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KAUST-Tsinghua-Industry

Workshop on
Advances in Artificial Intelligence
November 23 to 26,
2019

About

Advances in Artificial Intelligence

How many Days

The Workshop will be composed of three technical presentation days plus a reception on campus the evening (Saturday) before.


No publications are planned. A KAUST-hosted web archive will be developed. Workshop talks do not have to be of publishable originality, but should target a broad audience within AI/ML

Themes

- AI/ML in scientific discovery

- Advancing the mathematical foundations of AI/ML

- How commercial AI/ML can aid scientific; how scientific can aid commercial

- Applying AI/ML to “edge” data

- High performance computing in AI/ML

AI workshop Motivation

Convergence of modes of investigation

AI and ML are commoditized in domains of human behavior, social science, marketing, and other realms that lack a "first principles" foundation. For fields that have such reference principles, such as conservation laws in physics, where there are traditional modes of scientific discovery through simulation, experiment, and theory, how can AI/ML bring additional inferential power and how can the four modalities work together better in situ?

Improving performance

AI and ML have largely been developed in domains where high performance is not at a premium and the main accomplishments were the refinement of the techniques. However real-time applications and data sizes that overwhelm traditional storage systems and slow down traditional algorithms are now upon us, especially from the scientific domain. How can HPC tools and techniques be leveraged to advance capabilities?

Improving rigor

AI and ML techniques have been likened by Peter Warden of Google's TensorFlow team to "banging on the side of the TV set until it works." Their success has been amazing and profitable, but little of the mathematical rigor that accompanies other modes of discovery has been developed so far. How can we bound error, quantify uncertainty, estimate sensitivities, and accelerate convergence? Also, what fundamental mathematical, statistical, operations research, and computer science methodologies can be better placed in the service of AI/ML applications?

Turning machine learning on the machine

High-performance computing, though a “science,” depends significantly upon “art” for the tuning of algorithmic and architectural parameters of the simulation, beyond the physical parameters of the simulation model, itself. When a complex application is run repeatedly for years, e.g., in weather forecasting or aircraft design, can accumulated performance data be exposed to AI and ML tools to tune the application?

Speakers

From KAUST and Tsinghua

Bo Zhang
Bo Zhang

Tsinghua University

Jianping Wu
Jianping Wu

Tsinghua University

Baining Guo
Baining Guo

Microsoft

Jinshui liu
Jinshui liu

Huawei

Shiqiang Yang
Shiqiang Yang

Peng Cheng Laboratory

Dejing Dou
Dejing Dou

Baidu

Jian Li
Jian Li

Theory

Maosong Sun
Maosong Sun

Natural Language Processing

Michael Zhang
Michael Zhang

Bioinformatics

Min Zhang
Min Zhang

Data Mining

Minlie Huang
Minlie Huang

Artificial Intelligence

Peng Cui
Peng Cui

Machine Learning

Xuegong Zhang
Xuegong Zhang

Bioinformatics

Bernard Ghanem
Bernard Ghanem

Computer Vision

Marco Canini
Marco Canini

Machine Learning

Panos Kalnis
Panos Kalnis

Algorithms

Peter Richtarik
Peter Richtarik

Machine Learning

Peter Wonka
Peter Wonka

Visual Computing

Xiangliang Zhang
Xiangliang Zhang

Machine Learning

Xin Gao
Xin Gao

Bioinformatics

Agenda

Agenda is subject to change


Open the Agenda in a new page

Committee

Xin Gao
Xin Gao

KAUST, PC Chair

Xiangliang Zhang
Xiangliang Zhang

KAUST

Jian Li
Jian Li

Tsinghua

Peter Richtarik
Peter Richtarik

KAUST

Shiqiang Yang
Shiqiang Yang

Tsinghua

Min Zhang
Min Zhang

Tsinghua

David Keyes
David Keyes

KAUST, ex officio

FAQ & Contact

Frequently Asked Questions

I would like to attend the conference. How do I register?

External attendees (non-KAUST) are encouraged to register.
However, they will have to cover their own costs and travel arrangements (accommodations can be provided). Also, out-of-Kingdom attendees will have to make their own arrangements to procure a valid visa;
we can sponsor visas only in exceptional cases.

Where is the conference located?

The plenary sessions of the KAUST-Tsinghua-Industry Workshop on Advances in Article Intelligence will take place on the KAUST campus in the Main Auditorium, Building 20.

I would like to present at the conference. How can I do that?

Presenting at the conference is by invitation only, except for a limited number of Ph.D. students whose poster session applications are accepted.

Is there any business center around the conference venue?

Yes, KAUST Library is open 24 hours and offers workstations and a quiet space for work and study. Wi-Fi is also accessible everywhere on the KAUST campus.

What should I wear in KAUST?

Smart casual attire is required at the conference. Women do not have to wear an abaya but should be dressed in conservative clothing (neck, arms and legs covered with loosely fitted clothing).

What are the recreation options available in KAUST?

Recreational activities on campus on arrival Saturday, meal on beach at sunset, meal at Yacht Club or Beacon, Angawi House tour and banquet, lab tours and cultural activities on campus. To find out more, visit this link

contacts

Xin Gao

xin.gao@kaust.edu.sa

KAUST, Thuwal, Jeddah, Saudi Arabia

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