Eduardo D. Sontag

Biosketch

Eduardo D. Sontag received his Licenciado in Mathematics at the University of Buenos Aires (1972) and a Ph.D. in Mathematics (1977) under Rudolf E. Kalman at the University of Florida. From 1977 to 2017, at Rutgers University he was Distinguished Professor of Mathematics and in the Graduate Faculty of Computer Science and Electrical and Computer Engineering, and in the Cancer Institute of NJ. He directed the undergraduate Biomathematics Interdisciplinary Major and the Center for Quantitative Biology, and was Graduate Director at the Institute for Quantitative Biomedicine. In January 2018, Dr. Sontag became a University Distinguished Professor in the Departments of Electrical and Computer Engineering and of BioEngineering at Northeastern University, and is affiliated with the Mathematics and Chemical Engineering departments. Since 2006, he has been a Research Affiliate at the Laboratory for Information and Decision Systems, MIT, and since 2018 a the Faculty Member in the Program in Therapeutic Science at Harvard Medical School. Sontag is a Fellow of IEEE, AMS, SIAM, and IFAC. Awards include: Reid Prize in Mathematics (2001), Hendrik W. Bode Lecture Prize (2002), IEEE Control Systems Field Award (2011), Richard E. Bellman Control Heritage Award (2022), IFAC Triennial Award on Nonlinear Control (2023), and from Rutgers the Board of Trustees Award for Excellence in Research (2002) and the 2005 Teacher/Scholar Award (2005). In 2024 he was elected to the American Academy of Arts and Sciences and in 2025 to the US National Academy of Sciences.

Research Interests

Dr. Sontag's interests are in control and dynamical systems theory, theoretical computer science, machine learning, cancer, immunology, and molecular, synthetic, and computational biology. He introduced novel techniques for the analysis of nonlinear systems based on commutative algebra and algebraic geometry, for observability and minimal realizations (1970s), the concepts of input-to-state stability of nonlinear systems (1989) and control-Lyapunov functions (1981), and a “universal formula'” for smooth stabilization (1989). He studied computational complexity for control systems and hybrid (piecewise linear) systems (1980s) and discontinuous stabilization (1990s) and the foundations of observation spaces, identifiability, and input/output equations for nonlinear behaviors (1980s-1990s). His long-standing interest in biology and neuroscience dates from his AI book (1972). He studied representability, identifiability, and computability by neural networks, sample complexity for learning dynamical systems, rates of approximation in function approximation, and neural-network feedback control. Since 2000, in systems and synthetic biology he has developed theory (monotone systems, stochastic models, chemical network theory) and applications (chemotaxis, cell cycle, immune recognition, chemotherapy-induced resistance, metastasis, epigenetics, translation, tumor heterogeneity, ecological, epidemiology, gene copy number compensation, rejection of disturbances, Boolean computation).