Distinguished Lecture Series
2024-2025 Speakers
John Guttag
Professor, Computer Science and Electrical Engineering
Massachusetts Institute of Technology
Prompt-based Medical Image Processing
Tuesday, October 8, 2024
11 a.m.
Bahen Centre for Information Technology,BA 3200
Abstract:
Medical image analysis is central to many clinical goals (e.g., diagnosing, monitoring, and treating disease) and research aims (e.g., studying anatomical structure, function, and development). Historically, medical image processing has involved solving complex optimization problems. Machine learning offers the opportunity to develop accurate tools that are far more efficient. However, existing AI-based methods most often focus on building models that address specific image processing objectives while targeting specific regions of interest or diseases. This has led to the creation of many special-purpose models, posing a challenge to users who must navigate an expanding landscape of rigid and brittle tools, often failing to find one that meets their specific needs.
This talk will discuss our approach to building powerful and general ML-based models that can be used “out-of-the-box” by clinicians and researchers who have no machine learning expertise. A key challenge in building such a model is incorporating a way for users to specify exactly what medical imaging task they wish the model to perform. Consider, for example, a model that, given an MRI of a brain, is capable of segmenting any neuroanatomical structure, diagnosing a tumour, correcting for motion artifacts, etc. When a brain MRI is supplied as input to the model, how should the model to decide which task to perform? How should the model handle a task not seen in the training data? In response to this challenge, we have developed networks that exploit various prompting methods. In this talk we will discuss three approaches to prompting in medical image analysis—context-based, graphic-based, and language-based—together with the novel network architectures and training regimes that support them.
Bio:
John Guttag, a U of T graduate, is the Dugald C. Jackson Professor of Computer Science and Electrical Engineering at MIT. He leads MIT’s Computer Science and Artificial Intelligence Laboratory’s Clinical and Applied Machine Learning Group. The group develops and applies advanced machine learning and computer vision techniques to a variety of problems. Current research projects include medical imaging, language/image models, and robust deep learning. In addition to his academic activities, Prof. Guttag is CTO of Health at Scale Technologies. The company’s machine intelligence platforms are used to help manage care for millions of individuals.
Raquel Urtasun
Professor, Computer Science, University of Toronto
CEO and Founder of Waabi
Bringing generative AI to the physical world
Monday, October 21
6 p.m.
Schwartz Reisman Innovation Campus
Abstract:
Advances in AI over recent years have been nothing short of remarkable, but we've only scratched the surface. The next frontier for AI will see the technology move from the virtual world and into the physical world. Explore how generative AI is unlocking the future of robotics, starting with self-driving.
Bio:
Raquel Urtasun is Founder and CEO of Waabi, an AI company building the next generation of self-driving technology. Raquel is also a Full Professor in the Department of Computer Science at the University of Toronto and a co-founder of the Vector Institute for AI. Raquel earned her PhD from the Computer Science department at Ecole Polytechnique Fédérale de Lausanne (EPFL) in 2006 and did her postdoc at MIT and UC Berkeley. In 2024, she was named a fellow of The Royal Society of Canada for her contributions to computer science and was included on the CNBC Changemakers: Women Transforming Business list. In 2023, she was named one of the TIME100 Most Influential People in AI, made Business Insider’s AI 100 list of Top People in AI, and was awarded the Ontario Chamber of Commerce’s Emerging Tech CEO Award and the Order of Ontario, the highest civilian honour in the province.
Toniann Pitassi
Professor, Computer Science
Columbia University
Professor, Computer Science
University of Toronto
Proof Complexity: From Theoretical Roots to Fertile Forest
Thursday, November 21, 2024
11 a.m.
Bahen Centre for Information Technology, BA 3200
Abstract:
One common view of proofs is an excessively formal set of rules that one must figure out how to apply in order to prove or derive something that is obvious in the first place. In this talk I will present proof complexity from a personal perspective, and will argue that proof systems are natural, and essential for understanding algorithms and the limits of computation. I will provide examples showing that proof systems hide behind many fundamental problems in mathematics and computer science, and how the intrinsic difficulty of the underlying problem is closely mirrored by the inherent complexity of their associated proofs. We will then see how Cook and Reckhow’s seminal paper from 1979 gave rise to proof complexity as a discipline, and revealed the fundamental connection between proof complexity and the P versus NP problem. I will highlight some of the major discoveries that have been made, with a focus on new results and exciting connections that have been made with other areas in the last 10-20 years. These new connections have enabled breakthrough discoveries in several areas, including: algorithms for solving distributional learning problems, connections with total NP search problems (TFNP), approximation algorithms, circuit lower bounds, cryptography (secret sharing schemes), and error correcting codes.
This talk will be tutorial in style and is intended for people working in related areas who may not be familiar with proof complexity. Moreover, since proof complexity has its roots at the University of Toronto, this will also be an opportunity to celebrate 60 years of a truly outstanding department.
