IOP Publishing and Fudan University convene experts to explore AI and Machine Learning’s impact on the Physical sciences

IOP Publishing and Fudan University are organising a one-day international workshop, AI-driven discoveries: Machine Learning for the Physical Sciences. The event brings together leading researchers from around the world to explore how machine learning and AI are transforming the physical sciences.  

The international workshop provides an opportunity for leading and early-career researchers to share their work and spark ideas. Distinguished experts in the field will deliver plenary talks including:  

• Prof. Kyle Cranmer, University of Wisconsin-Madison 

• Prof. Weinan E, Center for Machine Learning Research, Peking University

• Prof. Xingao Gong, Fudan University 

• Prof. Hideki Okawa, Institute of High Energy Physics, CAS 

• Prof. Dongling Deng, Tsinghua University

The event will be free online and will be streamed via Zoom and KouShare to ensure international accessibility. For those wishing to join in-person, attendance is available for a fee. 

Topics will include: 

• Machine learning for high-energy particle physics 

• Quantum machine learning 

• Machine learning and big data at large-scale facilities 

• Discovery of new materials and molecules 

Jian Shen, Head of Institute for Nanoelectronic devices and Quantum computing and Director of State Key Laboratory of Surface Physics at Fudan University, says: “We are honoured to bring together some of the world’s leading machine learning scientists to advance this rapidly evolving research area. Machine learning is revolutionising the way we approach scientific problems, and this event will highlight the pivotal role it plays in pushing the boundaries of research.”  

Tim Smith, Head of Portfolio Development at IOP Publishing says: “Co-organising this event with Fudan University presents a unique opportunity for researchers from around the world to learn from leaders in their field and discuss some of the latest developments in how AI and machine learning are helping to advance new discovery across the physical sciences. Aligned with our recently launched Machine Learning series of journals this workshop further exemplifies IOP Publishing's commitment to fostering global collaboration and driving new innovations through the scientific community we serve.”  

Join us for a one-day workshop on April 27, 2025, hosted at Fudan University in Shanghai.

Registration opens today (February 11) via the Zoom registration page.

For further details, visit our event website at ioppublishing.org/ai-driven-discoveries/

ENDS

About IOP Publishing  

IOP Publishing is a society-owned scientific publisher, delivering impact, recognition and value to the scientific community. Its purpose is to expand the world of physics, offering a portfolio of journals, ebooks, conference proceedings and science news resources globally.    

IOPP is a member of Purpose-Led Publishing, a coalition of society publishers who pledge to put purpose above profit.   

As a wholly owned subsidiary of the Institute of Physics, a not-for-profit society, IOP Publishing supports the Institute’s work to inspire people to develop their knowledge, understanding and enjoyment of physics. Visit ioppublishing.org to learn more. 

About Fudan University 

Fudan University, established in 1905 in Shanghai, China, is one of the country’s most prestigious and research-intensive institutions. Renowned for its rigorous academics and innovative research, it excels in fields such as natural sciences, engineering, and AI-driven technology etc. Fudan boasts a diverse and international community, with partnerships across global universities and institutions. It is home to state-of-the-art facilities, including national key laboratories and interdisciplinary research centres. As a leader in education and innovation, Fudan University is committed to fostering global talent, advancing knowledge, and addressing critical technological challenges, positioning itself as a key driver of China’s academic and technological progress. 

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