The 杏吧原创 University Institute for Data Science (CUIDS) invites you to a virtual talk on Cardiac Electrophysiology: The Digital Frontier with Dr. Damian Redfearn, a leading expert in the study and treatment of cardiac arrhythmias.

Cardiac Electrophysiology began with the recording of the electrocardiograph and consisted of hypothesis and conjecture until the digital age. In this seminar, Dr. Redfearn will describe the rapid advances in understanding with the advent of sophisticated computing and digital signal processing techniques. Contemporary computing has advanced our understanding and practical applications for treatment of life-threatening arrhythmias, feature extraction and 3-Dimensional computer generated cardiac reconstructions for minimally invasive cardiac procedures have made treatment more accessible and successful. With a multi-billion dollar global market, efforts to improve on current understanding and therapy have accelerated in recent years. Barriers remain to understanding some of the most common rhythm problems such as atrial fibrillation where no mapping methodologies exist due to the highly complex nature of the signals. In this seminar we will explore the history, contemporary treatments and open problems in this exciting and largely 鈥榙igital鈥� subspeciality.

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杏吧原创 the speaker:

Dr. Redfearn holds the rank of full Professor at the University of Ottawa Heart Institute. His clinical appointment is within the Electrophysiology team, bringing a wealth of experience in diagnostics and complex ablation to this already well-established and exceptional group. His mission is to build capacity for more complex and advanced electrophysiology procedures for the people within the Ottawa region. In addition, his research program, which involves computer scientists and cardiologists, develops and implements novel signal processing techniques to advance the field with the aim of mapping rhythms like atrial fibrillation that lack effective mapping technology.

Dr Redfearn is a very experienced electrophysiologist with world-renowned expertise in the management of arrhythmia with procedural intervention. He pioneered new techniques for catheter ablation of atrial flutter, atrial fibrillation and ventricular tachycardia and continues a keen interest in advancing the field of interventional electrophysiology. He has developed and patented a mapping program for atrial fibrillation called TARLESS which he hopes to test as a clinical tool for patient care.

Dr Damian Redfearn was born and educated in the UK. He graduated from the University of Leicester Medical School and specialized in Cardiology with subspecialty training in cardiac electrophysiology at the Birmingham University Teaching Hospitals. He moved to Canada in 2004 to pursue research and clinical training within the field of cardiac electrophysiology. He was recruited to Queen鈥檚 University in Kingston, Ontario in 2006 and was appointed Director of the Heart Rhythm Service in 2007, and Chair of Cardiology in 2023. He was promoted to the rank of full Professor in 2017. He has a track record of training graduate students and clinical trainees, having supervised 24 graduate students and trained over 17 EP fellows. He holds several peer-reviewed research grants to investigate the mechanisms of atrial fibrillation and ventricular arrhythmia through advanced signal processing. He has authored over 300 peer-reviewed publications and holds 3 US patents on arrhythmia-based algorithms

Moderator: Prof. Matthew Holden, School of Computer Science

RSVP today to explore the cutting edge of digital cardiology and computational innovation on December 11th!

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Speaker: Rosie Lickorish, Research Software Engineer at IBM

From Mayflower to Machine Learning: Advancing Ocean Research with AI

In this talk, Rosie will explore the art of the possible for AI at sea, starting with the story of the Mayflower Autonomus Ship’s successful crossing of the Atlantic ocean, followed by an overview from IBM Research on the latest innovations into geospatial foundation models at sea, on land and in space.

Rosie is a Research Software Engineer at IBM, specializing in the development of next-generation tools and technologies designed to drastically accelerate solutions for today鈥檚 most urgent global challenges. Her technical focus involves leveraging geospatial data, AI models, and scalable cloud architectures, with a critical emphasis on applications for climate change mitigation and adaptation. A recognized innovator within the organization, Rosie is designated as a Senior Inventor at IBM and holds two high-value patents.

Her extensive project portfolio demonstrates a blend of scientific ownership and deep engineering execution. Rosie served as the Principal Investigator for the IBM-led science experiments onboard the groundbreaking Mayflower Autonomous Ship, where she drove the project from initial concept through technical development and final deployment. Her ongoing technical leadership includes developing a scalable platform for geospatial AI model inference and fine-tuning, alongside providing core contributions to the IBM granite geospatial ocean foundation model. Rosie is also the lead software engineer for TerraKit, an open-source library for curating AI-ready geospatial datasets.

