Proactive decisions for Digital Twin
Advancing Temporal Multimodal Multivariate Learning (KDD22) for personalized decision-making by creating predictive digital twins.
Research Area
NSF (No. 1910397, 2106989, 2200590), NASA JPL (No. 1625294, 1646362, 1659540), DOT Projects developed knowledge discovery and data mining to understand large-scale dynamics, extract multi-dimensional spatiotemporal correlations, predict behaviors, and make intelligent decisions in complex heterogeneous networks applied to active sensing (US Patent 10,743,198) and transfer learning (US Patent 11,046,247).
Latest News
NSF CISE Information Theoretic Navigation
Part of this project was in collaboration with NASA JPL MAARS: Machine learning-based Analytics for Rover Systems. Imagine you are a food writer for…
Larkin Folsom receiving a national award
Larkin Folsom receiving one of 2019 Outstanding Students of the Year Award at the Annual meeting of TRB2020, during the Council of University Transportation…
Park & NASA-JPL Education
Finding the best driving route for a Mars rover isn’t as easy as turning on a navigation app – but John Park and Hiro…
2 Research Awards to Park
During 2022 Research award ceremony, Dr. Park received two awards, first time in NCAT award history two awards given to one faculty: Outstanding Young…
Dr. Park Joins The Eastern Transportation Coalition Committee
As a faculty committee member of The Eastern Transportation Coalition (TETC), Dr. Park will help selecting vendor for the TETC Transportation Data Marketplace, lead…
"Active Sensing" News, ESS Journal, Press Release
1. Nationwide News through Spectrum Network Link 2. ESS Journal Publication Link In collaboration with Masahiro Ono at Hui Su at the Jet Propulsion…
