{"682823":{"#nid":"682823","#data":{"type":"event","title":"PhD Defense | Employing Machine Learning Techniques to Increase the Quality of Ionospheric Modeling","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003ETitle:\u0026nbsp;\u003C\/strong\u003EEmploying Machine Learning Techniques to Increase the Quality of Ionospheric Modeling\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDate:\u0026nbsp;\u003C\/strong\u003EJuly 9, 2025\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime:\u0026nbsp;\u003C\/strong\u003E2:00 PM EDT\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation: \u003C\/strong\u003EVan Leer 218\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EVirtual\u003C\/strong\u003E: \u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/95145365349?pwd=naXbcJeBkUbUHERizobilOwJKmNXok.1\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/95145365349?pwd=naXbcJeBkUbUHERizobilOwJKmNXok.1\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003EMeeting ID: 951 4536 5349\u003C\/p\u003E\u003Cp\u003EPasscode: 709575\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELiam Smith\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EMachine Learning PhD Student\u003C\/p\u003E\u003Cp\u003ESchool of Electrical and Computer Engineering\u003Cbr\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E1 Dr. Morris Cohen (Advisor)\u003C\/p\u003E\u003Cp\u003ESchool of\u0026nbsp;Electrical and Computer Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E2 Dr. David Anderson\u003C\/p\u003E\u003Cp\u003ESchool of\u0026nbsp;Electrical and Computer Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E3 Dr. Mark Davenport\u003C\/p\u003E\u003Cp\u003ESchool of\u0026nbsp;Electrical and Computer Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E4 Dr. Justin Romberg\u003C\/p\u003E\u003Cp\u003ESchool of\u0026nbsp;Electrical and Computer Engineering\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E5 Dr. Sven Simon\u003C\/p\u003E\u003Cp\u003ESchool of\u0026nbsp;Earth and Atmospheric Sciences\u003C\/p\u003E\u003Cp\u003EGeorgia Institute of Technology\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EWireless communications are impacted by the ionosphere, which is the charged part of the upper atmosphere. This ionization affects the signal paths of communications, with some signals reflecting and others passing through. Understanding the state of the ionosphere, specifically the electron density, gives insight into how these signal paths are affected. Thus, knowing the electron density of the ionosphere is quite desirable. Because the ionosphere is difficult to densely measure, observations are sparse, and models are needed to complete the data. This work investigates modeling the ionosphere with Machine Learning (ML) and using various techniques to enable the usage of additional data types. Specifically, this work details the use of temporal architectures to include fine-grained solar information and missing-compliant networks to deal with sparse data.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EEmploying Machine Learning Techniques to Increase the Quality of Ionospheric Modeling\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Liam Smith - Machine Learning PhD Student - School of Electrical and Computer Engineering"}],"uid":"36518","created_gmt":"2025-06-20 15:50:33","changed_gmt":"2025-06-20 15:51:54","author":"shatcher8","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-07-09T14:00:00-04:00","event_time_end":"2025-07-09T16:00:00-04:00","event_time_end_last":"2025-07-09T16:00:00-04:00","gmt_time_start":"2025-07-09 18:00:00","gmt_time_end":"2025-07-09 20:00:00","gmt_time_end_last":"2025-07-09 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Van Leer 218","extras":[],"groups":[{"id":"576481","name":"ML@GT"}],"categories":[],"keywords":[],"core_research_areas":[],"news_room_topics":[],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}