The AI Quorum

Launched in 2022 by MBZUAI, The AI Quorum is a winter series of gatherings designed to stimulate cutting-edge AI research with leading scientists and share an understanding of the discipline as a force for good. The series is strategic and high-level by design. The AI Quorum is focused on curiosity, collaboration, authenticity, and the pursuit of excellence – it is a coming together of the brightest minds to set the research agenda and to imagine both what AI could accomplish and how it might get there.

2023-2024 Workshops

The Future of HCI in the Era of AI : 22-23 February

There is significant debate around the future of Human Computer Interaction (HCI) in the emerging world of AI. Some say that the field of human-computer interaction has been rendered irrelevant by new kinds of AI systems, such as LLM chatbots, designed to directly interact with people. Many researchers in HCI, however, have shown the ways in which these systems may ignore people’s needs, trample on their rights, and demonstrate a very narrow vision of what it means for AI to be human-centered. We have gathered some of the most important voices in the field to discuss the future of HCI in this new era of AI. The attendees come from HCI fields as diverse as learning technologies, XR (virtual and enhanced reality), accessibility and health, multimodal technologies, body-centered devices, mobile devices, human-robot interaction, and search and information retrieval. Participants will debate what aspects of HCI deserve investment of time and other resources, to contribute and shape new AI initiatives, and what HCI education must look like in order to support the next generation of scholars and contributors in the area of HCI in AI and AI in HCI.

Chairs:

Speakers (to be confirmed):

  • Roberto Martinez-Maldonado
  • Christian Holz
  • Elisabeth André
  • Hannes Viljhalmsson
  • Takeo Igarashi
  • Mark Billinghurst
  • Juho Kim
  • Stephen Brewster
  • Yvonne Rogers
  • Mutlu Cukurova
  • Erika Poole
  • Dan Russell
  • Jaime Teevan
  • Ayman Shamma
  • Per Ola Kristensson
  • Shumin Zhai
  • Roderick Murray-Smith
  • Jeff Bigham
  • Pedro Lopes
  • Kia (Kristina) Höök
  • Amy Pavel
  • Jeff Nichols
  • Lauren Wilcox

View Speaker Profiles

MBZUAI Workshop on Statistics for the Future of AI: 8 – 10 January 2024 (Three days)

  • Mon 8: Presentations & discussion
  • Tues 9: Presentations & discussion
  • Weds 10: Planning and writing (closed session)

Location of event: MBZUAI campus, Visitors Center (Capacity: 60)

The statistics field has come under pressure from governments and society in general to make itself more relevant to today’s grand challenges. Members of the statistics community are looking for ways to demonstrate a pivot to relevance. The aim of the workshop is to fill that need by identifying challenges that can be addressed by the statistics community and setting out a detailed action plan.

Statistics is crucial for Artificial Intelligence as it contributes to both the theoretical foundations and applications of AI through theoretical advancements that drive the development of models and inferential methods, and statistical techniques that address data issues and support decision.

Day 1 (1/8/2024) Day 2 (1/9/2024)
09:00 AM – 09:15 AM Opening Remarks Recap of Day 1
09:15 AM – 09:30 AM Introduction to Day 1 Introduction to Day 2
09:30 AM – 11:00 AM Imaging
Chair: Tian Zheng
Keynote (30 mins)

– Hongtu Zhu
Lightning Talks

– Jian Kang
– Martin Lindquist
– Mohammad Yaqub
– Annie Qu
EHR/Crosscutting
Chair: Tian Zheng

Keynote (30 min)
– Tianxi Cai

Lightning Talks
– Xinzhou Guo
– Jinfeng Zhang
– Jianfeng Feng
– Lexin Li
11:00 AM – 11:15 AM Break Break
11:15 AM – 12:15 PM Computational Biology
Chair: Heping Zhang
Keynote (30 mins)
– Xihong Lin
Lightning Talks
– Hongzhe Li
– Wenyi Wang
– Hongyu Zhao
Smart Health
Chair: Peter Song
Keynote (30 min)
– Susan Murphy
Lightning Talks
– Haoda Fu
– Maxim Panov
– Chengchun Shi
12:15 PM – 01:30 PM Lunch Lunch
01:30 PM – 02:00 PM Introduction to breakouts Introduction to breakouts
02:00 PM – 03:30 PM Breakouts Breakouts
03:30 PM – 04:00 PM Break; Summary Break; Summary
04:00 PM – 05:00 PM Reports from breakouts Reports from breakouts
05:00 PM – 09:00 PM Transfer to hotel and dinner Transfer to hotel and dinner

Watch the Workshops

MBZUAI Workshop on Collaborative Learning : 9 – 11 December 2023 (Three days)

  • Sat 9: Presentations & discussion
  • Sun 10: Presentations & discussion
  • Mon 11: Round-up and cultural activity

Organizers:

  • Michael I. Jordan, UC Berkeley
  • Sai Praneeth Karimireddy, UC Berkeley
  • Yaodong Yu, UC Berkeley

Local committee:

  • Samuel Horvath, MBZUAI
  • Karthik Nandakumar, MBZUAI
  • Praneeth Vepakomma, MIT, MBZUAI

The pursuit of sustainable development has become a global priority as societies strive to ensure a harmonious coexistence between economic growth, social well-being, and environmental preservation. Achieving the ambitious sustainable development goals (SDGs) outlined by the United Nations requires a comprehensive understanding of complex systems and the availability of high-quality data to guide informed decision-making.

