Distinguished Lecture Series

Meet world-renowned researchers at lectures hosted by the School of Computing Science. These are open to students, researchers and those working in industry and education to share the latest leading-edge research. Admission is free of charge.

Contact is Yasutaka Furukawa (furukawa@sfu.ca).


Pat Hanrahan

Date: Thursday, May 18, 2023

Time: 11:30 AM - 12:30 PM PST

Location: TASC 1 9204, Burnaby campus

Talk Title: Shading Languages and the Emergence of Programmable Graphics Systems

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Abstract: A major challenge in using computer graphics for movies and games is to create a rendering system that can create realistic pictures of a virtual world.  The system must handle the variety and complexity of the shapes, materials, and lighting that combine to create what we see every day.  The images must also be free of artifacts, emulate cameras to create depth of field and motion blur, and compose seamlessly with photographs of live action.

Pixar's RenderMan was created for this purpose, and has been widely used in feature film production.  A key innovation in the system is to use a shading language to procedurally describe appearance.  Shading languages were subsequently extended to run in real-time on graphics processing units (GPUs), and now shading languages are widely used in game engines.  The final step was the realization that the GPU is a data-parallel computer, and the the shading language could be extended into a general-purpose data-parallel programming language.  This enabled a wide variety of applications in high performance computing, such as physical simulation and machine learning, to be run on GPUs.  Nowadays, GPUs are the fastest computers in the world. This talk will review the history of shading languages and GPUs, and discuss the broader implications for computing.

Biography: Pat Hanrahan is the Canon Professor of Computer Science and Electrical Engineering in the Computer Graphics Laboratory at Stanford University.  His research focuses on rendering algorithms, graphics systems, and visualization.  Hanrahan received a Ph.D. in biophysics from the University of Wisconsin-Madison in 1985.  As a founding employee at Pixar Animation Studios in the 1980s, Hanrahan led the design of the RenderMan Interface Specification and the RenderMan Shading Language.  In 1989, he joined the faculty of Princeton University.  In 1995, he moved to Stanford University.  More recently, Hanrahan served as a co-founder and CTO of Tableau Software.  He has received three Academy Awards for Science and Technology, the SIGGRAPH Computer Graphics Achievement Award, the SIGGRAPH Stephen A. Coons Award, and the IEEE Visualization Career Award.  He is a member of the National Academy of Engineering and the American Academy of Arts and Sciences.  In 2019, he received the ACM A. M. Turing Award.

Kevin Murphy

Date: Friday, February 03, 2023

Time: 11:30 AM - 12:30 PM PST

Location: TASC 1 9204, Burnaby campus

Talk Title: The four pillars of machine learning

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Abstract: I will present a unified perspective on the field of machine learning research, following the structure of my recent book, "Probabilistic Machine Learning: Advanced Topics" (https://probml.github.io/book2). In particular, I will discuss various models and algorithms for tackling the following four key tasks, which I call the "4 pillars of ML": prediction, control, discovery and generation. For each of these tasks, I will also briefly summarize a few of my own contributions, including methods for  robust prediction under distribution shift, statistically efficient online decision making, discovering hidden regimes in high-dimensional time series data and for generating high-resolution images.

Biography: Kevin was born in Ireland, but grew up in England. He got his BA from U. Cambridge, his MEng from U. Pennsylvania, and his PhD from UC Berkeley. He then did a postdoc at MIT, and was an associate professor of computer science and statistics at the University of British Columbia in Vancouver, Canada, from 2004 to 2012. After getting tenure, he went to Google in California on his sabbatical and then ended up staying. He currently runs a team of about 8 researchers inside of Google Brain; the team works on generative models, Bayesian inference, ML methods that go beyond the iid assumption, and various other topics. Kevin has published over 125 papers in refereed conferences and journals, as well 3 textbooks on machine learning published in 2012, 2022 and 2023 by MIT Press. (The 2012 book was awarded the DeGroot Prize for best book in the field of Statistical Science.) Kevin was also the (co) Editor-in-Chief of JMLR 2014--2017.

Mario Szegedy

Date: Thursday, December 15, 2022

Time: 2:00 PM - 3:00 PM PST

Location: SFU Big Data Hub, Room 1900, Burnaby campus

Zoom details for online participation: 


Meeting ID: 895 8817 4259
Password: 378277

Talk Title: Entanglement as a resource.

