Varun Shankar
Assistant Professor, Kahlert School of Computing, University of Utah. Director of the Utah Digital Twin Lab.
I build efficient, scalable, and provably-accurate machine learning models for scientific applications. My work sits where scientific machine learning meets scientific computing and high performance computing: neural and kernel operators for surrogate modeling, learning architectures that preserve physical structure by construction, and high-order kernel and meshless methods for PDEs on complicated and moving geometries.
I joined the faculty on the tenure track in July 2023. Before that I was an Assistant Professor (Lecturer) in the Kahlert School of Computing (2018–2023), where I helped build and later co-direct the Master of Software Development program. I received my PhD in Computing (Scientific Computing) from the University of Utah and did a four-year postdoc in the Math Biology group in Utah's Department of Mathematics.
- Emailshankar@cs.utah.edu
- OfficeWEB 2891, 72 S. Central Campus Drive, Salt Lake City, UT 84112
- StudentsI am taking on new students, and current University of Utah students are especially welcome to reach out. See the lab's Join page.
Research areas
Details →Operator Learning & Surrogate Modeling
Fast, accurate surrogates that map inputs, geometries, and parameters directly to PDE solutions.
02Trustworthy, Property-Preserving SciML
Models that respect physics by construction, with guarantees on accuracy, stability, and correctness.
03Kernel & Meshless Numerical Methods
High-order RBF-FD and kernel methods for PDEs on irregular domains, surfaces, and moving manifolds.
04Digital Twins for Engineering & Biology
Coupling numerics, learning, and HPC for composites, fluids, robotics, and biomechanics.
Recent papers
All 43 →- Neural Operators for Design-Space Surrogate Modeling of Tendon-Actuated Continuum Robots Branden Frieden, James M Ferguson, Alan Kuntz, Varun Shankar IEEE International Conference on Robotics and Automation, ICRA, 2026
- Kernel Neural Operators (KNOs) for Scalable, Memory-efficient, Geometrically-flexible Operator Learning Matthew Lowery, John Turnage, Zachary Morrow, John D Jakeman, Akil Narayan, Shandian Zhe, Varun Shankar Transactions on Machine Learning Research, 2026
- Polynomial-Augmented Neural Networks (PANNs) with Weak Orthogonality Constraints for Enhanced Function and PDE Approximation Madison Cooley, Shandian Zhe, Robert M Kirby, Varun Shankar SIAM Journal on Scientific Computing, 2026
- A Unified Framework for Efficient Kernel and Polynomial Interpolation Milena Belianovich, Gregory E. Fasshauer, Akil Narayan, Varun Shankar Journal of Approximation Software, 2026
- Rootfinding and Optimization Techniques for Solving Nonlinear Systems of Equations Arising from Cohesive Zone Models Alberto Cattaneo, Varun Shankar, Michael Ballard AIAA SciTech 2025 Forum, 2025
News
- Sep 2026Varun gave an invited talk on multiscale kernel frames for operator learning at the Banff Workshop on Kernel Approximation and Gaussian Processes, and chaired its panel on kernel/GP software.
- Sep 2026Varun joined the editorial board of Nature Scientific Reports.
- May 2026New NSF/DOE CS2 award (with Ben Greenman and John Regehr): on-demand semantic analysis via program synthesis.
- 2026Kernel Neural Operators (KNOs) published in TMLR; PANNs published in SISC.
- 2026Neural-operator surrogates for tendon-actuated continuum robots at ICRA 2026 (with Alan Kuntz's group).
- Sep 2025New NSF award (PI): Unified Neural Operator (UNO) — towards trustworthy operator learning for scientific applications.