August 22, 2022

Fall Colloquia Features Leading AI Experts, Starts August 25

by Kap Stann in Announcement

Join the Digital Transformation Institute this fall at its weekly Colloquium Series on Digital Transformation as it hosts the nation’s top experts in artificial intelligence. From rising stars to venerable authorities, this fall’s lineup includes several top tech industry scientists and entrepreneurs as well as academic researchers from leading universities.

The colloquium series features weekly hourlong online talks via Zoom on how artificial intelligence, machine learning, and big data can lead to scientific breakthroughs with large-scale societal benefit. The fall series has two themes: Distributed and Federated Learning, and Adversarial Machine Learning. Google research scientist Brendan McMahan, who led the team that pioneered the concept of Federated Learning, is among industry leaders represented in the series.

“Artificial intelligence is rapidly evolving from an art to a science,” says Richard Y. Zhang, assistant professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, who organized the fall series with colleague Gauri Joshi, associate professor of Electrical and Computer Engineering at Carnegie Mellon University. “These weekly peer-to-peer talks by field-shaping experts provide an insider’s look into this paradigm shift, in an accessible format that is open to the public without charge,” added Zhang.

The series runs from August 25 through December 8, most Thursdays at 1 pm PT/3 pm CT (with breaks for holidays and academic schedules), all open to the public at no charge.

See complete talk and speaker information on the DTI fall series webpage. You can register now for the Zoom webinar series. All talks are recorded and posted on DTI’s YouTube channel at

Preview the fall 2022 speaker list below. DTI Fall 2022 Colloquium Series

August 25
The Many Facets of Robust Machine Learning: from Mathematical Guarantees to Real-world Shifts
Aditi Raghunathan, Assistant Professor of Computer Science, Carnegie Mellon University

September 1
Two Surprises When Optimization Meets Machine Learning
Suvrit Sra, Associate Professor of Electrical Engineering and Computer Science, Massachusetts Institute of Technology

September 8
Trustworthy Machine Learning: Robustness, Privacy, Generalization, and their Interconnections
Bo Li, Assistant Professor of Computer Science, University of Illinois at Urbana-Champaign

September 15
Federated Learning with Formal User-Level Differential Privacy Guarantees
Brendan McMahan, Research Scientist, Google

September 22
New Approaches to Detecting and Adapting to Domain Shifts in Machine Learning
Zico Kolter, Associate Professor of Computer Science, Carnegie Mellon University

October 6
Machine Learning at All Levels: A Pathway to “Autonomous” AI
Eric Xing, Professor of Machine Learning, Carnegie Mellon University

October 13
Adversarial Machine Learning from a Privacy Perspective
Tom Goldstein, Perotto Associate Professor of Computer Science, University of Maryland

October 20
Improving Communication for Differential Privacy: Insight from Human Behavior
Rachel Cummings, Assistant Professor of Industrial Engineering and Operations Research, Columbia University

October 27
AI Model Inspector: Towards Holistic Adversarial Robustness for Deep Learning
Pin-Yu Chen, Principal Research Staff Member, Trusted AI Group, IBM

November 3
Confidently Scaling Optimization
John Duchi, Associate Professor of Statistics and Electrical Engineering, Stanford University

November 10
Underspecified Foundation Models Considered Harmful
Nicholas Carlini, Research Scientist, Google Brain

November 17
Tackling Computational and Data Heterogeneity in Federated Learning
Gauri Joshi, Associate Professor of Electrical and Computer Engineering, Carnegie Mellon University

December 1
Improved Adversarial Attacks and Certified Defenses via Nonconvex Relaxations
Richard Y. Zhang, Assistant Professor of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign

December 8
Do Meta-learning and Federated Learning Find Good Representations?
Sewoong Oh, Associate Professor of Computer Science and Engineering, University of Washington, and Staff Research Scientist, Google


The Digital Transformation Institute is a research consortium jointly hosted by the University of California, Berkeley and the University of Illinois at Urbana-Champaign that is dedicated to accelerating the benefits of artificial intelligence for business, government, and society. The Institute engages the world’s leading scientists to conduct research and train practitioners in the Science of Digital Transformation, which operates at the intersection of artificial intelligence, machine learning, cloud computing, internet of things, big data analytics, organizational behavior, public policy, and ethics.