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Events for April 29, 2024
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Aircraft Accident Investigation AAI 24-4Mon, Apr 29, 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
Mon, Apr 29, 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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PhD Dissertation Defense - Mengxiao Zhang
Mon, Apr 29, 2024 @ 01:30 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Robust and Adaptive Algorithm Design in Online Learning: Regularization, Exploration, and Aggregation
Abstract: In recent years, online learning is becoming a central component in Artificial Intelligence and has been widely applied in many real applications. In this thesis, we focus on designing algorithms for online learning with the two characteristics: robustness and adaptivity. Motivated by the existence of unpredictable corruptions and noises in real-world applications such as E-commerce recommendation systems, robustness is a desired property. It means that the designed algorithm is guaranteed to perform well even in adversarial environments. In contrast, adaptivity complements robustness by enhancing performance in benign environments.In order to achieve robustness and adaptivity, we utilize the following three methodologies, namely regularization, exploration, and aggregation. Regularization method has been widely used in the field of machine learning to control the dynamic of the decisions, which is especially important when facing a possibly adversarial environment. In online learning problems, very often the learner can only observe partial information of the environment, making an appropriate exploration method crucial. Aggregation, a natural idea to achieve adaptivity, combines multiple algorithms that work well in different environments. Though intuitive, this requires non-trivial algorithm design for different online learning problems.In this thesis, we design algorithms for a wide range of online learning problems. We first consider the problem of multi-armed bandits with feedback graphs. Then, we consider more complex problems including linear bandits and convex bandits, which involve an infinite number of actions. We hope that the techniques and algorithms developed in this thesis can help improve the current online learning algorithms for real-world applications. Committee Members:Haipeng Luo (Chair), Vatsal Sharan, Renyuan XuLocation: Ronald Tutor Hall of Engineering (RTH) - 114
Audiences: Everyone Is Invited
Contact: Ellecia Williams
