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SUNMONTUEWEDTHUFRISAT
Events for May 01, 2024
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Aircraft Accident Investigation AAI 24-4Wed, May 01, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
The course is designed for individuals who have limited investigation experience. All aspects of the investigation process are addressed, starting with preparation for the investigation through writing the final report. It covers National Transportation Safety Board and International Civil Aviation Organization (ICAO) procedures. Investigative techniques are examined with an emphasis on fixed-wing investigation. Data collection, wreckage reconstruction, and cause analysis are discussed in the classroom and applied in the lab.
The USC Aircraft Accident Investigation lab serves as the location for practical exercises. Thirteen aircraft wreckages form the basis of these investigative exercises. The crash laboratory gives the student an opportunity to learn the observation and documentation skills required of accident investigators. The wreckage is examined and reviewed with investigators who have extensive actual real-world investigation experience. Examination techniques and methods are demonstrated along with participative group discussions of actual wreckage examination, reviews of witness interview information, and investigation group personal dynamics discussions.Location: WESTMINSTER AVENUE BUILDING (WAB) - Unit E
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AAAI4
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Safety Management for Aviation Maintenance MAINT 24-2
Wed, May 01, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
This course provides supervisors with aviation safety principles and practices needed to manage the problems associated with aircraft maintenance operations. In addition, it prepares attendees to assume safety responsibilities in their areas of operation. It does not teach aircraft maintenance and assumes the attendee has a maintenance background.
Location: Century Boulevard Building (CBB) - 920
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AMAINT2
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Wed, May 01, 2024 @ 09:45 AM - 10:45 AM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Jeff Saucerman, Ph.D., Professor of Biomedical Engineering and Cardiovascular Medicine Vivian Pinn Scholar, School of Medicine University of Virginia
Talk Title: Fusing mechanistic networks and machine learning to understand inflammation-fibrosis coupling
Abstract: Inflammation and fibrosis are conserved phases of wound healing in the heart,skin, and other organs. Yet therapeutic attempts at manipulating inflammationand fibrosis have had limited success. In this talk, I will present ourcomputational and experimental systems biology research on cardiacinflammation and fibrosis. These studies include large scale computationalmodels of the intracellular signaling networks of multiple cardiac cell types,experimental drug screens, and new methods that fuse mechanistic andmachine-learning approaches to understand how these drugs work. Ourcomputational models are validated with new experiments in cells and mice.
Biography: Dr. Jeff Saucerman is a Professor of Biomedical Engineering and Professor ofCardiovascular Medicine at the University of Virginia. He leads a research group in cardiacsystems biology, focused on identifying and controlling the molecular networks involved inheart disease. He received a B.S. in Engineering Science from Pennsylvania StateUniversity, Ph.D. in Bioengineering from the University of California San Diego, andcompleted a postdoctoral fellowship with Dr. Donald Bers at Loyola University Chicago. Dr.Saucerman has received a number of awards including an NSF CAREER Award, Fellow ofthe American Heart Association and American Institute of Medical and BiologicalEngineering, the Dean’s Excellence in Teaching Award, BME Mentoring Award, and theVivian Pinn Scholar Award.
Host: Stacey Finley
Location: 101
Audiences: Everyone Is Invited
Contact: Carla Stanard
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PhD Defense- Woojeong Jin
Wed, May 01, 2024 @ 10:00 AM - 11:30 AM
Thomas Lord Department of Computer Science
Student Activity
PhD Defense- Woojeong Jin
Title: Bridging the Visual Knowledge Gaps in Pre-trained Models
Committee: Xiang Ren (chair), Ram Nevatia, Yan Liu, Toby Mintz.
Abstract: Humans acquire knowledge by processing visual information through observation and imagination, which expands our reasoning capability about the physical world we encounter every day. Despite significant progress in solving AI problems, current state-of-the-art models in natural language processing (NLP) and computer vision (CV) have limitations in terms of reasoning and generalization, particularly with complex reasoning on visual information and generalizing to unseen vision-language tasks. In this thesis, we aim to build a reasoner that can do complex reasoning about the physical world and generalization on vision-language tasks. we will present a few lines of work to bridge the visual knowledge gaps in pre-trained models.Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Woojeong Jin
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Wed, May 01, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Paula Cannon, Ph.D. , Distinguished Professor of Molecular Microbiology and Immunology in the Keck School of Medicine of USC
Talk Title: Move over CAR T cells -“ engineering B cells to express custom molecules
Abstract: We use CRISPR/Cas9 gene editing to reprogram B cells to express custom antibodies and antibody-like molecules. These include broadly neutralizing antibodies that can control HIV, but which are not made in response to candidate HIV vaccines. To do this, we developed a simplified gene editing protocol that inserts custom antigen-recognizing domains into constant regions of the immunoglobulin locus, resulting in molecules that mimic the heavy chain only antibodies found in Camelids. This approach preserves the important features of natural antibody expression, allowing engineered B cells to respond to matched antigens and differentiate into antibody-secreting cells. I will present our data evaluating this approach in ex vivo human tonsil organoids and in non-human primates, and describe the flexibility and potential applications of this new type of immune cell therapy.
