Assistant Professor
Electrical Engineering and Computer Science
University of Tennessee, Knoxville
I develop mathematical and AI methods for the quantitative study of structure and dynamics across scales. I ask what we can predict about each, and how they shape function. AI is both a method I develop and a complex system I study. I am extending this work toward inverse design.
I joined UTK in Fall 2025 and lead the DUAL research group. I studied at the University of Southern California (Ph.D., 2019–2025) and Zhejiang University (bachelor's, 2015–2019).
Forward ModelingandInverse Design:Quantifying structure and dynamics across scales, and guiding design.AI for ScienceandScience for AI:Understanding the world with AI, and understanding AI itself.
GeoWind2Plan predicts local 3D urban wind with a geometry-conditioned neural operator for energy-efficient UAV planning, with planned routes evaluated under high-fidelity CFD wind.
ScaleSplit-NO conditions local, full-resolution patch operators on a global coarse forecast for memory- and data-efficient 3D turbulence prediction.
NeuroMFA characterizes the multifractal organization of AI weight networks across training checkpoints.
MIHC combines multi-view hypergraphs and an information bottleneck for interpretable congestion prediction.
Multifractal spiking statistics connect neuronal dynamics to network structure and function.
ITHP combines a neuro-inspired processing hierarchy with information bottlenecks for multimodal learning.
CMWNO learns coupled solution operators through interacting kernels in multiwavelet space.
NMFA uses node-centered box growth to characterize multiscale network structure and compare its complexity and heterogeneity.
I teach Artificial Intelligence (COSC 423/523) and Introduction to Machine Learning (COSC 325) at UTK.
I encourage students to turn broad interests into precise questions and testable hypotheses.
