Open Innovation Conference

Welcome to the exciting 1st Annual International Open Innovation Conference, hosted by VinUniversity in collaboration with the University of Oxford’s Saïd Business School, the Cornell University’s SC Johnson College of Business, and the Duke University’s Center for International Development. This event will be taken place at VinUniversity on December 6-7, 2024.

This year’s conference will explore the theme “Innovation for a Green Future,” featuring pioneering ideas and sustainable solutions. It convenes thought leaders, policymakers, leading scholars, scientists, and practitioners committed to a sustainable future across diverse sectors such as healthcare, education, hospitality, industry, agriculture, and infrastructure.

Attendees will have the opportunity to engage with cutting-edge innovations, network with prominent scientists, and contribute to shaping the future of technology and sustainability. We look forward to your participation in this stimulating and impactful gathering.

Recent Events

December 04, 2024

The 16th Asian Conference on Machine Learning

ACML 2024 provides a leading international forum for researchers in machine learning and related fields to share their new ideas, progress and achievements. It will take place in Hanoi, Vietnam from 5-8th December 2024. Read more HERE. Important Dates Conference Track Dates Date Event 03 July 2024 Submission deadline 14 August 2024 Reviews released to […]

10:00- 11:00 AM, July 26, 2024

[Research Seminar] Foundation of Mixture of Experts in Complex and Massive AI Models

About the Talk: Since the release of the original Transformer model, extensive efforts have been devoted to scaling up the model complexities to take advantage of massive datasets and advanced computing resources. To go beyond simply increasing the network depth and width, Sparse Mixture-of-experts (SMoE) has risen as an appealing solution for scaling Large Language […]

2:00 - 4:00 PM, July 15, 2024

[Research Seminar] Scalable Uncertainty: AI Needs to Know What it Doesn’t Know

About the Talk: Large models fuel impressive capabilities. However, they currently do not know what they do not know, which is critical to the important challenges of exploration and alignment. Knowing what is not known is essential, for example, to gathering informative data. In this talk, I will discuss the need for scalable uncertainty and […]