDB). She has been associate editor in top-tier conferences (such as ACM SIGMOD, VLDB) and journals (VLDB Journal, IEEE TKDE), and she has been in the organizing committee of several conferences including SIGMOD, ICDE, EDBT, among others.

She has received a PhD and a diploma in Computer Science from the Department of Informatics and Telecommunications, University of Athens, Greece.

Algorithmic systems, driven by large amounts of data, are increasingly being used in all aspects of society to assist people in forming opinions and taking decisions. For instance, search engines and recommender systems amongst others are used to help us in making all sort of decisions from selecting restaurants and books, to choosing friends and careers. Other systems are used in school admissions, housing, pricing of goods and services, job applicant selection, and so forth. Such algorithmic systems offer enormous opportunities, but they also raise concerns regarding how fair they are. How much trust can we put in these systems?

We will analyze fairness risks through well-known use cases. Then, we will present some representative models and methods for fairness in search engines and recommender systems. We will conclude our journey to algorithmic fairness by discussing challenges and critical research paths for future work.

John Daugman received his degrees at Harvard University and then taught at Harvard before coming to Cambridge University, where he is Professor of Computer Vision and Pattern Recognition. He has held the Johann Bernoulli Chair of Mathematics and Informatics at the University of Groningen, and the Toshiba Endowed Chair at the Tokyo Institute of Technology. His areas of research and teaching include computer vision, information theory, neuro computing, and statistical pattern recognition. Awards for his work in science and technology include medals from the British Computer Society, and the OBE (Order of the British Empire) from The Queen. He has been elected to Fellowships of: the UK Royal Academy of Engineering; the US National Academy of Inventors; and the Institute of Mathematics and its Applications. He has been inducted permanently into the US National Inventors Hall of Fame. He is the founder and benefactor of the Cambridge Chrysalis Trust.

Questions of human identity take many forms, including: (1) the mind/brain problem: how our mental life and consciousness arise from neural activity in the brain; (2) the relationships among concepts of personhood, society, and the state (who is master, who is servant); and (3) how the identities of different persons can be visually distinguished from each other. Recent progress in artificial intelligence and machine learning gives perspectives on all these topics. This lecture will discuss several aspects of the Identity questions, focusing mainly on computational neuroscience, computer vision, automatic face and iris recognition, and the concept of biometric entropy. Understanding big biometric entropy has resulted in 1.5 billion persons worldwide being enrolled by their iris patterns (including 1.3 billion in India for the UIDAI welfare benefits distribution scheme), after US Government tests involving trillions of iris comparisons, using the speaker’s algorithms.

Masashi Sugiyama received a Ph.D. degree in Computer Science from Tokyo Institute of Technology, Japan, in 2001. After experiencing assistant and associate professors at the same institute, he became a professor at the University of Tokyo in 2014. Since 2016, he has concurrently served as Director of RIKEN Center for Advanced Intelligence Project, leading the groups of fundamental AI technologies, AI applications, and social issues of AI. His research interests include theories and algorithms of machine learning and statistical data analysis. He (co)-authored various machine learning monographs, including Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), Statistical Reinforcement Learning (Chapman and Hall, 2015), Introduction to Statistical Machine Learning (Morgan Kaufmann, 2015), Variational Bayesian Learning Theory (Cambridge University Press, 2019), and Machine Learning from Weak Supervision (MIT Press, in press). He served as a Program Chair and General Chair for NeurIPS2015 and NeurIPS2016 and a Program Chair for AISTATS2019. He was a recipient of the Japan Academy Medal in 2017.

Artificial intelligence became an indispensable tool to advance science and industry, and machine learning is one of the main driving forces to boost this movement.

In this talk, I will first introduce the activities of the RIKEN Center for Advanced Intelligence Project (RIKEN-AIP): RIKEN is Japan's largest and most comprehensive research organization for basic and applied science, and AIP is working on advancing fundamental artificial intelligence technologies (machine learning, optimization, etc.), their applications in accelerating scientific research (cancer, material, etc.) and solving socially critical problems (natural disaster, elderly healthcare, etc.), and discuss social aspects of artificial intelligence (ethical guidelines, personal data management, etc.).

