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.

To take full advantage of this exciting opportunity, we are arranging a series of briefing presentations by IBM Education Lead, Dennis Buttera. The first such event is scheduled for Thursday, June 10th from 9:30 a.m. to 11:00 a.m. Eastern Time (US and Canada). This interactive presentation will detail what resources we will have available.

To receive the Zoom details for this event, please email: cuids@carleton.ca