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

Scientific machine learning 57 Numerical methods and FEM 47 HPC and GPU computing 19 Cyber-agricultural systems 85 Plant phenotyping and sensing 79 Energy materials and devices 38 Microfluidics and electrokinetics 26 Building and urban physics 12 Design, optimization, and control 26

selected publications

  1. InsectNet: Real-time identification of insects using an end-to-end machine learning pipeline
    Shivani Chiranjeevi, Mojdeh Saadati, Zi K. Deng, and 10 more authors
    PNAS 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.
    Cyber-agricultural systemsPlant phenotyping and sensing
    DOI
  2. Fast inverse design of microstructures via generative invariance networks
    Xian Yeow Lee, Joshua R. Waite, Chih-Hsuan Yang, and 6 more authors
    Nature 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.
    Scientific machine learningEnergy materials and devices
    DOI
  3. Polarized X-ray scattering measures molecular orientation in polymer-grafted nanoparticles
    S. Mukherjee, Jason K. Streit, Eliot Gann, and 7 more authors
    Nature 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.
    Energy materials and devicesScientific machine learning
    DOI
  4. Nanoscale control of internal inhomogeneity enhances water transport in desalination membranes
    Tyler E. Culp, Biswajit Khara, Kaitlyn P. Brickey, and 10 more authors
    Science, 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.
    Energy materials and devices
    DOI
  5. An explainable deep machine vision framework for plant stress phenotyping
    Sambuddha Ghosal, David Blystone, Asheesh K. Singh, and 3 more authors
    Proceedings 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.
    Cyber-agricultural systemsPlant phenotyping and sensing
    DOI