Bio:
Toniann Pitassi is the Jeffrey L. and Brenda Bleustein Professor of Computer Science at Columbia University, and also a professor of Computer Science at the University of Toronto where she is currently on leave. Pitassi received her bachelor’s degree from Penn State, and her PhD from the University of Toronto, where she was lucky to be supervised by Stephen Cook. She has previously held positions at UCSD, University of Pittsburgh, University of Arizona, and the Institute for Advanced Study. Pitassi’s primary research area is complexity theory and proof complexity, which aim to understand the limits of proofs and computation. More recently her research interests have expanded to include theoretical aspects of machine learning, privacy and fairness, and whatever her graduate students are interested in. Pitassi was an invited speaker at the International Congress of Mathematicians in Berlin in 1998. Her recent honours include the EATCS (European Association for Theoretical Computer Science) distinguished research award (2021), fellow of the ACM (2018), and member of the National Academy of Sciences (2022).
Philip Bernstein
Distinguished Scientist at Microsoft Research
Affiliate Professor at University of Washington
Database System Design for Cloud Computing
Thursday, December 12, 2024,
11 a.m.
Bahen Centre for Information Technology, BA 3200
Abstract:
Much of the research in data management involves redesigning system components to use cloud computing platforms. Cloud computing has many characteristics that differ from on-premise servers that were the target of past data management systems. For example, it traditionally disaggregates storage services from compute servers. The latest cloud platforms also disaggregate memory from compute servers, and they offer different types of auxiliary processors, such as GPUs and SmartNICs. They exhibit performance improvements, such as faster networking, but also performance degradations, such as higher storage latency. These changes demand reconsideration of past designs of data management components. In this talk, I’ll summarize these trends and show their impact on data management systems through research projects I’ve pursued in recent years, such as distributed transaction processing, remote caching, and storage offloading.
Bio:
Philip A. Bernstein is a Distinguished Scientist at Microsoft Research and Affiliate Professor at University of Washington. He was previously a product architect at Digital Equipment Corporation, a professor at Harvard University, and VP Software at Sequoia Systems. He has co-authored over 200 papers on database systems and two books on transaction processing. He is a Fellow of the ACM and AAAS, a winner of the E.F. Codd SIGMOD Innovations Award, and a member of the U.S. National Academy of Engineering. He received a B.S. from Cornell and MSc and PhD from University of Toronto.
Joanna McGrenere
Professor and Co-Head, Computer Science
University of British Columbia
Designing Personalized User Interfaces
Thursday, January 30, 2025,
11 a.m.
Bahen Centre for Information Technology, BA 3200
Abstract:
There is no such thing as an average user. Users bring their own individual needs, desires, and skills to their everyday use of interactive technologies. While many of today’s technologies – from desktop applications to mobile devices and apps – accommodate some degree of personalization, users are often left with the sense that these technologies were created for some mythical user who is different from themself. It seems intuitive that interfaces should be designed with thoughtful adaptation in mind so as to better accommodate individual differences. Yet, what seems intuitive is not necessarily straightforward. I will highlight some examples of our research in the area of personalization, including designing for older adults and people with impairments, touching on what we’ve learned about the strengths and limitations of personalization, and where promising future opportunities lie.
Bio:
Joanna McGrenere is a Professor and Co-Head of Computer Science at the University of British Columbia. Her research specializes in Human-Computer Interaction, with a focus on designing personalized user interfaces, developing interactive systems for diverse user populations, including older adults and people with impairments. Joanna’s research has strong ties to industry, having been an IBM Visiting Scientist and received research support from Microsoft, Samsung, Google, Autodesk, Mozilla, Nokia and Meta. Joanna’s research leadership is demonstrated through appointments such as her recent 5-year Inria International Research Chair and serving as Technical Program Chair for the ACM CHI conference.
Aaron Hertzmann
Principal Scientist at Adobe Research
Affiliate Professor at University of Washington
Can Computers Create Art?
Tuesday, April 1, 2025
6 p.m.
Schwartz Reisman Innovation Campus
Abstract:
Can AI algorithms make art, and be considered artists? Within the past decade, the growth of new neural network algorithms has enabled exciting new art forms with considerable public interest. These tools raise recurring questions about their status as creators and their effect on the arts. In this talk, I will discuss how these developments parallel the development of previous artistic technologies, like oil paint, photography, and traditional computer graphics, with many useful analogies between past and current developments. I argue that art is a social phenomenon, that “AI” algorithms will not have human-level intelligence in the foreseeable future, and thus it is extremely unlikely that we will ever consider algorithms to be artists. However they, like past art technologies, will change the way we make and understand art.
Bio:
Aaron Hertzmann is a Principal Scientist at Adobe Research, and Affiliate Faculty at University of Washington. He received a bachelor’s degree in Computer Science and Art at Rice University and a PhD degree in Computer Science from New York University. He was previously a Professor of Computer Science at the University of Toronto for ten years. He has published over 120 papers in computer graphics, several subfields of AI, and in the science of art. He is an IEEE Fellow, an ACM Fellow, and winner of the 2024 SIGGRAPH Computer Graphics Achievement Award.