NITMB Affiliate Members

Dr. Changqing Cheng is an Associate Professor in the School of Systems Science and Industrial Engineering at Binghamton University. His research interest lies in computational engineering and network science for quality and resilience assurance in interdisciplinary innovations. He has been working on statistical learning and nonlinear dynamics analysis for simulation, optimal design and optimization of complex systems, with applications in healthcare systems, including epidemic modeling and chronic diseases diagnosis. He has also explored interdisciplinary applications in non-biology domains, including manufacturing and power networks. He is always inspired by new mathematical discoveries, particularly from the perspective of complexity, statistical learning, and nonlinear dynamics that are underlying most complex systems.

I’m a Professor of Bioengineering and Physics at the University of Illinois UrbanaChampaign. I codirect the Center for Artificial Intelligence and Modeling at the Carl R. Woese Institute for Genomic Biology and am affiliated with Argonne National Laboratory. My passion is blending theoretical statistical physics, computational biology and machine learning to tackle both big-picture and handson questions in microbial ecology and systems biology. I build simple but powerful “bottomup” models to tease apart the dynamics of complex systems—from whole microbial communities down to biomolecular networks. Here are a few highlights from my recent work: Microbial ecosystems: I model how communities stay diverse, how many stable “states” they can have, and how we might steer them. For example, I’ve used simulations of nutrient competition, crossfeeding and phagehost coevolution in gut microbiome models—insights that can improve health, manage environmental systems and help control outbreaks. Systems biology: I study how the burstiness and noise in generegulatory and proteininteraction networks shape cellular function and robustness. My models infer RNAvelocity, explore learning dynamics in dimerization networks, and quantify how nonspecific interactions affect cell behavior. Origin of life: Using physicsbased approaches, I’ve mapped out conditions under which simple chemical reactions (templateassisted ligation) can assemble long, structured polymers like ribozymes—shedding light on Darwinian processes in the RNA world. Deep learning: I apply LLMs and specialized models (e.g., ProBERTA, DRBERT) to predict gene fitness, epistasis and regulatory links, to analyze protein interactions, and to forecast microbial community behavior and drug responses. NITMB’s focus on marrying math and biology is a perfect fit for my interdisciplinary style. I’m excited to collaborate with the NITMB community to push those boundaries further—and to help train the next generation of computational biologists. https://maslov.bioengineering.illinois.edu
Akhenaton-Andrew Jones, Duke University
Alexandria Volkening, Purdue University
Andreas Buttenschoen, University of Massachusetts Amherst
Daniel Cooney, University of Illinois Urbana-Champaign
Guowei Wei, Michigan State University
Juliano Morimoto Borges, University of Aberdeen
Megan Morrison, Illinois Institute of Technology
Robert Eisenberg, Rush University; Illinois Institute of Technology; University of Illinois Chicago
Senay Yitbarek, University of North Carolina
Siwei Wang, Stony Brook University
Vudtiwat Ngampruetikorn, University of Sydney