Researchers & Contacts

I am fascinated with the idea that networks of chemical reactions operate in harmony to govern living system. I believe that we could create chemical reaction networks (CRNs) capable of functions akin artificial intelligence. To realize this ambition, I started my Tenure Track position at the University of Twente in 2020. Together with my team, we explore and demonstrate the operating principles in networks of interacting molecules under out-of-equilibrium conditions. 

See my CV for further information. 

[to be updated]

I've been a Ph.D. researcher at the University of Twente since October, where I focus on using hypergraphs to model chemical reactions. I’m originally from Brazil and I bring a background in complex science (chaos and complex networks), machine learning, and control theory to my research. My interests extend beyond my academic pursuits, and I enjoy learning new things and incorporating them into my life and work. I believe that this background provides me with a unique perspective to explore real complex systems, such as chemical reactions, neuroscience, social science, metascience, and many other systems with amazing emergent behaviors.

Hi! My name is Yanna, and I am an applied mathematician. Since September 2022, I am employed as a PhD candidate at the University of Twente, on the topic of Chemical Reaction Networks. My expertise lies in network science; I am intrigued by the beauty of networks and their effectiveness in uncovering structures. Within this project, I will therefore focus on establishing a connection between chemical reactions and mathematical networks and on discovering interesting properties of these networks. I am excited to contribute to this pioneering research.

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).