Scheduled Speakers:
Perspectives on Generative AI from leading academic and industrial researchers. Topics include foundation models, fine-tuning, LLMs, diffusion, vision, optimization and related applications. Hosted at UT Austin by IFML.
| Surbhi
Goel Computer and Information Science University of Pennsylvania |
Zico
Kolter Department of Computer Science Carnegie Mellon University |
| John
Duchi Statistics & Electrical Engineering Stanford University |
Mathews
Jacob Department of Electrical and Computer Engineering University of Iowa |
| Zaid
Harchaoui Department of Statistics, Allen School in Computer Science & Engineering University of Washington |
Daniel
Hsu Department of Computer Science Columbia University |
| Dylan
Foster Reinforcement Learning Group Microsoft Research |
Aditi
Raghunathan Department of Computer Science Carnegie Mellon University |
| Georgia
Gkioxari Computing and Mathematical Sciences Caltech |
Jon
Barron Senior Staff Research Scientist Google Research |
| Sitan
Chen John A. Paulson School of Engineering and Applied Sciences Harvard University |
Aryan
Mokhtari Department of Electrical & Computer Engineering UT Austin |
| Alex
Dimakis (IFML
Co-Director) Department of Electrical and Computer Engineering UT Austin |
Sujay
Sanghavi Department of Electrical and Computer Engineering UT Austin |
| Jon
Tamir Department of Electrical and Computer Engineering UT Austin |
Participating Organizations:
Organizers:
| Adam
Klivans (Director) Department of Computer Science, Machine Learning Lab UT Austin |
Alex
Dimakis (IFML Co-Director) Department of Electrical and Computer Engineering UT Austin |
| Sanjay
Shakkottai Department of Electrical and Computer Engineering UT Austin |
Sujay
Sanghavi Department of Electrical and Computer Engineering UT Austin |
| Jon
Tamir Department of Electrical and Computer Engineering UT Austin |
Venue & Registration
Mulva Auditorium, Engineering
Education & Research Center (EERC),
Austin, TX
Register to attend this in-person event. Space is limited; we will be updating this landing page with presentation topics from our guest speakers in the coming days and weeks.
RegisterWorkshop Program & Agenda
Our multi-day workshop will cover a variety of topics across generative AI research. Find our full interactive agenda with topic titles and abstracts below. Remember to register above to save your seat! We will continue to update the agenda in the coming weeks.
Breakfast and lunch will be provided as part of your registration.
Foundational Datasets
Foundational, open source datasets underpin research and development in machine learning and generative AI, providing benchmarks and training data for a wide range of applications, from computer vision to natural language understanding.
Diffusion
Diffusion methods are crucial for advancing generative AI, providing a pathway to create realistic and diverse multi-modal data while addressing training challenges and promoting fine-tuned control over the generated outputs.
Large Language Models
LLMs are essential in generative AI for their capacity to understand, generate, and adapt human language, leading to breakthroughs in a wide range of natural language understanding and generation tasks.
