Distinguished Lecture Series
2026-2027 Speakers

Reflections on a Long Career and What's Next

Thursday, September 17, 2026
11 a.m.

Bahen Centre for Information Technology, BA 3200

Abstract:

Coming soon

Bio:

Allan Borodin is a distinguished computer scientist and University Professor Emeritus in the Department of Computer Science at the University of Toronto. He earned a Bachelor of Arts in Mathematics from Rutgers University in 1963, followed by a Master's degree from the Stevens Institute of Technology in 1966 while working part-time as a programmer at Bell Laboratories. He then pursued doctoral studies at Cornell University, completing his PhD in 1969 under the supervision of renowned computer scientist Juris Hartmanis.

Borodin joined the University of Toronto faculty in 1969 and was promoted to Full Professor in 1977. He served as Chair of the Department of Computer Science from 1980 to 1985 and was appointed University Professor in 2011, one of the institution’s highest academic distinctions.

Internationally recognized for his pioneering contributions to theoretical computer science, Borodin's research has advanced the understanding of computational complexity, online algorithms, resource tradeoffs, and algorithmic models. His achievements have been recognized through numerous prestigious honours. He was elected a Fellow of the Royal Society of Canada in 1991, received the CRM-Fields-PIMS Prize in 2008, became a Fellow of the American Association for the Advancement of Science in 2011, and was named a Fellow of the Association for Computing Machinery in 2014 for his influential contributions to theoretical computer science. In 2020, he was appointed an Officer of the Order of Canada in recognition of his outstanding impact on the field and on Canadian scholarship.

Determinations: Theory and Practice

Tuesday, September 29, 2026, 11 a.m.

Bahen Centre for Information Technology, BA 3200

Abstract:

Every time a language model writes a proof, a plan, or a program, it does something our usual theory of computation does not explain: it commits, token by token, to one answer among many admissible ones. This is not the familiar picture of computation as evaluating a function — given an input, produce the one correct output. It is the separate task of resolving a relation: choosing one of many admissible outcomes by making irrevocable commitments that progressively narrow the possibilities. This setting is by no means AI-specific; it is surprisingly widespread in modern computing. A distributed system in the cloud must settle on one consistent state from a set of admissible ones; a database system must choose an ordering of concurrent transactions; a market or auction must select one equilibrium. 
 
We call the structure of commitments a determination. This talk develops a theory of determinations around three questions, each with a clean answer: 
 
1. When are irrevocable commitments unavoidable? Exactly when a specification's outcomes are non-monotone over history. This Complete CALM Theorem generalizes a foundational result from databases and distributed systems, and yields a Complete CAP Theorem as a corollary. 
 
2. How many commitments are required? We introduce determination complexity: the number of irreducible sequential layers a task forces. It is a new axis, with autoregressive generation (think Chain-of-Thought) as a natural witness of orthogonality from computational complexity. 
 
3. Why would a particular outcome emerge, and how likely is it? These are determination provenance questions, answered by extending the theory of data provenance to the determination setting. The algebra is different from classical provenance — a filtration — suggesting a bridge to measure theory and probability. 
 
The theory pays off in practice. Hydro, an open-source Rust framework for distributed programming, instantiates it directly: the compiler rejects programs with unintended race conditions, and applies Complete CALM to report the consistency level of each output. Hydro also points toward a new kind of observability that can answer the "why" questions above. 
 
The talk is aimed at systems, theory, and AI researchers alike. 

Bio

Joseph M. Hellerstein is the Jim Gray Professor of the Graduate School at UC Berkeley, and a VP/Distinguished Scientist at Amazon Web Services where he leads the Hydro open source project. His research focuses broadly on data-oriented systems and the way they drive computing. This spans topics in database systems, distributed computing, programming languages and AI. Hellerstein is an ACM-SIGMOD Codd Award winner and a Fellow of the ACM. His industry work prior to AWS includes co-founding multiple startups and leading industrial research teams.