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.

Distinguished Speaker Series

In 2023, the Government of Canada (GoC) issued numerous reports about Arctic security and sovereignty. This talk will open by reviewing some of those key outcomes augmented with scientific evidence and data. Canada being a maritime nation, the primary focus of this presentation is about the monitoring of the oceanic and underwater domains. Then, the presentation will provide an overview of the main challenges and extreme conditions facing the development and use of monitoring systems in underwater and Arctic conditions. The speaker鈥檚 recent research will help convey the impact of constrains, and technological solutions, related to power and energy, communication, sensing, and endurance, just to name a few. The talk will conclude by highlighting opportunities for future research. Through this talk, the audience will gain a high-level and better understanding of the R&D efforts that will help enable a sovereign Arctic.

Speaker

St茅phane Blouin is a Defence Scientist with Defence R&D Canada (DRDC) since 2010. He received degrees in mechanical (Laval University, 1992), electrical (Ecole Polytechnique, 1998), and chemical engineering (Queen’s University, 2003). He has acquired more than 12 years of industrial R&D experience through various positions in technology development and commercialization of automated solutions. He currently holds three Adjunct Professor positions at Canadian universities. He has (co-) authored more than 100 peer-reviewed scientific documents, holds eight inventions and patents, and is the Canadian technical lead on international projects. Dr. Blouin was the recipient of the Technology Investment Funds, two best-paper prizes, one MARLANT Appreciation Coin, and three Assistant Deputy Minister (DRDC) awards. His research interests include dynamic modeling, real-time monitoring, control, optimization and experimental research applied to underwater networks of autonomous systems.

Moderator: Michel Barbeau

Director, School of Computer Science & Interim Director, Institute for Data Science (DS & DSAAI) at 杏吧原创 University

Light refreshments will be provided.

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.

Large volumes of data generated from human interaction -颅 with software systems that support daily applications in areas such as commerce, law, and entertainment -颅 have given rise to what is commonly referred to as the 鈥淏ig Data Problem鈥�. Graph data is of growing importance in this context. Applications that rely on graph data include the semantic web (i.e., RDF), bioinformatics, finance and trade, and social networks among others. Graphs naturally model complicated structures, such as protein interaction networks, product 听purchasing, business transactions, relationships and interactions in social or computer networks, and web page connections.

The size and complexity of these graphs raise significant data management and data analysis challenges. In my talk, I will start by an overview of existing parallel systems for graph analysis, then show how dynamic graphs could be supported, and conclude with relevant use cases for graph data at Thomson Reuters.

杏吧原创 the Speaker

Khaled Ammar, Data Scientist at Thomson Reuters Innovation Labs

Khaled creates innovative applications for听legal, governance, regulation and tax use cases. Before joining , he was a research engineer at the Cognitive Computing Center, the R&D technical team for Thomson Reuters. Khaled鈥檚 research interests are motivated by real-颅life data processing and understanding, specifically data analytics over large and dynamic graphs. His research focuses on three aspects of this problem. The first focus is on identifying potential graph structures in business data, extracting these graphs, and proposing innovative solutions for different use cases. The second is studying dynamic graphs, which are graphs that change over time. He is developing data structures, algorithms, and systems for efficient analytics over multiple releases of a graph. The听third aspect is scalability to very large graphs, where he is investigating parallel and distributed systems.

Khaled is also working on his PhD at the David R. Cheriton School of Computer Science at the University of Waterloo. His thesis studies distributed systems for graph data processing with emphasis on large dynamic graphs. He has published multiple papers in VLDB, SIGMOD, and听BigData, spoken at industrial conferences such as Strata, and won academic awards such as OGS and IBM CAS fellowship.

SPACE IS LIMITED.

Please arrive 5-10 minutes early to check in at the registration desk, grab coffee or a snack听and get seated. See our Seminar FAQ for more information.

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 杏吧原创 University Institute for Data Science.听For any questions or concerns, please contact听Kathryn Elliott听(kathryn.elliott@carleton.ca).听

Join us at Ottawa’s beautiful Orange Art Gallery for a catered reception and exclusive keynote with Jim Sterne, Digital Analytics Association Co-Founder and Board Chair Emeritus, and internationally celebrated speaker, consultant, and author on big data and analytics.

Hosted by Napkyn Analytics, and proudly sponsored by Google, Pythian, the Digital Analytics Association, 杏吧原创 University’s Institute for Data Science, and the Telfer School of Management this is a discussion on the future of advanced analytics, machine learning, big data, and artificial intelligence’s use in marketing that you don’t want to miss.

