Profiles

Juan Pablo Bernal Tamayo is a Ph.D. student Applied Mathematics and Computational Science (AMCS) at King Abdullah University of Science and Technology (KAUST), working in Professor Jesper Tegnér’s AI Biomedicine Lab research group. He develops dynamic models to infer gene regulatory interactions during cellular differentiation. He holds an M.S. in Applied Mathematics from the Mathmods - Erasmus Mundus program (University of L’Aquila and University of Hamburg) and dual bachelor’s degrees in Engineering Physics and Pure Mathematics from Universidad Nacional de Colombia, Medellín. During his doctoral studies, he has served as a teaching assistant for graduate courses in Numerical Optimization and Numerical Linear Algebra.

Juan develops and applies dynamical models to infer gene regulatory interactions underlying cellular differentiation. By integrating high-dimensional single-cell transcriptomics and RNA-velocity inference within a Hopfield network formalism, he derives cell-type–specific interaction parameters that capture both the stability and trajectory of cell states. He developed scHopfield, a Python package that fits continuous Hopfield model parameters to infer cell-type specific regulatory interactions. Beyond this core method, Juan investigates the decomposition of Hopfield dynamics, revealing distinct mechanistic contributions to system behavior; also he analyzes small regulatory motifs within the Hopfield framework to understand their approximation limits and motif-specific dynamics. These efforts bridge mechanistic modeling and data-driven inference to generate predictive insights into cell-fate decisions and pinpoint key regulators for experimental validation.

Umberto Zerbinati is a Stipendiary Lecturer in Applied Mathematics at Oriel College, University of Oxford, where he is also pursuing a D.Phil. in Mathematics. He completed his M.S. in Applied Mathematics at KAUST in 2022 and his B.S. in Mathematics from the University of Pavia in 2020.

Umberto's research interests are in Partial Differential Equations (PDE), Numerical Analysis and Scientific Computing. I focus on the derivation and analysis of models for fluids with ordering, such as liquid crystals, starting from a kinetic description.

Dr. Xiujuan Zhang is an Associate Professor at School of Engineering and Applied Sciences, Department of Materials Science and Engineering, Nanjing University and at the Nanjing National Laboratory of Microstructures. She received her Ph.D. degree in Applied Mathematics and Computational Sciences from King Abdullah University of Science and Technology (KAUST) in 2017. In 2018, she joined Nanjing University, starting as a postdoc. Now,  Her research focuses on acoustic metamaterials and topological physics. She has published over 30 papers in prestigious journals, including Nature, Nature Physics, Nature Communications, and Physical Review Letters. Four papers have been recognized as ESI Highly Cited Papers, with a total citation exceeding 2000.

Xiujuan Zhang's research interests include topological physics and artificial microstructured topological materials, and has published more than 30 articles as the first author in international journals, including Nature, Nature Physic, Nature Communications and Physical Review Letters. Her he doctoral studies focused on the effective dielectric theory of Yu Supang materials (including photonic/phonon crystals and grating/sound grid structures), the novel physical effects associated with the Dirac dispersion curve, and the study of wave losses in the materials of the superstructure.

Yang Liu is a Postdoctoral Fellow at the Stochastic Numerics Research Group (STOCHNUM) of Professor Raul F. Tempone at King Abdullah University of Science and Technology (KAUST). His primary research interests involve uncertainty quantification, Monte Carlo methods, and finite element methods.