Data Day 11.0: Empowering Tomorrow: Quantum Computing Meets Data Science and Machine Learning

Data Day 11.0 will focus on Empowering Tomorrow: Quantum Computing Meets Data Science and Machine Learning. In keeping with this theme, the event program includes panel discussions on quantum computing, data science, machine learning, and new workforce skills trends.

Jointly hosted by the Faculty of Science and the听杏吧原创 University Institute for Data Science (CUIDS),听Data Day is an annual conference that celebrates the latest developments in data science and analytics research.

Data Day includes presentations by 杏吧原创 researchers and industry experts, a keynote address, panel discussion and networking opportunities, as well as a poster competition to showcase graduate student research in data science across all disciplines.

Big OLAP Data Cube Compression Algorithms in Column-Oriented Cloud/Edge Data Infrastructures

Big data is gaining momentum in the research community, due to the several challenges posed by managing such kind of data. Big data are relevant not only in the academic context, but also in the industrial context, where they play the major role. Indeed, several kinds of application are now exploiting big data, such as: Web advertisement, social network intelligence, e-science applications, smart city applications, and so forth. Among big data, big multidimensional data are a special case of big data that fully expose the 鈥渇amous鈥� 3V (volume, velocity, variety) and are of relevant interest at now. Within this research context, the seminar 鈥�Big OLAP Data Cube Compression Algorithms in Column-Oriented Cloud/Edge Data Infrastructures鈥� focuses the attention on the issue of compressing so-called big OLAP data cubes over column-oriented cloud/edge data infrastructures. More specifically, the seminar proposes a specialized representation of massive OLAP data cubes over cloud/edge environments via column-oriented paradigms, which have been traditionally used in fortunate in-memory database query engines. Under this decomposition mechanism, each 鈥渃olumn鈥� is then compressed via a state-of-the-art synopsis data structure, called D-Syn, which already proofed its effectiveness and efficiency in multidimensional data compression, thanks to an innovative analytical interpretation of multidimensional data cubes. The seminar also discusses several alternatives according to which the deriving synopsis chunks can be effectively and efficiently distributed across Cloud and/or Edge nodes, and how to support approximate query answering over such big data structures.

Speaker

Speaker: Prof. Alfredo Cuzzocrea

• iDEA Lab, Director
• University of Calabria, Rende, Italy
• Excellence Chair in Big Data Management and Analytics, University of Paris City, Paris, France
• Honorary Professor of Computer Engineering Amity University, Noida, India
• Research Associate, National Research Council (CNR), Rome, Italy

Alfredo Cuzzocrea is Professor in Computer Engineering at the University of Calabria, Rende, Italy. He is the Director of the Big Data Engineering and Analytics Lab of the University of Calabria. He also covers the role of Full Professor in Computer Engineering at the University of Paris City, Paris, France, as holding the Excellence Chair in Big Data Management and Analytics. His current research interests span the following scientific fields: big data, database systems, data mining, OLAP, data warehousing, and knowledge discovery. He is author or co-author of more than 800 papers in international conferences, international journals and international books. He is recognized in prestigious international research rankings, such as: (i) 1st World-Wide Scientist听 for Research Topic: 鈥淥nLine Analytical Processing (OLAP)鈥� by Microsoft Academic, Redmond, WA, USA; (ii) 5th World-Wide Highly-Ranked Scholar Lifetime for Research Topic: 鈥淏ig Data鈥� by ScholarGPS, Los Angeles, CA, USA; (iii) Top 2% World-Wide Scientist by METRICS, Stanford, CA, USA; (iv) Top Scientists in Computer Science and Electronics by Guide2Research, Clifton, NJ, USA; (v) Top Researchers in Computer Science by SciVal Elsevier, Amsterdam, Netherlands; (vi) Top Italian Scientists in Computer Sciences by Virtual Italian Academy, Manchester, UK.

Seminar Moderator:

Koon-Ho Alan Tsang – Assistant Professor at the School of Computer Science at 杏吧原创 University

Light refreshments will be provided.

Please RSVP below to help us prepare for the event.

Ethical AI: What does it mean, how can we do it, and why should we care?

– Computer Scientist in the Digital Technologies Research Centre at the National Research Council Canada – Adjunct Research Professor at the School of Computer Science – 杏吧原创 University

Every day brings exciting new advancements in Artificial Intelligence (AI), and yet questions remain about whether these technologies have the ability to make the right moral choices 鈥� and who gets to decide what the “right” choice is, anyway? In this talk, I will define eight key pillars of Ethical AI, as established by Harvard’s Berkman Klein Center for Internet & Society: privacy, accountability, safety, transparency, fairness, human control of technology, professional responsibility, and the promotion of human values.听听I will illustrate each theme with real-world examples of the harms that result when these pillars are overlooked听听鈥� and ways that we can avoid these harms to build a better, safer, and more equitable society with AI technology.

