Baskar Group
Computational sustainability at Iowa State University.
Two-phase flow, Proteus solver
Computational tools for food, energy, environment, and health
We build computational tools that help solve societal challenges in food, energy, environment, and health: identifying crop pests from a phone photo, simulating how air and heat move through buildings, and designing the materials inside next generation electronics. Baskar Ganapathysubramanian directs the AI Institute for Resilient Agriculture (AIIRA), a 20 million dollar national AI institute, and is Associate Director of the Translational AI Center (TrAC).
We are always looking for curious students, postdocs, and collaborators. See Join to work with us.
Research themes
selected publications
- MolGen-Transformer: A molecule language model for the generation and latent space exploration of organic moleculesComputational Materials Science, 2026
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panelPlant Phenomics, 2026
Summary
We release a curated dataset of 3D point clouds and procedural leaf models of field-grown maize from a diverse panel, giving researchers ready-to-use data for studying plant structure and phenotyping. - Digital twins for the plant sciencesTrends in Plant Science, 2025
Summary
We lay out how digital twins, virtual models kept in step with real plants, can change how the plant sciences predict, design, and manage crops. - Benchmarking scientific machine-learning approaches for flow prediction around complex geometriesCommunications Engineering, 2025
Summary
We benchmark scientific machine learning methods for predicting flow around complex geometries, moving the field beyond the simple shapes most studies rely on. - InsectNet: Real-time identification of insects using an end-to-end machine learning pipelinePNAS Nexus, 2024
Summary
We build a machine learning system that identifies insects from ordinary photos in real time, covering both pests and beneficial species. It holds up across life stages and messy field conditions, giving growers fast, reliable identification. - Fast inverse design of microstructures via generative invariance networksNature Computational Science, 2021
Summary
We built generative neural networks that respect physical symmetries to design material microstructures with targeted properties, orders of magnitude faster than conventional optimization. - Polarized X-ray scattering measures molecular orientation in polymer-grafted nanoparticlesNature Communications, 2021
Summary
We built a fast computational twin of a powerful X-ray characterization tool, so that microstructural details that were previously impossible to recover can be reconstructed in near real time. - Nanoscale control of internal inhomogeneity enhances water transport in desalination membranesScience, 2020
Summary
Using computational modeling, we showed that nanoscale unevenness in reverse osmosis membranes, long thought to be a defect, actually improves water transport. The finding changed how the field links membrane structure to desalination performance. - An explainable deep machine vision framework for plant stress phenotypingProceedings of the National Academy of Sciences, 2018
Summary
We showed that deep learning for classifying plant stress can be made interpretable, so scientists can trust and check its decisions. This helped launch the field of AI-augmented plant science.