Lin Zhong

Date: Wednesday, October 16, 2024

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

Location:TASC 1 9204, Burnaby campus

Talk Title: How Classical Computer Scientists Can Help Advance Quantum Computing

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Abstract:  Quantum computing promises unprecedented computational power, yet in practice, quantum hardware remains highly error-prone due to the challenge of isolating quantum states from environmental interference. A primary method for building fault-tolerant quantum systems is through Quantum Error Correction (QEC) codes, which utilize many physical qubits to construct a more reliable logical qubit. During operation, certain qubits are measured, and these measurements are analyzed to determine the errors that have occurred—a classical process known as QEC decoding.

In this talk, we present our results in developing scalable QEC decoders for surface codes, a key class of QEC codes. We demonstrate that crucial QEC decoding algorithms can be parallelized to achieve decoding speeds faster than measurement at unprecedented scales—d=51 for Union Find decoders and d=33 for MWPM decoders. Our experience indicates that classical computer scientists can contribute to advancing quantum computing, even without a deep understanding of quantum mechanics, especially when help from quantum experts is available.

Biography: Lin Zhong is Joseph C. Tsai Professor of Computer Science with Yale University. He received his B.S and M.S. from Tsinghua University and Ph.D. from Princeton University. From 2005 to 2019, he was with Rice University. At Yale, he leads the Efficient Computing Lab to make computing, communication, and interfacing more efficient and effective. He and his students received the best paper awards from ACM MobileHCI, IEEE PerCom, ACM MobiSys (3), ACM ASPLOS and IEEE QCE. He is a recipient of the NSF CAREER Award, the Duncan Award from Rice University, the RockStar Award (2014) and Test of Time Award (2022) from ACM SIGMOBILE. He is a Fellow of IEEE and ACM. More information about his research can be found at http://www.yecl.org.

Maneesh Agrawala

Date: Thursday, September 5, 2024

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

Location:TASC 1 9204, Burnaby campus

Talk Title: Unpredictable Black Boxes are Terrible Interfaces

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Abstract:  Modern generative AI models are capable of producing surprisingly high-quality text, images, video and even program code. Yet, the models are black boxes, making it impossible for users to build a mental/conceptual model for how the AI works. Users have no way to predict how the black box transmutes input controls (e.g., natural language prompts) into the output text, images, video or code. Instead, users have to repeatedly create a prompt, apply the model to produce a result and then adjust the prompt and try again, until a suitable result is achieved. In this talk I’ll assert that such unpredictable black boxes are terrible interfaces and that they always will be until we can identify ways to explain how they work.  I’ll also argue that the ambiguity of natural language and a lack of shared semantics between AI models and human users are partly to blame. Finally I’ll suggest some approaches for improving the interfaces to the AI models.

Biography: Maneesh Agrawala is the Forest Baskett Professor of Computer Science and Director of the Brown Institute for Media Innovation at Stanford University. He works on computer graphics, human computer interaction and visualization. His focus is on investigating how cognitive design principles can be used to improve the effectiveness of audio/visual media. The goals of this work are to discover the design principles and then instantiate them in both interactive and automated design tools. Honors include an Okawa Foundation Research Grant (2006), an Alfred P. Sloan Foundation Fellowship (2007), an NSF CAREER Award (2007), a SIGGRAPH Significant New Researcher Award (2008), a MacArthur Foundation Fellowship (2009), an Allen Distinguished Investigator Award (2014) and induction into the SIGCHI Academy (2021). He was named an ACM Fellow in 2022. 

Scott Hudson

Date: Wednesday, April 10, 2024

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

Location:TASC 1 9204, Burnaby campus

Talk Title: The Future is Not What it Used to Be: Some Thoughts on Why the Fun Stuff in Technical Human-Computer Interaction is All Ahead of Us

Abstract:  In this talk I will consider how the future of technical Human-Computer Interaction is different than it used to be - what has changed, what has stayed the same, and mostly what should we do about it. Although it seems mundane, when we consider change in any sort of computing technology, we must consider "the elephant in the room" of Moore's law. I will present two quick thought experiments in this talk to try to convince you that you really don't understand the implications of Moore's law, that this really does matter, and that you should perhaps be thinking a little differently about your work as a result. (Spoiler alert: you are dramatically underestimating how much change in computing power is ahead of you, and probably under-utilizing it's potential for HCI advances.) Based on this, the core of my talk will consider what we might be missing in terms of how we go about our work, and talk about several exemplars of where a different view of a "new future" might lead in terms of specific research directions. With these exemplars as motivation, I will consider some more general thoughts about the methodologies we use in our work, and suggest a few ways we might consider thinking differently about how we go about our work.

