Manu Jayadharan, PhD Applied mathematician and computational scientist Postdoctoral Fellow, Engineering Sciences & Applied Mathematics Northwestern University CV Google Scholar GitHub LinkedIn ORCID Email I work at the intersection of scientific machine learning, dynamical systems, and numerical methods, with a focus on developing algorithms to discover real-world models. I design numerically stable, data-driven methods for discovering and solving differential equations and implement them as open-source software. My background spans high-performance numerical solvers for PDEs, industry-scale quantitative modeling at Citigroup, and data-driven model discovery, with applications in biological, physical, and financial domains. At Northwestern I work with Dr. Niall Mangan, and I am an affiliated researcher at the NSF–Simons National Institute for Theory and Mathematics in Biology (NITMB) and the Trienens Institute for Sustainability and Energy. I hold a PhD in Mathematics from the University of Pittsburgh (2021) and worked as a Quantitative Analyst (AVP) at Citigroup from 2021 to 2023. What I work on Equation discovery from data Stable, interpretable algorithms for learning differential-algebraic equations from noisy measurements: SODAs (Proc. Royal Society A, 2026) and the open-source package DAE-FINDER. Shown: recovery of power-grid constraints on the IEEE-39 benchmark under measurement noise. Inverse problems & ill-conditioning Why dictionary-based model discovery fails, diagnosed through inverse-problem theory, and how to fix it. Shown: SVD diagnostics of a chemical-reaction-network candidate library at 15% noise. Multiphysics PDE solvers Fast finite-element solvers for coupled Poisson–Nernst–Planck electrochemical systems and domain decomposition for Biot poroelasticity. Shown: my MPI-parallel poroelastic flow simulation (BiotDD). Agentic AI for scientific computing Protocols, validation frameworks, and reusable agentic skill sets for using frontier AI agents in scientific computing, with a new graduate course at Northwestern in Fall 2026. More about my research → News 2026 Teaching a new project-based graduate course, Agentic AI for Scientific Computing, at Northwestern Applied Mathematics in Fall 2026. 2026 SODAs, our sparse-optimization algorithm for discovering differential-algebraic systems from data, published in Proceedings of the Royal Society A. 2026 Senior-author preprint on ill-conditioning in dictionary-based equation learning (arXiv:2603.11330) under review at SIAM J. Life Sciences. Aug 2025 Invited talk, From Data to Differential Equations, at the Indian Institute of Space Science and Technology. Jul 2025 Invited talk on SODAs at the SIAM Annual Meeting (AN25). 2025 Paper on multiscale mortar mixed FEM for the Biot system published in Computer Methods in Applied Mechanics and Engineering. Sep 2023 Joined Northwestern University as a Postdoctoral Fellow in Applied Mathematics. Selected publications M. Jayadharan, N. M. Mangan, et al., “SODAs: Sparse Optimization for Discovery of Differential-Algebraic Systems from Data,” Proceedings of the Royal Society A, 2026. M. Jayadharan, I. Yotov, “Multiscale mortar mixed finite element methods for the Biot system of poroelasticity,” Comput. Methods Appl. Mech. Engrg., 2025. M. Jayadharan, M. Kern, M. Vohralík, I. Yotov, “A space-time multiscale mortar mixed finite element method for parabolic equations,” SIAM J. Numer. Anal., 2023. Y. Feng, N. M. Mangan, M. Jayadharan†, “Ill-conditioning in dictionary-based dynamic-equation learning,” under review at SIAM J. Life Sciences, 2026. Full publication list →