Kourosh Darvish is a staff scientist and principal investigator at the Acceleration Consortium at the University of Toronto. Previously, he served as a postdoctoral researcher at the Computer Science and Robotics Institute of the University of Toronto (UofT) and was a member of the Vector Institute. Before joining UofT in 2022, he worked as a postdoctoral researcher at the Italian Institute of Technology (IIT). In 2019, he completed his PhD in Bioengineering and Robotics from the University of Genoa, Italy. He earned his B.Sc. and M.Sc. degrees in Aerospace Engineering from K.N. Toosi University of Technology and Sharif University of Technology (Tehran, Iran) in 2012 and 2014, respectively. His research focuses on robotics, reinforcement learning & control theory, computer vision, reasoning & planning, and shared autonomy.

Jiang Jun, currently a Chair Professor at the University of Science and Technology of China, has accumulated remarkable achievements throughout his academic journey. In 2011, He was honorably selected for the first batch of the Youth Program under the National Major Talent Project. Two years later, in 2013, he was appointed Chief Scientist of the Key Project of Chinese National Programs for Fundamental Research and Development (973 Program), receiving an exemplary evaluation upon its completion. In 2020, Professor Jiang was awarded funding from The National Science Fund for Distinguished Young Scholars Program, and the following year, he was named the core leader of the Youth Team of AI-Chemist at the Chinese Academy of Sciences, underscoring his exceptional leadership and influence in scientific research.

For many years, Professor Jiang has been deeply involved in theoretical and intelligent chemistry research, striving to integrate artificial intelligence and big data technologies to pioneer new methodologies in quantum chemistry. Building on this foundation, he successfully developed the world's first data intelligence-driven AI-Chemist platform, establishing a new paradigm for intelligent chemistry research and enhancing research efficiency by 2 to 5 orders of magnitude through the deep fusion of theory and practice, thereby revolutionizing the scientific community.

Beyond his research endeavors, Professor Jiang is also an avid advocate for the Alliance of AI Scientist Ecosystem. He led the establishment of the alliance and launched the AI-Chemist instruction set, operating system, and experimental template library. These initiatives have had a profound and widespread impact in academia, fostering cross-integration and innovative development in intelligent science and chemistry research.

In terms of academic output, Professor Jiang has authored over 240 papers in prestigious international journals like Nature Synthesis, Nature Chemistry, Nature Catalysis, and the Journal of the American Chemical Society (JACS). Additionally, he has secured over 50 patents in intelligent chemistry, robotics, and new materials, further testament to his outstanding contributions to scientific innovation.

Moreover, Professor Jiang is the founding editor-in-chief of AI Chemistry, Elsevier's leading journal in intelligence research, and his academic contributions have garnered widespread recognition within the field. He has been honored with prestigious awards such as Chinese Chemical Society Tang Ao-Chin Youth Award on Theoretical Chemistry, the Anhui Youth Science and Technology Award, and the Asian Distinguished Lectureship Award from the Chemical Society of Japan. These accolades not only affirm his personal achievements but also celebrate his exemplary contributions to scientific research.

Shijing Sun is an associate professor at the University of Cambridge, and an affiliate faculty at the University of Washington. Her research primarily focuses on autonomous materials design and collaborative intelligence specifically aimed at advancing energy technologies. Sun previously held the position of a senior research scientist at the Toyota Research Institute. During her time there, she dedicated her efforts to the development of AI-powered solutions that aimed to accelerate R&D in the fields of electric vehicle batteries and fuel cells. Prior to joining the industry, Sun worked as a research scientist in the Department of Mechanical Engineering at the Massachusetts Institute of Technology, leading the development of high-throughput synthesis and characterization methods for thin-film solar cells. She completed her academic studies at Trinity College, University of Cambridge, where she obtained her BA degree in natural sciences, as well as MSci and PhD degrees in materials science.

I am a Senior Research Fellow in the Department of Chemistry at University College London.  I completed my DPhil in Inorganic Chemistry at the University of Oxford in 2021, working with Prof. Andrew Goodwin on identifying structural analogues of complex magnetic phases. Following my DPhil, I joined the Department of Chemistry at Imperial College London as a postdoctoral researcher with Prof. Kim Jelfs, where I developed coarse-grained methods for predicting the crystalline phase behaviour of molecular materials, focusing on porous molecular materials. I later moved to the Department of Materials at Imperial as an Eric and Wendy Schmidt AI Postdoctoral Research Fellow. I am now a Leverhulme Early Career Fellow in the Department of Chemistry at University College London.

My research focuses on developing simple models to improve our understanding of the structural phase behaviour of materials. These simple models can create data quickly and at minimal computational cost, making it possible to generate large amounts of data. By manipulating the parameters of the models, we can determine how changing the chemical features of molecules affects their solid-state structure, informing design rules for targeted crystal structures.