Biography: Scott Hudson is a Professor of Human-Computer Interaction at Carnegie Mellon and previously held positions at the University of Arizona and Georgia Tech. He has published extensively in technical HCI. He recently received the ACM SIGCHI Lifetime Research Award. Previously he received the ACM SIGCHI Lifetime Service Award, was elected to the CHI Academy, and received the Allen Newell Award for Research Excellence at CMU. His research interests within HCI are wide ranging, but tend to focus on technical aspects of HCI. Much of his recent work has been considering advanced fabrication technologies such as new machines, processes, and materials for 3D printing, as well as computational knitting and weaving, and applications of mechanical meta-materials.

Elisa Bertino

Date: Thursday, March 14, 2024

Time: 11:30 AM -12:30 PM PST

Location:TASC 1 9204, Burnaby campus

Talk Title: Security of Cellular networks

Abstract: As the world moves to 5G cellular networks and next-generation is being envisioned , security is of paramount importance and new tools are needed to ensure it. In the talk, after discussing motivating trends in wireless communications, we present LTEInspector a model-based testing approach for cellular network protocols. LTEInspector combines a symbolic model checker and a cryptographic protocol verifier in the symbolic attacker model. Using it, we uncovered 10 new attacks along with 9 prior attacks, categorized into three abstract classes (i.e., security, user privacy, and disruption of service), in three procedures of 4G LTE. To ensure that the exposed attacks pose real threats and are indeed realizable in practice, 8 of the 10 new attacks have been validated and their accompanying adversarial assumptions have been tested in a real testbed. We then present results obtained by 5GReasoner, which extends the 5GReasoner to 5G protocols. We then overview on-going research projects.

Biography:Elisa Bertino is Samuel Conte professor of Computer Science at Purdue University. She serves as Director of the Purdue Cyberspace Security Lab (Cyber2Slab). Prior to joining Purdue, she was a professor and department head at the Department of Computer Science and Communication of the University of Milan. She has been a visiting researcher at the IBM Research Laboratory in San Jose (now Almaden), at Rutgers University, at Telcordia Technologies. She has also held visiting professor positions at the Singapore National University and the Singapore Management University.  Her recent research focuses on security and privacy of cellular networks and IoT systems, and on edge analytics for cybersecurity.  Elisa Bertino is a Fellow member of IEEE, ACM, and AAAS. She received the 2002 IEEE Computer Society Technical Achievement Award for “For outstanding contributions to database systems and database security and advanced data management systems”, the 2005 IEEE Computer Society Tsutomu Kanai Award for “Pioneering and innovative research contributions to secure distributed systems”, the 2019-2020 ACM Athena Lecturer Award, and the 2021 IEEE 2021 Innovation in Societal Infrastructure Award. She is currently serving as ACM Vice-President.

Martin Grohe

Date: Thursday, February 1, 2024

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

Location: TASC 1 9204, Burnaby campus

Talk Title: The Logic of Graph Neural Networks

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Abstract: Graph neural networks (GNNs) are deep learning models for graph data that play a key role in machine learning on graphs. A GNN describes a distributed algorithm carrying out local computations at the vertices of the input graph. Typically, the parameters governing this algorithm are acquired through data-driven learning processes. After introducing the basic model, in this talk I will focus on the expressiveness of GNNs: which functions on graphs or their vertices can be computed by GNNs? Understanding the expressivenness will help us understand the suitability of GNNs for various application tasks and guide our search for possible extensions. Surprisingly, the expressiveness of GNNs has a clean and precise characterisation in terms of logic and Boolean circuits, that is, computation models of classical (descriptive) complexity theory.

Biography: Martin Grohe is a Professor for Theoretical Computer Science at RWTH Aachen University. He received his PhD in Mathematics at Freiburg University in 1994 and then spent a year as a visiting scholar at Stanford and the University of California at Santa Cruz. Before joining the Department of Computer Science of RWTH Aachen in 2012, he held positions at the University of Illinois at Chicago, the University of Edinburgh, and the Humboldt University at Berlin. His research interest are in theoretical computer science interpreted broadly, including logic, algorithms and complexity, graph theory, theoretical aspects of machine learning, and database theory.