{"681885":{"#nid":"681885","#data":{"type":"event","title":"PhD Defense by Haejun Han","body":[{"value":"\u003Cp\u003E\u003Cstrong\u003EHaejun Han\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EBioE PhD Defense Presentation\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ETime and Date:\u0026nbsp;\u003C\/strong\u003E2:00 pm, Wednesday, April 30th, 2025.\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ELocation:\u003C\/strong\u003E\u0026nbsp;EBB CHOA\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EZoom Link:\u0026nbsp;\u003C\/strong\u003E\u003Ca href=\u0022https:\/\/gatech.zoom.us\/j\/7702113243?pwd=ZUVpVUFJS1RXYzdiMVg2dktTbFhiZz09\u0022\u003Ehttps:\/\/gatech.zoom.us\/j\/7702113243?pwd=ZUVpVUFJS1RXYzdiMVg2dktTbFhiZz09\u003C\/a\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EMeeting ID:\u0026nbsp;\u003C\/strong\u003E770 211 3243\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EPasscode:\u0026nbsp;\u003C\/strong\u003EeJjAV9\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003EAdvisor: \u003C\/strong\u003EHang Lu, Ph.D. (School of Chemical and Biomolecular Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ECommittee Members:\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003EOliver Hobert, Ph.D. (Department of Biological Sciences, Columbia University)\u003C\/p\u003E\u003Cp\u003EPatrick McGrath, Ph.D. (School of Biological Sciences, Georgia Tech)\u003C\/p\u003E\u003Cp\u003EEva Dyer, Ph.D. (Wallace H. Coulter\u0026nbsp;Department of Biomedical Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003EGhassan AlRegib, Ph.D. (School of Electrical and Computer Engineering, Georgia Tech)\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cstrong\u003ENovel Computational Methods for Scalable \u003C\/strong\u003E\u003Cem\u003E\u003Cstrong\u003EC. elegans\u003C\/strong\u003E\u003C\/em\u003E\u003Cstrong\u003E\u0026nbsp;Neuroimaging Analysis\u003C\/strong\u003E\u003C\/p\u003E\u003Cp\u003E\u2002\u2002\u2002\u2002\u2002\u2002Deciphering the relationship between neural circuit structure, function, and behavior requires powerful methods for observing and analyzing neural dynamics at scale, posing a fundamental challenge in neuroscience. The nematode \u003Cem\u003ECaenorhabditis elegans\u003C\/em\u003E, with its fully mapped nervous system and powerful genetic tools, provides an exceptional model for dissecting these mechanisms, especially when combined with fluorescence microscopy to visualize neural structure and function \u003Cem\u003Ein vivo\u003C\/em\u003E. However, realizing the full potential of this approach is often limited by critical bottlenecks in data analysis throughput, inherent trade-offs in imaging parameters like speed and resolution, and the prohibitive labor cost associated with manually annotating large, dynamic datasets.\u003C\/p\u003E\u003Cp\u003E\u2002\u2002\u2002\u2002\u2002\u2002This thesis focuses on developing novel computational tools, leveraging computer vision and machine learning, to overcome these specific limitations in \u003Cem\u003EC. elegans\u003C\/em\u003E\u0026nbsp;fluorescence neuroimaging. First, to address the challenge of quantifying synaptic structures at scale, I developed WormPsyQi, an automated pipeline using machine learning for robust, high-throughput synapse segmentation and analysis across diverse reporter types (Aim 1). Second, to mitigate imaging trade-offs that compromise functional studies, I created Deep Video DeBinning (DVDB), a semi-supervised video super-resolution method that computationally restores spatial detail lost to camera binning, enabling high-fidelity activity analysis from high-speed, high-SNR recordings for applications like studying rapid locomotion dynamics or performing volumetric whole-brain imaging (Aim 2). Third, confronting the annotation bottleneck in analyzing large volumetric neural activity datasets, I designed ASCENT, a self-supervised deep learning framework utilizing a Neuron Embedding Transformer (NETr) to achieve state-of-the-art 3D neuron tracking accuracy without requiring manual annotations (Aim 3).\u003C\/p\u003E\u003Cp\u003E\u2002\u2002\u2002\u2002\u2002\u2002Together, these computational tools provide validated, scalable solutions that enhance our ability to perform quantitative analysis of neural circuit structure and function in \u003Cem\u003EC. elegans\u003C\/em\u003E. By automating key analysis steps and computationally overcoming imaging limitations, this work lays the groundwork for deeper investigations into the structure, function, and dynamics of neural circuits in this powerful model system.\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E","summary":"","format":"limited_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003Esee below\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Novel Computational Methods for Scalable C. elegans Neuroimaging Analysis"}],"uid":"27707","created_gmt":"2025-04-17 19:12:31","changed_gmt":"2025-04-17 19:13:10","author":"Tatianna Richardson","boilerplate_text":"","field_publication":"","field_article_url":"","field_event_time":{"event_time_start":"2025-04-30T14:00:00-04:00","event_time_end":"2025-04-30T16:00:00-04:00","event_time_end_last":"2025-04-30T16:00:00-04:00","gmt_time_start":"2025-04-30 18:00:00","gmt_time_end":"2025-04-30 20:00:00","gmt_time_end_last":"2025-04-30 20:00:00","rrule":null,"timezone":"America\/New_York"},"location":"EBB CHOA","extras":[],"groups":[{"id":"221981","name":"Graduate Studies"}],"categories":[],"keywords":[{"id":"100811","name":"Phd Defense"}],"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":""}}}