Group leader

Albert Wong started his Tenure Track position at University of Twente in 2020. His research focuses on finding routes to explore, and demonstrate, the operating principles in networks of interacting molecules under out-of-equilibrium conditions. He developed strategies for the de novo design of chemical reaction networks during his Ph. D. at Radboud University, with prof. dr. Wilhelm T. S. Huck. He then worked with prof. dr. George M. Whitesides as a Rubicon postdoctoral fellow at Harvard University, where he branched off into examining how simple but prebiotically relevant types of chemical reactions can self-assemble into complex networks. See my CV for further information. 


Éverton Fernandes da Cunha will join our team in October 2022: He obtained his M.Sc. in Computational Physics at the Institute of Physics of São Carlos, (IFSC), University of São Paulo, (USP), and has a special interest in Complex Networls. Éverton will formulate the complexity indicators underlying synthetic chemical reaction networks, and use them to translate CRNs into mathematical models.

Yanna Kraakman will join our team in September 2022: She obtained her M.Sc. in Mathematics (cum laude) at the University of Twente, and has a background in Game and Graph theory. "Graphs are in my opinion one of the most elegant structures to model relations between objects", and she will examine how to model chemical reactions with hypergraphs in her PhD. 

Hazal Koyuncu joined our lab s a Ph. D. student in April 2021: She obtained her M.Sc. degree in Chemical Engineering at Prague University of Chemistry and Technology in Czech Republic. Hazal's research focuses on the development reactive surfaces in confined, microfluidic, spaces. More specifically, she is developing methods that uses poly-L-lysine (i.e., charged polymers) to functionalize surfaces in microfluidic devices, which allows for so-called local chemical feedback (i.e., spatially-organized activation and inhibition feedback loops).

Dmitry Kryukov joined our lab as a Ph. D. student in September 2020. He graduated for his M.Sc. degree in Chemistry at the Moscow State University (MSU) in Russia. Dmitrii's research focuses on understanding how CRNs can respond to changes in gradients. His recently published work demonstrates that a simple three-component CRN, under out-of-equilibrium conditions, is already capable of history-dependent functions (i.e., essential property for neuromorphic computing).