Abstract: In the main part of the talk, I discuss basic properties of quantum entanglement: How does quantum entanglement differ from classical random  correlation? Does it make sense to ask, when in the quantum  teleportation protocol the teleportation actually happens? We give an  explanation of quantum measurement using  heat analogy; We talk a little bit about controlling and Localizing  entanglement. In the very end of the talk I mention a recent result,  joint with Sergei Bravyi, Yash Sharma and Ronald de Wolf, entitled  "Generating k EPR-pairs from an n-party resource state".

Biography:  Mario Szegedy is a Hungarian-American computer scientist, professor of computer science at Rutgers University. He received his Ph.D. in computer science in 1989 under the supervision of Laszlo Babai from the University of Chicago. He held a Lady Davis Postdoctoral Fellowship at the Hebrew University, Jerusalem (1989–90), a postdoc at the University of Chicago, 1991–92, and a postdoc at Bell Laboratories (1992). He was awarded the Gödel Prize twice, in 2001 and 2005, for his work on computational complexity including probabilistically checkable proofs and on the space complexity of approximating the frequency moments in streamed data.


Magdalena Balazinska

Date: Thursday, November 24, 2022

Time: 11:30 AM - 12:30 PM PST

Location: TASC I 9204

Talk Title: Storing and Querying Video Data.

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Abstract: The proliferation of inexpensive high-quality cameras coupled with recent advances in machine learning and computer vision have enabled new applications on video data. This in turn has renewed interest in video data management systems (VDMSs). In this talk, we explore how to build a modern data management system for video data. We focus, in particular, on the storage manager and present several techniques to store video data in a way that accelerates queries over that data. We then move up the stack and discuss different types of data models that can be exposed to applications. Finally, we discuss how it's possible to support users in expressing queries to find events of interest in a video database.

Biography: Magdalena Balazinska is Professor, Bill & Melinda Gates Chair, and Director of the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Magdalena's research interests are in the field of database management systems. Her current research focuses on data management for data science, big data systems, cloud computing, and image and video analytics. Prior to her leadership of the Allen School, Magdalena was the Director of the eScience Institute, the Associate Vice Provost for Data Science, and the Director of the Advanced Data Science PhD Option. She also served as Co-Editor-in-Chief for Volume 13 of the Proceedings of the Very Large Data Bases Endowment (PVLDB) journal and as PC co-chair for the corresponding VLDB'20 conference. Magdalena is an ACM Fellow. She holds a Ph.D. from the Massachusetts Institute of Technology (2006). Shortly after her arrival at the University of Washington, she was named a Microsoft Research New Faculty Fellow (2007). Magdalena received the inaugural VLDB Women in Database Research Award (2016) for her work on scalable distributed data systems. She also received an ACM SIGMOD Test-of-Time Award (2017) for her work on fault-tolerant distributed stream processing and a 10-year most influential paper award (2010) from her earlier work on reengineering software clones.

Steven Feiner

Date: Thursday, October 27, 2022

Time: 11:00 AM - 12:00 PM PST

Location: TASC1 9204

Talk Title: Cueing Action in Augmented Reality and Virtual Reality

Abstract: For over fifty years, researchers have investigated how augmented reality (AR) and virtual reality (VR) can help people perform tasks, training them in advance or assisting them on the fly. I will describe work our lab has done in this domain, addressing collaboration between a remote expert and a local technician, as well as system-provided assistance of an individual user. In these tasks, users are given information about the current step (cues), either immediately before or while doing it. Many tasks, in the real world and in AR and VR, also use precues about what to do after the current step. We have been exploring how precueing multiple steps in AR and VR can influence performance, for better or for worse, in ordered sequential tasks ranging from simple path following to translating and rotating physical objects, and in unordered bimanual tasks with potentially concurrent actions. I will present some of our results on the effectiveness of different numbers and types of cues and precues and the visualizations through which they are communicated. And I will conclude with some thoughts about future directions.

Biography: Steven Feiner is a Professor of Computer Science at Columbia University, where he directs the Computer Graphics and User Interfaces Lab. His lab has been conducting AR and VR research for over 25 years, designing and evaluating novel 3D interaction and visualization techniques, creating the first outdoor mobile AR system using a see-through head-worn display and GPS, and pioneering experimental applications of AR and VR to a wide range of fields. Steve is a Fellow of the ACM and the IEEE and a member of the CHI Academy and the IEEE VR Academy. He is the recipient of the ACM SIGCHI Lifetime Research Award, the IEEE ISMAR Career Impact Award, and the IEEE VGTC Virtual Reality Career Award. Together with his students, he has won the IEEE ISMAR Impact Paper Award, the ISWC Early Innovator Award, and the ACM UIST Lasting Impact Award. Steve has served as general chair or program chair for over a dozen ACM and IEEE conferences and is coauthor of two editions of Computer Graphics: Principles and Practice.

