David Balcells completed his PhD studies in 2006 in the Maseras group at ICIQ, on the topic of computational asymmetric catalysis. After a postdoc with Odile Eisenstein in the University of Montpellier, he became a Juan de la Cierva fellow in the Autonomous University of Barcelona, working on C-H activation in open-shell systems. In 2012, he moved to the University of Oslo and, after an MSCA postdoctoral fellowship, he started his independent career as a Principal Investigator at the Hylleraas Research Center of Excellence, where he was promoted to Research Professor in 2022. At the Hylleraas Centre, David is leading a research group advancing the application of machine learning to transition metal chemistry. He has received several awards, including the Ground-Breaking Research Grant from the Norwegian Research Council and the Young Researcher Award from the Spanish Royal Society of Chemistry.

Melodie Christensen serves as a Director in the Merck Process Research & Development Enabling Technologies Data-Rich Experimentation group. She earned her Master of Science in Chemistry from the American University in 2004 and began her professional journey as a process chemist at Schering-Plough Research Institute in 2005.
In 2010, Melodie joined Merck's Catalysis and Automation group, where she focused on high-throughput experimentation and laboratory automation. Her dedication to continuous learning and professional growth led her to pursue a PhD in Chemistry at the University of British Columbia, which she completed in 2022 under the guidance of Professor Jason Hein. Her doctoral research explored the integration of data science with custom laboratory automation, contributing to advancements in self-driving labs.
Melodie is deeply committed to fostering a collaborative and supportive environment for her team, encouraging them to explore and innovate within the realm of data-rich experimentation. Outside of her professional life, she enjoys yoga, container gardening, flower arranging, and culinary pursuits, which provide her with balance and inspiration.

Prof. Jun Jiang is a distinguished professor of physical chemistry at the University of Science and Technology of China (USTC), within the School of Chemistry and Materials Science. He earned a Ph.D. in Theoretical Chemistry from the Royal Institute of Technology, Sweden, in 2007, and another Ph.D. in Solid State Physics from the Shanghai Institute of Technical Physics, Chinese Academy of Science, in 2008, following a B.S. degree from WuHan University in 2000. He has published more than 150 papers in prestigious journals including Nature Synthesis, Nature Energy, J. Am. Chem. Soc., Angew. Chem. Int. Ed. Dr. Jiang is a recipient of the “National Science Fund for Distinguished Young Scholars in China”, and has won the “Young Theoretical Chemistry Investigator Award of Chinese Chemistry Society”, “Distinguished Lectureship Award of the Chemical Society of Japan 2020”.
Jiang’s research interests focus on the development and application of theoretical chemistry methods and machine learning techniques in chemistry science. By integrating robotic experiments, quantum chemistry simulations, and artificial intelligence guided predictions, his group has developed a data-driven robotic AI-chemist platform, targeting on a wide range of chemistry and material studies such as Photocatalysis, Biochemistry, Photochemistry, Molecular electronics and photonics. His recent works have demonstrated that generative AI combined with spectroscopic descriptors, has the potential to revolutionize chemical material design by overcoming the constraints of conventional atomic-coordinate-based descriptors, leading to a new way to combine generative AI and mobile robots for the intelligent discovery of new chemicals/materials. For more in-depth information about his research works, his personal homepage (http://staff.ustc.edu.cn/~jiangj1/) and dedicated research platforms might provide additional insights.

Haiping Lu is a Professor of Machine Learning at the University of Sheffield, UK, where he leads AI Research Engineering at the Centre for Machine Intelligence. He is also Director of the UK Open Multimodal AI Network (UKOMAIN), funded by the UK’s Engineering and Physical Sciences Research Council (EPSRC). His research focuses on translational multimodal AI, integrating diverse types of data to tackle challenges in healthcare and scientific discovery, with methodological interests in foundation models, generative AI, domain adaptation, and transfer learning.
His recent work spans brain and cardiac imaging, cancer diagnosis, protein design, and drug and materials discovery. He leads the development of PyKale, an open-source Python library for knowledge-aware machine learning. He serves as an Associate Editor for IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Cognitive and Developmental Systems, and has received awards from the Alan Turing Institute, Amazon, the Wellcome Trust, and the UK’s National Institute for Health and Care Research.