Dr. Kathleen Fraser is a computer scientist in the Digital Technologies Research Centre at the National Research Council Canada.听听Her research focuses on natural language processing (NLP), particularly examining the social biases that end up encoded in artificial intelligence systems. She also has a research interest in NLP for healthcare, specifically the automated analysis of speech and language in conditions such as dementia, aphasia, and post-COVID condition (鈥渓ong COVID鈥�). Dr. Fraser received her PhD in computer science from the University of Toronto in 2016, and subsequently completed a post-doc at the University of Gothenburg, Sweden. She was named an MIT Rising Star in Electrical Engineering and Computer Science in 2015, and was awarded the Governor General’s Gold Academic Medal in 2017. She has been a research officer at the National Research Council since 2018, where she was awarded the Digital Technologies 鈥淩esearch Excellence鈥� Award in 2022. She also holds a position as an adjunct research professor at 杏吧原创 University in the School of Computer Science.

Seminar Moderator:

Koon-Ho Alan Tsang – Assistant Professor at the School of Computer Science at 杏吧原创 University

Light refreshments will be provided. Please RSVP below to help us prepare for the event.

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Victims of Circumstance: How Environment Manipulation Shapes Reinforcement-Learning Behaviours

Zinovi Rabinovich, Assistant Professor, School of Computer Science – 杏吧原创 University

Machine learning algorithms have been subjected to a range of attacks, both to thwart and to subvert their learning. It is particularly easy to do with Reinforcement Learning algorithms that heavily depend on their perceptions being reliable, their attempted actions correctly executed, and the rewards they reap indicative of the progress towards their goal. Control any one of those aspects, and you can make an RL agent fail or, worse, learn a bad behaviour. But what if perceptions come with error correcting codes, actions are verifiable, and the reward is strictly intrinsic to the agent? Are our RL agents safe from manipulation, then? Turns out no. It is possible, by the process of environment poisoning (i.e., changing how the environment behaves in response to agent actions), to manipulate an RL agent into learning a target (bad) behaviour. In this talk, I will show how it can be done, discuss how flexible the approach is, and what the future expects of it.

Zinovi Rabinovich is an Assistant Professor in the School of Computer Science at 杏吧原创 University. He obtained his Ph.D. in Computer Science from the Hebrew University in Jerusalem, spent some years as an Algorithms Engineer at Mobileye Vision Technologies Ltd, and moved back into academia. His research focuses on how to leverage information asymmetry (in availability or in access) to manipulate decision processes. He’s looked into action advice provision, strategic information disclosure, election manipulation, and, more recently, poisoning Reinforcement Learning.

Seminar Moderator:

Koon-Ho Alan Tsang – Assistant Professor at the School of Computer Science at 杏吧原创 University

Light refreshments will be provided.

Please RSVP below to help us prepare for the event.

The Prospect of Generative AI

Muhammad Abdul-Mageed, University of British Columbia (UBC)

In the evolving landscape of artificial intelligence, generative models are revolutionizing our interface with computational systems and reshaping societal paradigms. These transformative shifts emerge from the profound capacity of sophisticated algorithms to assimilate multimodal data, encompassing text, voice, and imagery. This talk sketches the core methodologies propelling this groundbreaking progress, drawing illustrative examples from speech and language processing and computer vision. It also traverses the outcomes of a host of transnational academic partnerships, highlighting impactful intersections of AI with archival sciences and cultural preservation, education, and health.

Muhammad Abdul-Mageed is an Associate Professor and Canada Research Chair in natural language processing and machine learning at the University of British Columbia (UBC) and a Visiting Associate Professor at MBZUAI. His research focuses on large language models, cross-modal socio-pragmatics, and deep representation learning. This program is driven by a goal to innovate more equitable, efficient, and interactive machines for improved human health, safer social networking, and reduced information overload. Dr. Abdul-Mageed has published more than 120 articles in peer-reviewed venues, and his group has won several international competitions. He is a founding steering member of UBC’s and , Director of the , Co-Director of the , and Co-Lead of the . Dr. Abdul-Mageed’s research has been supported by Amazon, AMD, CFI, Google, NSERC, and SSHRC.

Augmenting Human Intelligence: from deep learning to Generative AI

Emanuele Frontoni, University of Macerata, Italy.

In this speech, Emanuele Frontoni will discuss the latest developments in augmenting human intelligence through the use of deep learning and generative AI. The talk will explore how these technologies are transforming various industries and enabling humans to perform tasks that were previously impossible. Several projects implemented in the VRAI Lab will be presented as examples of this evolution. The speaker will delve into the applications of these technologies, such as image analysis, natural language processing, and predictive modeling. A special section will be dedicated to the ITrustAI researches on deep learning on middle-aged parchments. He will also address the ethical considerations surrounding the use of AI and the need for responsible development and deployment of these technologies.

