2024-2025 DISTINGUISHED LECTURE SERIES

Hanna Hajishirzi, University of Washington

OLMo: Accelerating the Science of Language Modeling

Bio:
Hanna Hajishirzi is the Torode Family Associate Professor in the Allen School of Computer Science and Engineering at the University of Washington and a Senior Director of NLP at AI2. Her current research delves into various domains within Natural Language Processing (NLP) and Artificial Intelligence (AI), with a particular emphasis on accelerating the science of language modeling, broadening their scope, and enhancing their applicability and usefulness for human lives. She has published over 140 scientific articles in prestigious journals and conferences across ML, AI, NLP, and Computer Vision. She is the recipient of numerous awards, including the Sloan Fellowship, NSF CAREER Award, Intel Rising Star Award, Allen Distinguished Investigator Award, Academic Achievement UIUC Alumni Award, and Innovator of the Year Award by GeekWire. The work from her lab has been nominated for or has received best paper awards at various conferences and has been featured in numerous magazines and newspapers.

Abstract:

Language models (LMs) have become ubiquitous in both AI research and commercial product offerings. As their commercial importance has surged, the most powerful models have become closed off, gated behind proprietary interfaces, with important details of their training data, architectures, and development undisclosed. Given the significance of these details in scientifically studying these models, including their biases and potential risks, I argue that it is essential for the research community to have access to powerful, truly open LMs. In this talk, I present our OLMo project aimed at building strong language models and making them fully accessible to researchers along with open-source code for data, training, and inference. I describe our efforts in building language modeling from scratch, expanding their scope to make them applicable and useful for real-world applications, and investigating a new generation of LMs that address fundamental challenges inherent in current models.

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Matt Blaze, Georgetown University

Making Elections More Trustworthy (and trusted)

Bio:
Matt Blaze, Ph.D., is a professor of law at Georgetown Law and a professor of computer science at Georgetown University. For more than 25 years, Blaze’s research and scholarship has focused on security and privacy in computing and communications systems, especially as we rely on insecure platforms such as the internet for increasingly critical applications. His work has focused particularly on the intersection of this technology with public policy issues. For example, in 2007, he led several of the teams that evaluated the security of computerized election systems from several vendors on behalf of the states of California and Ohio.

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Abstract:

From voter registration to tallying ballots to reporting results, technology - computers and software - plays a central role in almost every aspect of US elections. Information technology has become essential for managing the US's complex elections, and, when all goes well, provides great benefits in efficiency, accuracy, and usability. But computers and software are also notoriously (and fundamentally) unreliable and vulnerable to tampering, and the systems we use for voting and election management are no exception. In some ways, the integrity of election outcomes has become dependent on the integrity of technology that may not always work as intended. Can we trust election outcomes? Should we?

Fortunately, recent advances have found reliable methods for conducting high-integrity elections even with flawed (or malicious( technology. This talk will examine the technologies used in elections, the ways they can fail, and practical safeguards that mitigate risks they introduce.

Margaret Martonosi, Princeton University

Taking on the World's Challenges: The Role of Computer Systems and Architecture Research

Bio:
Margaret Martonosi is the H.T. Adams '35 Professor of Computer Science at Princeton University, where she has been on the faculty since 1994. In addition, while on leave from Princeton, Martonosi recently served a 4-year rotation leading the U.S. National Science Foundation’s Directorate for Computer and Information Science and Engineering. NSF is the primary source of federal research funding for computing. Martonosi’s role there was to lead budget and operational strategy in stewarding this funding for the community.

Martonosi is an elected member of the US National Academy of Engineering and the American Academy of Arts and Sciences. In 2021, she received computer architecture’s highest honor, the ACM/IEEE Eckert-Mauchly Award, for contributions to the design, modeling, and verification of power-efficient computer architecture. She is a Fellow of IEEE and ACM. Her papers have received numerous long-term impact awards in the SIGARCH, SIGMOBILE, and other communities. She received the 2023 ACM Frances E. Allen Award for Outstanding Mentoring, for her impacts on computer architecture and the broader computing community. Other notable awards include the 2018 IEEE Computer Society Technical Achievement Award, 2010 Princeton University Graduate Mentoring Award, and the 2019 ACM SIGARCH Alan D. Berenbaum Distinguished Service Award. Her work with others to co-found the ACM CARES movement was recognized by the Computing Research Association’s 2020 Distinguished Service Award.

Abstract:

Throughout human history, society has faced great opportunities and challenges, and has used its available technologies to navigate them. Today, many of the global opportunities and challenges we face call for the full engagement of the computer systems and architecture research community. Resiliently navigating climate trends will require computing techniques and systems to model the future, as well as innovative techniques to mitigate carbon footprint by employing telepresence, optimizing logistics, and more. Another grand challenge of our era is the ability for us as individuals and as groups to communicate with each other in a way that upholds accuracy, integrity, privacy, and trust. This talk will discuss how the different elements of the computer science ecosystem— academia, industry, professional organizations, and governments—can work together to meet these challenges. It will be a call to action on how we can best navigate the next decade and beyond to do so.

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Işıl Dillig,

Neurosymbolic Program Synthesis: Bridging Perception and Reasoning in Real-World Applications

Abstract:

Abstract:
Neurosymbolic Program Synthesis (NSP) integrates neural networks and symbolic reasoning to tackle complex tasks requiring both perception and logical reasoning. This talk provides an overview of the NSP framework and its applications in domains such as image editing, data extraction, and robot learning from demonstrations. We will delve into the key ideas behind NSP learning algorithms, focusing on the synergistic interplay between neural guidance and symbolic reasoning. Finally, we will discuss recent advances in ensuring the correctness of synthesized neurosymbolic programs, paving the way for robust and reliable AI systems.

Bio:
Isil Dillig is a Professor of Computer Science at The University of Texas at Austin, where she leads the UToPiA research group. Her primary research interests span programming languages, formal methods, program synthesis, and software verification. She earned her Bachelor of Science, Master of Science, and Ph.D. degrees in Computer Science from Stanford University. Dr. Dillig’s work has been recognized with honors such as the Sloan Research Fellowship and the NSF CAREER Award, as well as best paper awards at conferences including PLDI, POPL, OOPSLA, and ETAPS. She has served as Program Committee Chair for PLDI 2022 and CAV 2019 and contributed to program committees for many conferences in her field. Finally, her dedication to teaching has been recognized with multiple awards such as the Texas 10 and the College of Natural Sciences Teaching Excellence Award.