Graduate Student Name: Chatterjee, Soumyadeep Worked for more than 160 Hours: Yes Contribution to Project: Develop structured sparse regression methods for prediction of land variables such as temperature and precipitation on ocean variables. Establish rates of convergence of such methods for low-sample high-dimensional regimes. Name: Chowdhury, Aritra Worked for more than 160 No Contribution to Project: Review of existing methods of reconstructions of past climate based on proxies. Investigation of suitability of matrix completion methods for paleoclimatic reconstructions. Name: Corcoran, Jennifer Worked for more than 160 Hours: No Contribution to Project: Develop new land cover/use mapping methods based on decision trees that can be used to create improved inputs to climate models. Name: Faghmous, Worked for more than 160 No Contribution to Project: Designed efficient network construction algorithms for entire group. Mentored three undergraduate students. Designed data-analytic methods to investigate SST- Hurricane relationship. Identified and built useful climate datasets for group research. Managed collaboration across Co-PIs for our hurricane research. Name: Fu, Qiang Worked for more than 160 Hours: Yes Contribution to Project: Developed scalable methods, based on graph-structured linear programs, for detecting significant droughts from spatiotemporal precipitation data. Developed parallel implementations which scales with use of multiple cores. Name: Harding, Keith Worked for more than 160 Hours: Yes Contribution to Project: Evaluation of historical CMIP5 model performance and dynamical downscaling of future projections for select models to investigate extreme precipitation events with climate change. Name: Heyman, Megan Worked for more than 160 Hours: Yes Contribution to Project: Statistical dimension reduction techniques and statistical inference and data analysis in high-dimensional problems. Research projects include alternative techniques of penalized regression estimation that may lead to better quality statistical inference, resampling techniques for penalized regression, and using probabilistic matrix factorization techniques for dimension reduction. Name: Jiang, Zhe Worked for more than 160 Yes Contribution to Project: Develop efficient computational approaches for learning spatial decision tree models for geographical classification (e.g., land cover classification, wetland mapping).
Graduate Student Name: Chatterjee, Soumyadeep Worked for more than 160 Hours: Yes Contribution to Project: Develop structured sparse regression methods for prediction of land variables such as temperature and precipitation on ocean variables. Establish rates of convergence of such methods for low-sample high-dimensional regimes. Name: Chowdhury, Aritra Worked for more than 160 No Contribution to Project: Review of existing methods of reconstructions of past climate based on proxies. Investigation of suitability of matrix completion methods for paleoclimatic reconstructions. Name: Corcoran, Jennifer Worked for more than 160 Hours: No Contribution to Project: Develop new land cover/use mapping methods based on decision trees that can be used to create improved inputs to climate models. Name: Faghmous, Worked for more than 160 No Contribution to Project: Designed efficient network construction algorithms for entire group. Mentored three undergraduate students. Designed data-analytic methods to investigate SST- Hurricane relationship. Identified and built useful climate datasets for group research. Managed collaboration across Co-PIs for our hurricane research. Name: Fu, Qiang Worked for more than 160 Hours: Yes Contribution to Project: Developed scalable methods, based on graph-structured linear programs, for detecting significant droughts from spatiotemporal precipitation data. Developed parallel implementations which scales with use of multiple cores. Name: Harding, Keith Worked for more than 160 Hours: Yes Contribution to Project: Evaluation of historical CMIP5 model performance and dynamical downscaling of future projections for select models to investigate extreme precipitation events with climate change. Name: Heyman, Megan Worked for more than 160 Hours: Yes Contribution to Project: Statistical dimension reduction techniques and statistical inference and data analysis in high-dimensional problems. Research projects include alternative techniques of penalized regression estimation that may lead to better quality statistical inference, resampling techniques for penalized regression, and using probabilistic matrix factorization techniques for dimension reduction. Name: Jiang, Zhe Worked for more than 160 Yes Contribution to Project: Develop efficient computational approaches for learning spatial decision tree models for geographical classification (e.g., land cover classification, wetland mapping).
2012%20Expeditions%20Annual%20Report%20v12.docx