However, much of the data required for this purpose is sensitive and private, belonging to individuals or organizations that are understandably reluctant to share it due to concerns about privacy, security, and potential misuse. In fact, the UN “Global Pulse” initiative has identified lack of data access to be the key bottleneck for unleashing AI for achieving SDGs. This poses a significant hurdle to harnessing the full potential of data-driven approaches for sustainable development. Fortunately, collaborative learning may present a solution to this predicament.

In addition to addressing data privacy concerns, collaborative learning has the potential to embody a more inclusive approach to sustainable development. Traditional centralized approaches to data analysis often exclude local stakeholders from the decision-making process, limiting their ability to actively participate in shaping interventions that directly impact their communities. Collaborative learning, on the other hand, empowers these stakeholders by requiring them to actively contribute to the model training process.

The decentralized nature of collaborative learning can also lead to more scalable and efficient systems, particularly in scenarios where data sources are geographically dispersed or resource-constrained. By leveraging local computational resources, we can reduce the reliance on centralized infrastructure, making it a more viable option for initiatives in resource-limited settings.

Watch the Workshops

2022-2023 Workshops

Collaborative Learning From Theory to Practice: October 8-9, 2022

Michael I. Jordan, UC Berkeley

Sai Praneeth Karimireddy, UC Berkeley

CASL and AI Operating Systems Workshop: October 13-15, 2022

Research Session – Thursday, October 13, 10 a.m. to 4 p.m.

  1. AI-Guided Experiments for ML Systems, AutoML, and Science, Willie Neiswanger, Stanford University
  2. Standard Model and Algorithm for ML, Zhiting Hu, Assistant Professor at UC San Diego
  3. Accelerated Deep Learning via Efficient, Compressed and Managed Communication, Marco Canini, Associate Professor at KAUST
  4. Algorithms and Software for Text Classification, Chih-Jen Lin, Distinguished Professor at National Taiwan University
  5. Causal Representation Learning: Advances and Perspective, Kun Zhang, Associate Professor at MBZUAI and Director of the Center for Integrative Artificial Intelligence (CIAI)
  6. Systems and Applications of Multilevel Optimization, Sang Choe, Carnegie Mellon University
  7. Accelerating Drug Discovery via Deep Learning + Molecular Simulation, Maruan Al-Shedivat, Principal Research Scientist at Genesis Therapeutics
  8. AI Ecosystems: The Case of Natural Language Processing, Timothy Baldwin, Professor at MBZUAI
  9. Grounded Multi-modal Pretraining and Applications, Xiaodan Liang, Associate Professor at Sun Yat-sen University
  10. Fair and Accurate Federated Learning under Heterogeneous Targets, Samuel Horvath, Assistant Professor at MBZUAI
  11. On the Utility of Gradient Compression in Distributed Training Systems, Hongyi Wang, Carnegie Mellon University
  12. Scaling Deep Learning Training With Compiler Optimizations, Jinliang Wei, Google

Industry Session (open for public) – Friday, October 14, 10 a.m. to 4 p.m.

  1. Opening remarks: AI at Scale
  2. Growing a workforce of talents for AI at Scale, Co-Founder at Petuum Inc., Qirong Ho, Assistant Professor at MBZUAI
  3. Panel discussion: AI at Scale in the Middle East
  4. Improving Correctness, Performance, and Energy Efficiency of ML Applications, Shan Lu, Professor at University of Chicago
  5. History and Systems forTraining AI at Scale, Hao Zhang, UC Berkeley
  6. Fantastic Large Models andWhat We Should Do, Hector Liu, Head of Engineering at Petuum Inc.
  7. AI System for Drug Design, Prof. Le Song, Department Chair of Machine Learning, and Professor of Machine Learning

Eric Xing

Professor and President of MBZUAI, Founder, Chairman and Chief Scientist of Petuum Inc.

Qirong Ho

MBZUAI Assistant Professor of Machine Learning, Co-founder and CTO of Petuum Inc.

Natural Language Processing: December 2022

Tim Baldwin

Professor, Associate Provost for Academic and Student Affairs, Department Chair of NLP at MBZUAI

Seeking Low‑Dimensionality in Deep Neural Networks: January 3-6, 2023

Yi Ma

Professor at UC Berkeley

Robotics: January 9 -10, 2023

Ken Goldberg

Professor and William S. Floyd Jr. Distinguished Chair in Engineering at UC Berkeley

The inaugural MBZUAI Research Opportunities in Robotics Symposium (RORS) will be hosted in Abu Dhabi on Jan 9-10. The event will be chaired by Prof. Ken Goldberg from UC Berkeley) and bring together a dozen of the world’s leaders in the field of robotics and automation. The first aim of the event are to explain MBZUAI intention to launch research and academic programs in the robotics and automation space. We will also hear case studies from the region and around the world and conduct brainstorming sessions to begin planning MBZUAI research lines and teaching curriculums.

Big Model AI in Drug Design: February 20-21, 2023

Affiliated Professor at ETH Zurich, Founding Director at the Max Planck Institute for Intelligent Systems

Co-chaired by Prof. Eran Segal and Prof. Le Song at MBZUAI

The AI Quorum in images

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