Dr Lauren T May (PhD 2007) is a Heart Foundation fellow (2018-2022) and Head of the Cardiac GPCR Biology laboratory at the Monash Institute of Pharmaceutical Sciences, where she leads a multidisciplinary program applying new and innovative GPCR drug discovery approaches for the development of safe and effective therapeutics. Dr May has secured sustained NHMRC project support (e.g. CIA: 2022 [Ideas], 2018, 2015).
Dr May has 65 publications, including original research articles (e.g. Nature (x2), Cell, Nat Commun., Proc. Natl. Acad. Sci. USA.) and invited reviews (e.g. Nature Chem Biol., Annu. Rev. Pharmacol. Toxicol.).
Dr May is an advocate for diversity in science, co-founder and chair of Her Research Matters and a member of the 2019 International Women’s Forum (IWFA) Emerging Leaders Cohort. As a supervisor, she aims to empower the careers of her PhD mentees who have gone on to win awards and secure prestigious fellowships in the field.

Felix is a tenure-track Assistant Professor of Digital Chemistry at the University of Wuppertal. Born and raised in Germany, Felix studied Chemistry at the University of Münster, where he graduated in 2017. After a research stay at the Massachusetts Institute of Technology (2016–2017, with Tim F. Jamison), Felix returned to Münster and obtained his PhD in Chemistry in the group of Frank Glorius. During that time, his research focused on systematic and computer-aided strategies for reaction development in homogeneous (photo-)catalysis. From 2021 to 2024, Felix was a Schmidt Futures “AI in Science” postdoctoral Fellow in the group of Alán Aspuru-Guzik at the University of Toronto, working on self-driving laboratories for chemistry and materials discovery. In 2024, Felix returned to Germany and assumed his current position as Assistant Professor of Digital Chemistry. 

Felix is an organic chemist at heart – and his research interests lie in the development of a digital toolbox for chemistry, and its application to the discovery of sustainable catalytic transformations. His research is inherently interdisciplinary, integrating established experimental techniques from organic chemistry with methods from data science, computational chemistry, and artificial intelligence.

Zhongyue John Yang is the SC Family Dean’s Faculty Fellow, Assistant Professor of Chemistry, Chemical and Biomolecular Engineering at Vanderbilt University. He graduated from the inaugural Chemistry Po-Ling program at Nankai University in 2013, earned his Ph.D. in Theoretical and Computational Chemistry with Ken Houk at UCLA in 2017, and undertook postdoctoral training with Heather Kulik in the Department of Chemical Engineering at MIT from 2018 to 2020. Since fall 2020, he has started his independent research group at Vanderbilt and has published 31 peer-reviewed papers as an independent investigator.
His group seeks to redefine protein engineering by anchoring it in molecular-level physical principles (Nat. Comput. Sci. 2025). They established Mutexa, a physics-augmented AI platform for predicting and designing beneficial protein variants (J. Chem. Theory Comput. 2023). Leveraging Mutexa, they established in silico tools for predicting the outcome of enzyme-catalyzed hydrolytic kinetic resolution (Chem. Sci. 2023), modifying enzymatic specificity (Chem. Catalysis 2025), designing cold adapted bidomain amylases (Angew. Chem. Int. Ed. 2025), predicting 3D structures of lasso peptides (Nat. Commun. 2025), and so on. His research is funded by U.S. National Science Foundation, National Institute of Health, and Rosetta Commons. He is a recipient of NIH MIRA Award in 2022, Robin Hochstrasser Young Investigator Award in 2023, and ACS OpenEye Junior Faculty Award in Computational Chemistry in 2024. He is a member of the Early Career Board for the Journal of Chemical Theory and Computation by the ACS Publications.