Onur Mutlu

Date: Thursday, September 22, 2022

Time: 11:00 PM - 12:00 PM

Location: Big Data Hub Presentation Studio (ASB 10900)

Talk Title: Memory-Centric Computing

Abstract: Computing is bottlenecked by data. Large amounts of application data overwhelm storage capability, communication capability, and computation capability of the modern machines we design today. As a result, many key applications' performance, efficiency and scalability are bottlenecked by data movement. In this lecture, we describe three major shortcomings of modern architectures in terms of 1) dealing with data, 2) taking advantage of the vast amounts of data, and 3) exploiting different semantic properties of application data. We argue that an intelligent architecture should be designed to handle data well. We show that handling data well requires designing architectures
based on three key principles: 1) data-centric, 2) data-driven, 3) data-aware. We give several examples for how to exploit each of these principles to design a much more efficient and high performance computing system. We especially discuss recent research that aims to fundamentally reduce memory latency and energy, and practically enable
computation close to data, with at least two promising novel directions: 1) processing using memory, which exploits analog operational properties of memory chips to perform massively-parallel operations in memory, with low-cost changes, 2) processing near memory, which integrates sophisticated additional processing capability in memory controllers, the logic layer of 3D-stacked memory technologies, or memory chips to enable high memory bandwidth and low memory latency to near-memory logic. We show both types of architectures can enable orders of magnitude improvements in performance and energy consumption of many important workloads, such as graph analytics, database systems, machine learning, video processing. We discuss how to enable adoption of such fundamentally more intelligent architectures, which we believe are key to efficiency, performance, and sustainability. We conclude with some guiding principles for future computing architecture and system designs.

A short accompanying paper, which appeared in DATE 2021, can be found here and serves as recommended reading: https://people.inf.ethz.ch/omutlu/pub/intelligent-architectures-for-intelligent-computingsystems-invited_paper_DATE21.pdf

A longer overview & survey of modern memory-centric computing can be found here and also serves as recommended reading: "A Modern Primer on Processing in Memory" https://people.inf.ethz.ch/omutlu/pub/ModernPrimerOnPIM_springer-emerging-computing-bookchapter21.pdf

Biography: Onur Mutlu is a Professor of Computer Science at ETH Zurich. He is also a faculty member at Carnegie Mellon University, where he previously held the Strecker Early Career Professorship. His current broader research interests are in computer architecture, systems, hardware security, and bioinformatics. A variety of techniques he, along with his group and collaborators, has invented over the years have influenced industry and have been employed in commercial microprocessors and memory/storage systems. He obtained his PhD and MS in ECE from the University of Texas at Austin and BS degrees in Computer Engineering and Psychology from the University of Michigan, Ann Arbor. He started the Computer Architecture Group at Microsoft Research (2006-2009), and held various product and research positions at Intel Corporation, Advanced Micro Devices, VMware, and Google. He received the Intel Outstanding Researcher Award, NVMW Persistent Impact Prize, IEEE High Performance Computer Architecture Test of Time Award, the IEEE Computer Society Edward J. McCluskey Technical Achievement Award, ACM SIGARCH Maurice Wilkes Award, the inaugural IEEE Computer Society Young Computer Architect Award, the inaugural Intel Early Career Faculty Award, US National Science Foundation CAREER Award, Carnegie Mellon University Ladd Research Award, faculty partnership awards from various companies, and a healthy number of best paper or "Top Pick" paper recognitions at various computer systems, architecture, and security venues. He is an ACM Fellow "for contributions to computer architecture research, especially in memory systems", IEEE Fellow for "contributions to computer architecture research and practice", and an elected member of the Academy of Europe (Academia Europaea). His computer architecture and digital logic design course lectures and materials are freely available on YouTube (https://www.youtube.com/OnurMutluLectures), and his research group makes a wide variety of software and hardware artifacts freely available online (https://safari.ethz.ch/). For more information, please see his webpage at https://people.inf.ethz.ch/omutlu/.