After completing her BSc in Mathematics from the University of Edinburgh and MSc in Oceanography from the University of Southampton (awarded distinction), Rosie joined IBM’s graduate program as a software engineer. Recognizing the value of using technology to tackle real world problems, like those covered in her MSc, Rosie became an Emerging Technology Specialist, progressing on to be a Research Software Engineer within IBM Research where she works today.

Moderator: , School of Computer Science

This is an in-person event and will not be recorded.

Beyond Scaling: Visual Learning by Adaptation

There have been significant advances in computer vision in the past decade. Current computer vision systems usually learn a generic model. In order to handle the diversity of the visual world, the current approach is to scale up the model. Although scaling has been proven effective in the era of large language model, I argue that there are also other alternative approaches we should explore. In this talk, I will introduce some of our recent work on building robust computer vision systems via adaptation and continual learning. Instead of learning and deploying one generic model, our goal is to learn a model that can effectively and continuously adapt itself to different environments. I will present applications of this framework in several computer vision applications.

杏吧原创 the Speaker: Yang Wang is currently an associate professor in the Department of Computer Science and Software Engineering, Concordia University. Previously, he was a faculty member at the University of Manitoba. During 2020-2022, he worked as the Chief Scientist in Computer Vision at the Consumer Business Group, Huawei Canada. He did his PhD from Simon Fraser University, MSc from University of Alberta, and BEng from Harbin Institute of Technology. Before joining UManitoba, he worked as a NSERC postdoc at the University of Illinois at Urbana-Champaign. His research focuses on computer vision and machine learning.

Moderator: Prof. Yuhong Guo

This event will be in-person. Light refreshments will be served.

Putting Test-Time Adaptation to the Test: Updates for Robust Visual Recognition

When the data shifts between training and testing the accuracy of predictions can degrade. The problem is that the data changes, but our听systems remain the same. Does it have to stay this way? In this talk we will examine how visual recognition can adapt and generalize to new and different data during testing. We will cover natural shifts, such as image corruptions, to highlight opportunities to update models (by entropy minimization and parameter mixing) and inputs (by diffusion) when the data differs. Then we will take a critical look at adversarial attacks through our case study of test-time adaptive defenses. On this front more adaptation is still needed in practice for empirical robustness, but there is progress in theory for certified robustness with our adaptive extension of randomized smoothing. As a last step, we will discuss what is next for test-time updates.

听is an assistant professor at UBC in Vancouver, member of the Vector Institute, and senior research scientist at Google DeepMind. His research is on visual recognition, self-supervised learning without annotations, and robustness by adaptation. He earned his PhD at UC Berkeley advised by Prof. Trevor Darrell. He was the lead developer of the Caffe open-source deep learning framework from version 0.1 to 1.0. His research and service have received awards including the best paper honorable mention at CVPR’15 for fully convolutional networks and the Mark Everingham award at ICCV’17, the open-source award at MM’14, and the test-of-time award at MM’24 for Caffe. He is new to Canada and is excited to learn more about all of its provinces and seasons!

Moderator: Prof. Jim Green, Department of Systems and Computer Engineering

RSVP today for the CUIDS Distinguished Speaker Series.

State of the Climate 2024 and the Role of Nature-Based Climate Solutions

Join Prof. Damon Matthews as he provides an update on the state of global climate change, including anticipated future trajectories and the consequent implications for Canada and the world. He will speak about the potential role of nature-based climate solutions: efforts to leverage natural systems to store and sequester carbon while also contributing to other environmental and social goals. In this context, he will present some recent research showing that investing in natural systems has climate value, but that this value is of a different character than fossil fuel emissions reductions, raising questions about the appropriate use of carbon offsets as a contribution to climate mitigation goals.

is a Professor in the Department of Geography, Planning and Environment at Concordia University. Damon holds a PhD in climate science from the University of Victoria, and is a member of the College of New Scholars, Artists and Scientists of the Royal Society of Canada.听He has published more than 130 research papers on topics ranging from quantifying the remaining carbon budget to assessing equitable approaches to allocate emission allowances to individual countries.听He is internationally recognized for his work in policy-relevant climate science, as well as for initiatives such as the听 that use digital visualization and web-based technologies to motivate and accelerate climate action.听Damon is the Scientific Director of听, and directs the NSERC CREATE in听Leadership in Environmental and Digital Innovation for Sustainability (LEADS)听program, which aims to train graduate student researchers at the intersection of sustainability science and digital innovation.

Moderator: Tracey Lauriault – Associate Professor in the School of Journalism and Communication

RSVP today for the CUIDS Distinguished Speaker Series.

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.

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).