{"id":1723,"date":"2024-09-25T20:17:00","date_gmt":"2024-09-25T20:17:00","guid":{"rendered":"https:\/\/research.uga.edu\/team-research\/?post_type=projects&p=1723"},"modified":"2024-09-25T20:17:00","modified_gmt":"2024-09-25T20:17:00","slug":"secure-and-privacy-sensitive-data-science-for-enabling-resilient-communities","status":"publish","type":"projects","link":"https:\/\/research.uga.edu\/team-research\/projects\/secure-and-privacy-sensitive-data-science-for-enabling-resilient-communities\/","title":{"rendered":"Secure and Privacy-Sensitive Data Science for Enabling Resilient Communities"},"content":{"rendered":"

[vc_row css=”.vc_custom_1727295399142{margin-top: 0px !important;margin-right: 0px !important;margin-bottom: 0px !important;margin-left: 0px !important;padding-top: 0px !important;padding-right: 0px !important;padding-bottom: 0px !important;padding-left: 0px !important;}”][vc_column css=”.vc_custom_1579011887505{margin-top: 0px !important;margin-bottom: 0px !important;margin-left: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;padding-left: 0px !important;}”][vc_column_text css=”.vc_custom_1727295414172{margin-top: 0px !important;margin-left: 0px !important;padding-top: 0px !important;padding-left: 0px !important;}”]<\/p>\n

Teaming for Interdisciplinary Research Pre-Seed Program<\/span><\/h1>\n

[\/vc_column_text][\/vc_column][\/vc_row][vc_row bg_type=”bg_color” css=”.vc_custom_1595865578984{margin-right: 0px !important;margin-bottom: 10px !important;padding-right: 0px !important;}” bg_color_value=”#004e60″][vc_column width=”1\/2″ css=”.vc_custom_1578671862861{background-color: #004e60 !important;}”][vc_column_text css=”.vc_custom_1608579786892{margin-left: 10px !important;}”]<\/p>\n

Secure and Privacy-Sensitive Data Science for Enabling Resilient Communities<\/span><\/h3>\n

[\/vc_column_text][\/vc_column][vc_column width=”1\/2″ css=”.vc_custom_1578595037141{margin-right: 0px !important;padding-right: 0px !important;}”][vc_single_image image=”523″ img_size=”full” alignment=”right” css=”.vc_custom_1608580409466{margin-top: 0px !important;margin-right: 0px !important;margin-bottom: 0px !important;margin-left: 0px !important;border-bottom-width: 0px !important;padding-top: 0px !important;padding-right: 0px !important;padding-bottom: 0px !important;padding-left: 0px !important;background-color: #ffffff !important;}”][\/vc_column][\/vc_row][vc_row][vc_column css=”.vc_custom_1595855041843{margin-top: 0px !important;padding-top: 0px !important;}”][vc_column_text][\/vc_column_text][vc_row_inner][vc_column_inner width=”2\/3″][vc_empty_space][vc_column_text]Many communities in the US and around the globe are facing increasing threats from anthropogenic climate change including flooding, wildfires, water quality degradation and heat hazards. Building resilient communities that can effectively overcome these threats requires a multi-pronged approach. While traditional approaches have been successful in addressing these challenges at local scales, there are practical limitations in operationalization of these models at regional or global scales. Recent dramatic advancements in data-driven technologies such as artificial intelligence (AI), deep learning, Internet-of-Things (IoT), and cloud computing have the potential to radically transform traditional approaches to enhance community resilience.<\/p>\n

This project will create a multidisciplinary working group to expand the conceptualization and operationalization of resilient communities through a \u201cbi-lingual\u201d dialogue between natural\/physical science researchers and data science researchers. This group will work towards laying a strong edifice for conducting exploratory research towards addressing increasingly intensifying vulnerabilities of communities by combining traditional physical\/natural science and engineering approaches with modern data-driven technologies. We will study the complex challenges involved in building robust data science driven frameworks that incorporate privacy-aware participatory sensing, IoT, cloud and edge computing, data integration and fusion, statistical and deep learning to tackle environmental grand challenges of the next decade. Furthermore, this project will outline a flexible yet robust cloud-based architecture for a data science-driven community resilience system. This layered plug-and-play architecture will support modules to seamlessly ingress data from diverse sources, integrating heterogeneous data, deriving explainable and actionable intelligence at multiple spatio-temporal scales, and adapting these modules to optimize them for multiple distinct resilience applications.[\/vc_column_text][vc_single_image image=”544″ img_size=”full”][\/vc_column_inner][vc_column_inner width=”1\/3″][vc_column_text]<\/p>\n

Team Lead<\/h4>\n

Lakshmish Ramaswamy
\nDepartment of Computer Science
\nlaksmr@uga.edu<\/a><\/p>\n

Team Members<\/h4>\n

Nandita Gaur<\/strong>
\nDepartment of Soil and Crop Sciences<\/p>\n

Andrew Grundstein<\/strong>
\nDepartment of Geography<\/p>\n

In Kee Kim<\/strong>
\nDepartment of Computer Science<\/p>\n

Sung-Hee \u201cSonny\u201d Kim<\/strong>
\nSchool of Environmental, Civil, Agricultural and Mechanical Engineering<\/p>\n

Jaewoo Lee<\/strong>
\nDepartment of Computer Science<\/p>\n

Kyu H. Lee<\/strong>
\nDepartment of Computer Science<\/p>\n

Sheng Li<\/strong>
\nDepartment of Computer Science<\/p>\n

Deepak Mishra<\/strong>
\nDepartment of Geography<\/p>\n

Cheolwoo Park<\/strong>
\nDepartment of Statistics<\/p>\n

Alicia Peduzzi<\/strong>
\nSchool of Forestry and Natural Resources<\/p>\n

Lori Sutter<\/strong>
\nSchool of Forestry and Natural Resources[\/vc_column_text][\/vc_column_inner][\/vc_row_inner][\/vc_column][\/vc_row]<\/p>\n<\/div>","protected":false},"featured_media":0,"menu_order":0,"template":"","format":"standard","meta":{"_links_to":"","_links_to_target":""},"categories":[],"class_list":["post-1723","projects","type-projects","status-publish","format-standard","hentry"],"_links":{"self":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/projects\/1723"}],"collection":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/projects"}],"about":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/types\/projects"}],"version-history":[{"count":0,"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/projects\/1723\/revisions"}],"wp:attachment":[{"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/media?parent=1723"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/research.uga.edu\/team-research\/wp-json\/wp\/v2\/categories?post=1723"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}