The TrAC Seed Grant Program fuels collaboration and innovation in Translational AI. Launched in 2022, it has supported multiple research projects, each exploring unique aspects of AI’s real-world applications. With its growing success, TrAC expanded the program in 2023, welcoming contributions from industry partners. This initiative fosters a dynamic research ecosystem where academia and industry collaborate to drive cutting-edge AI advancements.
Seed Fund Projects
Exploring New Frontiers in AI with Funding, Collaboration, and Innovation.
Data-driven discovery of physical systems and optimal control neural algorithms
PI : Hailiang Liu
Co-PI : Baskar Ganapathysubramanian
Hui Hu
Term : Spring 2023
Award : $20,000
Computational force spectroscopy for prediction of protein-protein interactions
PI : Anwesha Sarkar
Co-PI : Adarsh Krishnamurthy
Ratul Chowdhury
Term : Spring 2023
Award : $20,000
Machine learning for predicting phenotypic distribution based on group-level omics data
PI : Guiping Hu
Co-PI : Juan P. Steibe
Term : Spring 2023
Award : $20,000
Label-Free Confirmation of Melanoma Cells via Machine Learning on Trajectory Data
PI : Anuj Sharma
Co-PI : Robbyn Anand
Term : Spring 2023
Award : $20,000
Uncertainty Quantification in Physics-based Machine Learning Models
PI : Cody Fleming
Co-PI : Baskar Ganapathysubramanian
Adarsh Krishnamurthy
Term : Spring 2023
Award : $20,000
Bacterial Genome Annotation with Phylogenetic Profiling and Autoencoders
PI : Iddo Friedberg
Co-PI : Julie Dickerson
Yana Bromberg
Term : Spring 2022
Award : $20,000
Feedback Learning for Machine Perception with System-Level Objectives
PI : Tichakorn Wonpiromsarn
Co-PI : Soumik Sarkar
Term : Spring 2022
Award : $20,000
Machine Learning for Efficient Ansatz in Variational Quantum Algorithms
PI : Aditya Ramamoorthy
Co-PI : Thomas Ladecola
Term : Spring 2022
Award : $20,000
Data-driven optimal control for parametric dynamical systems
PI : Hailiang Liu
Co-PI : Baskar Ganapathysubramania
Term : Spring 2022
Award : $20,000
| Year | Awardees | Co-PIs | Topic | Award |
|---|---|---|---|---|
| Spring 2022 | Iddo Friedberg | Julie Dickerson, Yana Bromberg | Filling the Gaps in Bacterial Genome Annotation Using Phylogenetic Profiling and Variational Autoencoders | $20,000 |
| Spring 2022 | Hailiang Liu | Baskar Ganapathysubramanian | Data-driven optimal control for parametric dynamical systems | $20,000 |
| Spring 2022 | Aditya Ramamoorthy | Thomas Iadecola | Machine Learning for Efficient Ansatz Identification in Variational Quantum Algorithms | $20,000 |
| Spring 2022 | Tichakorn Wonpiromsarn | Soumik Sarkar | Feedback Learning for Machine Perception with System-Level Objectives | $20,000 |
| Spring 2023 | Cody Fleming | Adarsh Krishnamurthy, Baskar Ganapathysubramanian | Uncertainty Quantification in Physics-based Machine Learning Models | $20,000 |
| Spring 2023 | Anuj Sharma | Robbyn Anand | Label-Free Confirmation of Circulating Melanoma Cell Identity Following Dielectrophoretic Capture by Machine Learning Analysis of Trajectory and Brightfield Micrographs. | $20,000 |
| Spring 2023 | Guiping Hu | Juan P. Steibel | Machine learning for predicting phenotypic distribution based on group-level omics data | $20,000 |
| Spring 2023 | Anwesha Sarkar | Ratul Chowdhury, Adarsh Krishnamurthy | Computational force spectroscopy for prediction of protein-protein interactions | $20,000 |
| Spring 2023 | Hailiang Liu | Hui Hu, Baskar Ganapathysubramanian | Data-driven discovery of physical systems and optimal control neural algorithms | $20,000 |
| Spring 2024 | $20,000 | |||
| Spring 2024 | $20,000 |