Biography: Paula Cannon, PhD, is a Distinguished Professor of Molecular Microbiology and Immunology in the Keck School of Medicine of USC. She obtained her PhD in bacterial gene transfer from the University of Liverpool in the UK and did postdoctoral work on HIV and gene therapy at both Harvard and Oxford Universities. Dr. Cannon uses gene editing technologies such as CRISPR/Cas9 to manipulate immune cells, with the goal of developing cell therapy treatments for HIV, cancer and other chronic diseases. Most recently, her group has been editing B cells to express completely customized molecules, such as antibodies that can neutralize multiple different strains of HIV. Such a platform could turn B cells into factories in the body to secrete antibodies with desirable properties, including those that are not easily generated by vaccination. Dr. Cannon is well known as a gene therapist and will become the president of the American Society for Gene and Cell Therapy in 2024.
Host: Peter Wang
Location: Corwin D. Denney Research Center (DRB) - 146
Audiences: Everyone Is Invited
Contact: Carla Stanard
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PhD Dissertation Defense- Basel Shbita
Wed, May 01, 2024 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Student Activity
PhD Dissertation Defense- Basel Shbita
Title: Transforming Unstructured Historical and Geographic Data into Spatio-Temporal Knowledge Graphs
Committee: Craig A. Knoblock (chair), Cyrus Shahabi, John P. Wilson; Jay Pujara, Yao-Yi Chiang
Abstract: This dissertation presents a comprehensive approach to the transformation, integration and semantic enrichment of historical spatio-temporal data into knowledge graphs. The dissertation encompasses three core contributions: one, the automated generation of knowledge graphs from digitized historical maps for analyzing geographical changes over time; two, the integration of spatial and semantic context embeddings for accurate geo-entity recognition and semantic typing; and three, the creation of a comprehensive knowledge graph for the analysis of historical data from digitized archived records. I introduce innovative methodologies and practical tools to support researchers from diverse fields, enabling them to derive meaningful insights from historical and geographic data. My approach is demonstrated through various applications, such as analyzing geospatial changes over time in USGS (United States Geological Survey) historical maps of transportation networks and wetlands, automatic semantic typing of unlabeled georeferenced spatial entities, and constructing a spatio-temporal knowledge graph from digitized historical mineral mining data. The dissertation combines semantic web technologies, representation learning, and semantic modeling to build comprehensive knowledge graphs that support geospatial and temporal analyses.Audiences: Everyone Is Invited
Contact: Basel Shbita
Event Link: https://usc.zoom.us/j/97894910088?pwd=ZVQ0VU9lYlJaWTM4V2w5Vk1maEVOQT09
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PhD Thesis Proposal - Ta-Yang Wang
Wed, May 01, 2024 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Training Heterogeneous Graph Neural Networks using Bandit Sampling
Presenter: Ta-Yang Wang
Time: May 1st, 3:00 PM - 4:00 PM
Location: EEB 219
Committee members: Viktor Prasanna (chair), Jyotirmoy Deshmukh, Rajgopal Kannan, Aiichiro Nakano, and Cauligi Raghavendra
Abstract: Graph neural networks (GNNs) have gained significant attention across diverse areas due to their superior performance in learning graph representations. While GNNs exhibit superior performance compared to other methods, they are primarily designed for homogeneous graphs, where all nodes and edges are of the same type. Training a GNN model for large-scale graphs incurs high computation and storage costs, especially when considering the heterogeneous structural information of each node. To address the demand for efficient GNN training, various sampling methods have been proposed. In this proposal, we hypothesize that one can improve the training efficiency via bandit sampling, an online learning algorithm with provable convergence under weak assumptions on the learning objective. The main idea is to prioritize node types with more informative connections with respect to the learning objective. Additionally, we analyze the limitations of the framework, thus advancing its applicability in large-scale graph learning tasks.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 219
Audiences: Everyone Is Invited
Contact: Ellecia Williams
