EE Seminar: two lectures by Jordan Hashemi & Anish Simhal from Duke University
(The talk will be given in English)
Speaker: Dr. Jordan Hashemi and Dr. Anish Simhal
Duke University
Monday, October 15th, 2018
15:00 - 16:00
Room 206, Wolfson Mechanical Eng. Bldg., Faculty of Engineering
Each lecture will be 30 min.
The first (15:00-15:30) will be given by Jordan Hashemi
Computer Vision and Machine Learning for Computational Psychiatry
Observational assessments are critical for screening, diagnosing, and monitoring developmental disorders. However, current tools for objectively measuring young children’s observed behaviors are expensive, time-consuming, and require extensive training and professional administration. This lack of scalable, reliable, and validated tools impacts access to evidence-based knowledge and limits our capacity to collect population-level data in non-clinical, naturalistic settings. To address this gap, we developed a mobile paradigm to collect videos of young children while they watched movies designed to elicit autism-related behaviors and then used computer vision to automatically code behavioral responses of these videos. This mobile paradigm has allowed for one of the largest video data collections for autism and the discovery of novel biomarkers. In this talk, we will go into detail about our paradigm and demonstrate its impact towards scalable, objective behavioral coding for screening and monitoring children's development.
The second (15:30-16:00) will be given by Anish Simhal
A Computational Synaptic Antibody Characterization Tool for Array Tomography
Application-specific validation of antibodies is a critical prerequisite for their successful use. Here we introduce an automated framework for characterization and screening of antibodies against synaptic molecules for high-resolution immunofluorescence array tomography (AT). The proposed Synaptic Antibody Characterization Tool (SACT) is designed to provide an automatic, robust, flexible, and efficient tool for antibody characterization at scale. SACT automatically detects puncta of immunofluorescence labeling from candidate antibodies and determines whether a punctum belongs to a synapse. The molecular composition and size of the target synapses expected to contain the antigen is determined by the user, based on biological knowledge. Operationally, the presence of a synapse is defined by the colocalization or adjacency of the candidate antibody punctum to one or more reference antibody puncta. The outputs of SACT are automatically computed measurements such as target synapse density and target specificity ratio that reflect the sensitivity and specificity of immunolabeling with a given candidate antibody. These measurements provide an objective way to characterize and compare the performance of different antibodies against the same target, and can be used to objectively select the antibodies best suited for AT and potentially for other immunolabeling applications.
