Rate My Professor David Bortz

Bortz's primary research develops and analyzes computational methods for weak-form scientific machine learning, including WSINDy and WENDy algorithms, which are noise-robust and efficient for system identification, governing equation discovery, coarse-graining, and reduced-order modeling. Applications span biological systems such as collective cell motion in wound healing, structured population dynamics, disease ecology and epidemiology, as well as atmospheric fluid models and computational plasma physics for fusion. He has received major grants including NIH-NIGMS MIRA R35GM149335 (2023-2028), NSF eMB DMS-2527532 (2025-2028), DOE ASCR MMICC DE-SC0023346 (CU PI, 2022-2027), and NSF MODULUS MCB-2054085 (2021-2026). Awards include University of Colorado Marinus Smith Award for distinguished mentoring and teaching (2018), Graduate School Outstanding Faculty Mentor Award (2019), and COVID-19 Resilience Award (2021). Key publications encompass 'Direct Estimation of Parameters in ODE Models Using WENDy: Weak-form Estimation of Nonlinear Dynamics' (Bull. Math. Biol., 2023), 'Learning Anisotropic Interaction Rules from Individual Trajectories in a Heterogeneous Cellular Population' (J. R. Soc. Interface, 2022), 'Learning mean-field equations from particle data using WSINDy' (Physica D, 2022), and 'Weak SINDy: Galerkin-Based Data-Driven Model Selection' (Multiscale Model. Simul., 2021). Bortz serves as Associate Editor for Mathematical Biosciences & Engineering since 2012 and has held various university and departmental committee roles.