{"681747":{"#nid":"681747","#data":{"type":"event","title":"PhD Proposal by Xiao Jing","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EXiao Jing\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E(Advisor: Prof. Dimitri Mavris)\u003C\/p\u003E\u003Cp\u003Ewill propose a doctoral thesis entitled,\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EDEVELOPMENT OF AVIATIONBENCH: A COMPREHENSIVE BENCHMARK FRAMEWORK FOR NLP MODELS IN AVIATION SAFETY\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EOn\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EWednesday, April 16 at 9:30 a.m.\u003Cbr\u003EWeber Space and Technology Building (SST II)\u003Cbr\u003ECollaborative Visualization Environment (CoVE)\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETeams Meeting:\u003C\/strong\u003E\u0026nbsp;\u003Ca href=\u0022https:\/\/teams.microsoft.com\/l\/meetup-join\/19%3ameeting_OTg1NDNiNGEtMDk5Ni00ZWZhLTg0ODktMDE0NmYzZGIzYTc0%40thread.v2\/0?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%22cf6a291a-b734-4b9e-afd0-a9186e7de368%22%7d\u0022\u003Ehttps:\/\/teams.microsoft.com\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAbstract\u003C\/strong\u003E\u003Cbr\u003EThe field of aviation safety is increasingly reliant on advanced natural language processing (NLP) techniques to extract insights from the growing volume of incident and accident narratives. Despite significant advances in both data collection and modeling capabilities, there exists a critical gap in the standardized evaluation of NLP systems in this safety-critical domain. Current NLP research on aviation safety remains fragmented, with isolated studies targeting different datasets and tasks, making holistic progress difficult to measure. Additionally, critical aviation safety events are underrepresented in existing corpora, leading to class imbalance and poor model performance on rare but high-stakes scenarios.\u003C\/p\u003E\u003Cp\u003EIn the current paradigm, aviation safety NLP applications are evaluated using inconsistent metrics and datasets, often focusing on common scenarios while neglecting rare but critical events. Technical challenges include the domain-specific terminology of aviation safety narratives, the complex causal relationships they describe, and the need for reliable performance on safety-critical edge cases. Moreover, annotations for complex tasks like causal reasoning are scarce due to high labeling costs and the need for domain expertise. This motivates the overall objective of the current work.\u003C\/p\u003E\u003Cp\u003EThe objective of this thesis proposal is to develop a comprehensive benchmark framework that will address these limitations and standardize evaluation across multiple tasks and datasets. This framework will enable more consistent and meaningful comparison of different NLP approaches for aviation safety, with special attention to performance on rare but critical events. A thorough treatment of data imbalance and annotation scarcity is included in light of the challenges in obtaining representative datasets for all safety-critical scenarios.\u003C\/p\u003E\u003Cp\u003EThe proposed methodology utilizes a multi-faceted approach that integrates a benchmark framework for evaluating model performance across four core tasks, a knowledge-guided LLM generation system for creating high-quality synthetic data to address class imbalance, and a unified multi-task annotation framework to efficiently generate comprehensive labels. All of these components are combined in a methodology that will facilitate standardized evaluation, data augmentation, and efficient annotation for aviation safety NLP.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee\u003C\/strong\u003E\u003Cbr\u003EProf. Dimitri Mavris \u2013 School of Aerospace Engineering (Advisor)\u003Cbr\u003EDr. Mayank Bendarkar \u2013 School of Aerospace Engineering\u003Cbr\u003EProf. Duen Horng (Polo) Chau \u2013 School of Computational Science and Engineering\u003Cbr\u003EProf. Xiuwei Zhang \u2013 School of Computational Science and Engineering\u003Cbr\u003EProf. Kuen-Da (Dalton) Lin \u2013 School of International Affairs\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003E\u003Cstrong\u003EDEVELOPMENT OF AVIATIONBENCH: A COMPREHENSIVE BENCHMARK FRAMEWORK FOR NLP MODELS IN AVIATION SAFETY\u003C\/strong\u003E\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"DEVELOPMENT OF AVIATIONBENCH: A COMPREHENSIVE BENCHMARK FRAMEWORK FOR NLP MODELS IN AVIATION SAFETY"}],"uid":"27707","created_gmt":"2025-04-11 17:25:44","changed_gmt":"2025-04-11 17:26:39","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-16T09:30:00-04:00","event_time_end":"2025-04-16T11:00:00-04:00","event_time_end_last":"2025-04-16T11:00:00-04:00","gmt_time_start":"2025-04-16 13:30:00","gmt_time_end":"2025-04-16 15:00:00","gmt_time_end_last":"2025-04-16 15:00:00","rrule":null,"timezone":"America\/New_York"},"location":"Weber Space and Technology Building (SST II) Collaborative Visualization Environment (CoVE)","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"102851","name":"Phd proposal"}],"core_research_areas":[],"news_room_topics":[],"event_categories":[{"id":"1788","name":"Other\/Miscellaneous"}],"invited_audience":[{"id":"78771","name":"Public"}],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[],"email":[],"slides":[],"orientation":[],"userdata":""}}}