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About Badal
Badal Joshi is a Professor in the Department of Mathematics at California State University San Marcos, where he has served since 2013, advancing from Assistant Professor (2013–2018) to Associate Professor (2018–2023) and Professor (2023–present). Prior to joining CSUSM, he held positions as a Postdoctoral Fellow at the University of Minnesota (2012–2013) and Assistant Research Professor at Duke University (2009–2012). Joshi earned his Ph.D. in Mathematics from The Ohio State University in 2009 and an M.S. in Physics from the same institution in 2004. His academic career focuses on mathematical biology, employing tools from real analysis, dynamical systems, algebraic geometry, graph theory, and stochastic processes to analyze biochemical reaction networks and neuronal networks.
Joshi's research explores multistationarity in biochemical reaction networks, phase-delayed inhibition for decoding synchrony-encoded signals in the brain, and reciprocal inhibition as a stochastic switching mechanism. Key publications include 'Bifunctional enzyme action as a source of robustness in biochemical reaction networks: a novel hypergraph approach' (with Tung D. Nguyen, submitted 2025), 'Chemical mass-action systems as analog computers: implementing arithmetic computations at specified speed' (with David F. Anderson, Theoretical Computer Science, 2025), 'Bifunctional enzyme provides absolute concentration robustness in multisite covalent modification networks' (with Tung D. Nguyen, Journal of Mathematical Biology, 2024), 'Power-engine-load form for dynamic absolute concentration robustness' (with Gheorghe Craciun, SIAM Journal on Applied Mathematics, 2023), and 'Foundations of Static and Dynamic Absolute Concentration Robustness' (with Gheorghe Craciun, Journal of Mathematical Biology, 2022). Earlier works such as 'Simplifying the Jacobian criterion for precluding multistationarity in chemical reaction networks' (with Anne Shiu, SIAM Journal on Applied Mathematics, 2012) and studies on neuronal encoding and switching mechanisms further demonstrate his contributions to understanding biological dynamics through mathematical modeling.
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