Daniel Zuckerman

Daniel Zuckerman is a Senior Professor at Oregon Health & Science University, where he integrates biophysics, computational chemistry, and statistics to advance molecular and membrane biology. His research focuses on developing rigorous computational frameworks for analyzing biased and sparse trajectory data, including randomized iterative reweighting methods such as bRiteWeight and Regularized RiteWeight, which enhance equilibrium sampling without discretization error. He also applies physics-based computations to rectify AI-generated protein structure ensembles and employs Bayesian multi-dataset analyses to improve isothermal titration calorimetry precision. His recent work includes designing facilitated dissociation to time cytokine signaling, published in *Nature*, and contributions to the *Proceedings of the National Academy of Sciences* and the *Biophysical Journal*. His interdisciplinary approach bridges structural biology, neuroscience, and protein science, with international collaborations advancing statistical methods for biomolecular dynamics.