Agenda

5:30-6:15pm – Cocktails, hors d鈥檕euvres, and networking (enjoy a selection of beverages, pulled pork tacos, jackfruit, hickory smoked brisket, St Albert cheeses, fine cured meats, and more from celebrated caterer Meatings Barbecue)

6:15-6:30pm – Opening presentation by Jim Cain, CEO, Napkyn Analytics – From Analytics To Customer Data Platforms: User-Centered Measurement Grows Up At The Enterprise

6:45-7:30pm – Keynote presentation by Jim Sterne – Intro To Artificial Intelligence For Marketing: What It鈥檚 Good At And What It鈥檚 Good For

7:30-9pm – Cocktails, hors d鈥檕euvres, and networking

Overview

Are you an analytics and marketing professional? Looking to break into the industry?

Marketers at today鈥檚 enterprises are using advanced digital analytics and other data-driven methods to inform and drive much of their decision making and capabilities – from brand and product promotion, through marketplace positioning and pricing optimization. Right now, user-centric data collection and analysis that is both cross-device and omni-channel is becoming a common practice among leading marketing teams, and the ability to drive personalized customer experiences is starting to move from blue sky to bottom line.

If you consider the rapid pace of evolution in the ways enterprises are using data, you don鈥檛 need to look far into the future to see what鈥檚 to come. Marketing teams continue to find new and innovative ways to use data to reach their customers, be more relevant in the moment, uncover new insights, and be more efficient in their investments, while the most sophisticated data analysis teams examine the potential of machine learning, artificial intelligence, and data science to leverage big data to gain a competitive edge.

Dr. Hari Koduvely will discuss how Bayesian Inference is useful for Machine Learning. Bayesian methods allow for more accurate predictions under uncertainty and with less amount of data. When applied to very complex models such as Deep Neural Networks, they would help in automatic tuning of hyperparameters and efficient compression of the models. Dr. Koduvely will present a brief introduction to Bayesian Inference and some examples of machine learning models where Bayesian methods are used very effectively.

杏吧原创 the Speaker

Dr. Hari Koduvely, Chief Data Scientist of in Ottawa.

Prior to moving to Canada, Dr. Koduvely worked as Senior/Principal Data Scientist for Amazon and Samsung R&D Institute in Bangalore, India. He has听a PhD in Statistical Physics from Tata Institute of Fundamental Research in Mumbai, India and has done post doctoral research from Weizmann Institute of Science, Israel and Georgia Institute of Technology, USA. He has published several papers in the area of Bayesian Machine Learning including a book titled Learning Bayesian Models with R.

SPACE IS LIMITED.

Please arrive 5-10 minutes early to check in at the registration desk, grab coffee or a snack听and get seated. See our Seminar FAQ for more information.

SEMINAR REGISTRATION IS FULL. DUE TO SEATING LIMITATIONS, WE ARE NO LONGER ACCEPTING REGISTRATIONS.

Using reinforcement learning, an agent can learn how to win by training against itself, rather than being told how to win by a programmer. In this seminar, we are going to cover the basics of reinforcement learning and how it was applied to create an expert player of a board game. We鈥檒l show you what we鈥檝e learnt, the challenges, solutions, and results of a year of research.

杏吧原创 the Speaker

Emiliano Conde 鈥� President & Founder of Decisive Artificial Intelligence Inc.

Emiliano is a natural programmer, and has been writing code since the age of 10. By the time he finished high school, he had found a job at IBM writing code in Assembly and C. After turning twenty, he led a group of friends to develop a Real Time Strategy (RTS) game in his native Argentina. Following a year of effort, the game 鈥淩egnum鈥� was published in 1995 and sold well in its local market.

Emigrating to Canada in 1999, Emiliano held various software engineering jobs, including working for the global bank HSBC as a software architect, before starting his own software company: jBilling.

Starting alone and with no capital, he grew jBilling to over 100 employees. Founding and growing a company required much more than good programming skills, including leadership, sales, marketing, finance, legal, contract negotiation, B2B pricing, and recruiting. Emiliano sold jBilling to San Francisco-based company AppDirect in 2012 and was a part of the AppDirect leadership team for 4 years.

In 2016, Emiliano started Decisive Artificial Intelligence. This new venture combines his passion for games, software engineering, and research in AI.

SPACE IS LIMITED.

Please arrive 5-10 minutes early to check in at the registration desk, grab coffee or a snack听and get seated. See our Seminar FAQ for more information.

Registration is now closed due to听high demand.

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 杏吧原创 University Institute for Data Science.听For any questions or concerns, please contact听Kathryn Elliott听(kathryn.elliott@carleton.ca).听