Denis Tsygankov
Areas of Research
Biomedical Informatics & Systems ModelingContact
UAW 1212Georgia TechBiography
Dr. Denis Tsygankov earned his B.S. and M.S. degrees in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology and his Ph.D. in Physics from the Georgia Institute of Technology. His early training focused on nonlinear dynamics and complex systems, including theoretical studies of synchronization and emergent collective behavior in coupled oscillatory systems. After completing his Ph.D., he held a research appointment at the University of Maryland, where his work transitioned into molecular biophysics, with a focus on energy transduction and force generation by molecular motors. He subsequently joined the University of North Carolina at Chapel Hill, first as a postdoctoral research associate and later as a research assistant professor in the Department of Pharmacology. During this period, his research centered on computational cell biology and quantitative image analysis, with an increasing emphasis on integrating modeling with experimental data. In 2015, Dr. Tsygankov joined the Wallace H. Coulter Department of Biomedical Engineering at Georgia Tech and Emory University, where his work expanded toward systems-level analysis of biological processes and disease. He is currently an Associate Professor and a recipient of the National Science Foundation CAREER Award (2020).
Education
- B.S. Moscow Institute of Physics and Technology
- M.S. Moscow Institute of Physics and Technology
- Ph.D. Georgia Institute of Technology
Affiliated Centers & Institutes
Research Interests
Dr. Tsygankov’s research focuses on developing and applying computational and quantitative approaches (including mathematical modeling, biophysical simulations, machine learning, and image analysis) to understand how complex physiological processes emerge across molecular, cellular, and tissue scales. His work addresses fundamental and disease-relevant problems in biomedical engineering, including vascular malformations such as cerebral cavernous malformation, embryonic development and neural crest cell migration, immune and cancer cell migration in physically confining environments, and biomarkers of aging derived from molecular and clinical data. A central theme of this research is identifying the physical and systems-level principles that govern cell behavior and how these principles become altered in disease. By integrating modeling with experimental and clinical collaborations, his research aims to generate predictive, mechanistic frameworks that improve our understanding of human physiology and support the development of new diagnostic and therapeutic strategies.
Teaching Interests
He is particularly interested in preparing students to use modeling and simulation as practical tools for understanding complex physiological systems and for addressing real-world problems in research, medicine, and biotechnology.
