Project Description

Solving global challenges requires new approaches to integrate the latest sensing technology, machine learning, and big data analytics with human expertise and knowledge to created effective and efficient trusted decision making environments that increase agricultural and community sustainability. These approaches can enable long-term, effective decision making to solve community resiliency, business resiliency, and global food-energy-water problems. Some related projects include work in integrating human-knowledge, advanced analytics, data-science, and new plant and soil science for increasing perennial crop sustainability,creating decision-making environments for sustainability environments in agricultural and urban areas of Peru, and helping cities become more resilient to natural disasters, crime, and public safety threats.

Start Date

Jan 1, 2020

Postdoc Qualifications 

Data science expertise, interactive decision making, and/or sustainability experience or background. Strong interest in application/domain-driven research. Experience in trusted or explainable AI is an advantage.

Co-advising 

David Ebert
School of ECE
ebertd@purdue.edu

Tim Filley
EAS
Center for the Environment
filley@purdue.edu 

References 

Arequipa Nexus project - UNSA university, Arequipa, Peru. Numerous agricultural producers in California, as well as the National Wine Grape Initiative, and the California Almond Board.

Snyder, L. S., Lin, Y.-S., Karimzadeh, M., Goldwasser, D., & Ebert, D. S., “Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness,” IEEE Transactions on Visualization and Computer Graphics, to appear 2020. 

Zhang, J., Wang, Y., Molino, P., Li, L., & Ebert, D. S., “Manifold: A Model-Agnostic Framework for Interpretation and Diagnosis of Machine Learning Models,” IEEE Transactions on Visualization and Computer Graphics, 2019. 

Zhao, J., Karimzadeh, M., Masjedi, A., Wang, T., Zhang, X., Crawford, M. M., & Ebert, D. S., "FeatureExplorer: Interactive Feature Selection and Exploration of Regression Models for Hyperspectral Images", IEEE VIS 2019 Short Papers, to appear 2019. 

Ebert, D., Owens, P., Butzke, C., “4D High Resolution Vineyard Soil Assessment for Hydrological Modeling in Combination with Automated Data Analysis and Visualization to Manage Site-Specific Grape and Wine Quality,” 11th International Terroir Congress, 2016