Then I will give an overview of our recent research achievements on reliable machine learning: In modern applications of machine learning, it becomes increasingly important to consider robustness against various factors such as data bias (caused by changing environments, privacy concerns, etc.) and insufficient and inaccurate information (due to weak supervision, label noise, etc.). We have developed theories and algorithms of machine learning to cope with such problems.

Mohamed Mokbel (PhD, Purdue University, MS, BS, Alexandria University) is a Professor at the University of Minnesota. Prior roles while on leave/sabbatical from UMN include Chief Scientist of Qatar Computing Research Institute, Founding Technical Director of GIS Technology Innovation Center in Saudi Arabia, and multiple times Visiting Researcher at Microsoft Research, USA. His research interests include database systems, spatial data, and GIS. His research work has been recognized by the NSF CAREER Award, VLDB 10-years Best Paper Award, and four conference Best Paper Awards. Mohamed is the past elected Chair of ACM SIGSPATIAL, current Editor-in-Chief for Springer Distributed and Parallel Databases (DAPD) Journal, and on the editorial board of ACM Books, ACM TODS, VLDB Journal, ACM TSAS, and GeoInformatica journals. He has served as PC Co-Chair for ACM SIGMOD, ACM SIGSPATIAL, and IEEE MDM. Mohamed is an IEEE Fellow and ACM Distinguished Scientist. For more information, please visit: www.cs.umn.edu/~mokbel

This talk will focus on our recent efforts in adopting machine learning techniques for big spatial data and applications. This includes going for two orthogonal, but related, directions. First, injecting the spatial awareness inside machine learning techniques and applications, which will result in a higher accuracy for such applications. Second, taking advantage of the recent advances in machine learning techniques to boost the usability, deployment, scalability, and accuracy of long lasting spatial and spatio-temporal data analysis techniques. For the first direction, we will present the Sya system as a full-fledged spatial machine leaning-based probabilistic knowledge base construction system. For the second direction, we will present machine-learning-based techniques for spatial autologistic regression and map services.

Professor Wenfei Fan is the Chair of Web Data Management at the University of Edinburgh, UK, and the Chief Scientist of Shenzhen Institute of Computing Science, China. He is a Fellow of the Royal Society (FRS), a Fellow of the Royal Society of Edinburgh (FRSE), a Member of the Academy of Europe (MAE), an ACM Fellow (FACM), and a Foreign Member of Chinese Academy of Sciences. He received his PhD from the University of Pennsylvania (USA), and his MSc and BSc from Peking University (China). He is a recipient of Royal Society Wolfson Research Merit Award in 2018, ERC Advanced Fellowship in 2015, the Roger Needham Award in 2008 (UK), Yangtze River Scholar in 2007 (China), the Outstanding Overseas Young Scholar Award in 2003 (China), the Career Award in 2001 (USA), and several Test-of-Time and Best Paper Awards (Alberto O. Mendelzon Test-of-Time Award of ACM PODS 2015 and 2010, Best Paper Awards for SIGMOD 2017, VLDB 2010, ICDE 2007 and Computer Networks 2002). His current research interests include database theory and systems, in particular big data, data quality, data sharing, parallel computation, query languages and recommender systems.

Big data is typically characterized with 4V's: Volume, Velocity, Veracity and Value. When it comes to big graphs, these challenges become even more staggering. A number of questions remain open. What parallel computation model should we adopt to scale with big graphs? Does parallel processing suffice to cope with the volume of big graphs? Is there a systematic method for developing effective incremental algorithms in response to frequent updates? Is it possible to uniformly query relational databases and graphs in, e.g., SQL? How can we unify logic rules and machine learning models, to improve the quality of graph-structured data? This talk aims to incite interest in these topics, and raises as many questions as it answers.