Emanuele Frontoni is a Full Professor of Computer Science at the University of Macerata and the Co-Director of the VRAI Vision Robotics & Artificial Intelligence Lab.

His research interests include computer vision and artificial intelligence with applications in robotics, video analysis, human behavior analysis, extended reality, and digital humanities.

He is the author of over 230 international articles and collaborates with numerous national and international companies in technology transfer and innovation activities.

He has been Program Chair or General Chair of various international conferences and summer schools (e.g. IEEE / ASME MESA Mechatronic Embedded System & Applications 2016 and 2017, IEEE ECMR European Conference on Mobile Robotics 2017, BigDat 2020, DeepLearn 2021) and co-organizer of many international workshops (eg DeepRetail @ ICPR 2020, D2CH @ CVPR 2021, AI4DH @ ICIAP 2022).

He is also involved in several national and听international technology transfer projects in the fields of AI, Deep Learning, data interoperability, cloud-based technologies, and big multimedia data analysis, extended reality and digital humanities.

He is a member of the European Association for Artificial Intelligence, the European AI Alliance, and the International Association for Pattern Recognition. He served as expert for the EU Commission in the AI H2020 and Horizon Europe Calls and he is currently co-speaker of the European IPEI CIS (Important Project of Common European Interest – Cloud Infrastructure and Services) for the AI services of the next generation of European cloud 鈥� edge services.

Video Recording

Audio Version

Light refreshments will be provided!听

Explainable AI for Classification and Decision Systems
Leopoldo Bertossi, Skema Business School, Canada, Montreal

Explainable AI (XAI) has become an effervescent area of research in AI and Machine Learning (ML). It is interesting per se, but also touches several aspects of what is nowadays called Ethical AI. In this presentation we will introduce some key concepts and ideas related to score-based explanation methods in ML-systems for classification and decision making. We will also emphasize: (a) the need for reasoning with explanations, (b) the connection with other more traditional explanation methods in AI; and (c) the fact that XAI is at the very core of AI, as opposed to being a kind of meta-analysis of AI methods.

Leopoldo Bertossi is a Professor at the Skema Business School -a French private business school- and its R&D Lab for Business AI, Montreal, Canada (since July 2022).

Since 2019, he is a Professor Emeritus of the School of Computer Science, 杏吧原创 University (Ottawa, Canada), with Grad Supervision Status. Until August 2022 he was a Full Professor at the Faculty of Engineering and Sciences, “Universidad Adolfo Ibanez” (UAI, Santiago, Chile), where he was the Director of the PhD and MSc. Programs in Data Science. He is a Senior Fellow of the UAI. He is also a Senior Researcher at the “Millennium Institute for Foundational Research on Data” (IMFD, Chile).

His broad research interests are related to Data Science and Artificial Intelligence, with focus on explainable AI, causality, knowledge representation, data management, computational logic, ontologies, uncertainty management, and machine learning.

In the Spring of 2016, Statistics Canada initiated a pilot project aimed at understanding the potential of data crowdsourcing for statistical purposes. The project makes use of OpenStreetMap (OSM) as the open data platform and invites prospective contributors to volunteer georeferenced information on buildings. The pilot project focuses on the Ottawa-Gatineau region but other regions may be added to the project in the future. This seminar will outline some of the preliminary results from the pilot, the lessons learnt so far, as well as the next steps in the implementation of this project.

The project web page and a link to the OSM editor for the project can be found at:

ABOUT THE SPEAKER

Alessandro Alasia is Chief of the Data Exploration and Integration Lab (DEIL), at the Centre for Special Business Projects, Statistics Canada. He has been working full-time at Statistics Canada since 2007. Prior to that appointment, he led several research projects with the Agriculture Division (Statistics Canada) and the Rural and Cooperatives Secretariat (AAFC) as consultant and post-doctoral fellow. Since 2011, he has been delegate and then Vice-Chair of the Working Party on Territorial Indicators at the OECD. Alessandro also worked as consultant in agriculture and rural development research projects, mainly in Southern Africa, with international organizations and national cooperation agencies. He has taught at the graduate and undergraduate level at the University of Bologna (Italy), the Eduardo Mondlane University (Mozambique), and the International Comparative Rural Policies Studies program. Alessandro graduated in Economics from the University of Torino (Italy), earned a MSc from the School of Specialization in Agriculture Economics and Business of the Catholic University (Italy), and a PhD in Agricultural Economics with specialization in Rural Studies from the University of Guelph.

This seminar is free and open to all. Complimentary coffee, tea and light snacks will be provided beginning at 1:15 p.m. We hope you can join us!

Campus map:

Please note that photos or video may be taken at the event which may later be used in print and online media produced by the Institute for Data Science at 杏吧原创 University.听For any questions or concerns, please contact听Jena Lynde-Smith听(jena.lyndesmith@carleton.ca).