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Events for the 1st week of May
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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
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Aircraft Accident Investigation AAI 24-4
Tue, Apr 30, 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
Tue, Apr 30, 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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3rd Annual Symposium USC-Amazon Center on Trustworthy AI
Tue, Apr 30, 2024 @ 09:00 AM - 05:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Workshops & Infosessions
Join us in conversation with distinguished keynote speakers, technical presentations from the USC-Amazon Center’s innovative research projects, and talks by Amazon ML Ph.D. fellows.
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Ariana Perez
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PhD Dissertation Defense - Alan Romano
Tue, Apr 30, 2024 @ 09:30 AM - 11:30 AM
Thomas Lord Department of Computer Science
University Calendar
Title: Static Program Analyses for WebAssembly
Committee Members: Weihang Wang (Chair), Chao Wang, and Pierluigi Nuzzo
Date/Time: Tuesday, April 30th, 9:30am - 11:30am
Abstract: WebAssembly is a recent standard for the web that aims to enable high-performance web applications that can run at near-native speeds. The standard has gained attention in both academia and industry for its ability to speed up existing user-facing web applications. Due to its well-defined and sound design, many static program analysis techniques have been developed to accomplish various purposes of WebAssembly analysis. However, we identify gaps in the static program analysis tools of the current WebAssembly ecosystem. We find that current program optimizations applied on WebAssembly modules may lead to diminished performance. We also identify a lack of tools that help developers understand WebAssembly modules through robust binary decompilation. Finally, we find a gap in the ability to analyze cross-language WebAssembly applications across the two languages they are typically implemented in, i.e., WebAssembly and JavaScript.
In this thesis, we present a novel WebAssembly Analysis Framework, or WAF . WAF is a static program analysis framework for WebAssembly modules that consists of multiple intermediate representations. Inspired by frameworks made for Java, the core of our framework lies in our three intermediate representations that each model the WebAssembly module at a different semantic level. This structure enables WAF to serve in multiple use cases, including program optimizations, binary decompilation, cross-language program analysis, and malware detection. We aim to show that our framework can improve static program analysis in the areas that the WebAssembly ecosystem is lacking.Location: Henry Salvatori Computer Science Center (SAL) - 322
Audiences: Everyone Is Invited
Contact: Alan Romano
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PhD Thesis Proposal - Tian Ye
Tue, Apr 30, 2024 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Enhancing Adversarial Training in Low-Label Regimes
Committee Members: Viktor Prasanna (Chair), Paul Bogdan, Jyotirmoy Deshmukh, Rajgopal Kannan, Cauligi Raghavendra
Data & Time: April 30, 3:00 PM - 4:00 PM Location: EEB 219
Abstract: As machine learning models are increasingly deployed in critical real-world applications, ensuring their robustness against adversarial attacks is essential to prevent potentially severe consequences. Adversarial training, which involves teaching models to recognize and resist adversarial perturbations, is a key strategy for building such robustness. This thesis explores the enhancement of adversarial robustness in scenarios characterized by low-label regimes, where extensive labeled training data are not accessible, by addressing several challenges in existing semi-supervised adversarial training methods. Specifically, the proposed research focuses on: (1) optimizing the generation of adversarial samples to reduce the risk of overfitting, (2) enhancing the reliability of pseudo-labels to mitigate confirmation bias, and (3) simplifying the optimization of training processes to enhance accessibility and efficiency. These improvements will contribute to strengthening the security and functionality of machine learning applications against adversarial threats in a broader range of applications.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 219
Audiences: Everyone Is Invited
Contact: Ellecia Williams
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PhD Thesis Proposal - Nan Xu
Tue, Apr 30, 2024 @ 04:30 PM - 06:30 PM
Thomas Lord Department of Computer Science
University Calendar
Committee Members: Xuezhe Ma (chair), Muhao Chen, Jonathan May, Ram Nevatia, Daniel O’Leary
Title: Decoding Recipes for Coherent and Factual Text Generation
Abstract: While Large language models (LLMs) have demonstrated increasing power in generating texts, they have also called upon studies on their degeneration problems such as repetition, incoherence, hallucination, etc. My PhD thesis outlines my research aiming to tackle these challenges from the perspective of decoding, which is train-free and driven by models' own understanding of seen and generated texts. Specifically, I focus on 1) reducing undesired repetitions and off-topic generations by analyzing probability distribution of decoding steps for open-ended text generation and 2) mitigating hallucinations by studying models' uncertainty against user prompts for false-premise question answering. Motivated by the emergent ability of Large Vision Language Models (LVLMs) to perceive and understand visual signals, I will also introduce my proposal to mitigate hallucinations with effective decoding strategies given multimodal inputs.
Venue: RTH 306 and Zoom https://usc.zoom.us/j/97468606369?pwd=a2ovTlYweE1neGpTMHFtUlNrcVVnQT09
Date: 04/30/2024, 4:30-6:30PMLocation: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Ellecia Williams
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Aircraft Accident Investigation AAI 24-4
Wed, 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
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Aircraft Accident Investigation AAI 24-4
Thu, May 02, 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
Thu, May 02, 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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Computational Science Distinguished Seminar Series
Thu, May 02, 2024 @ 09:30 AM - 10:30 AM
USC School of Advanced Computing
Conferences, Lectures, & Seminars
Speaker: Yannis Kevrekidis, Johns Hopkins University
Talk Title: No Equations, No Variables, No Space and No Time: Data and the Modeling of Complex Systems
Abstract: I will give an overview of a research path in data driven modeling of complex systems over the last 30 or so years – from the early days of shallow neural networks and autoencoders for nonlinear dynamical system identification, to the more recent derivation of data driven “emergent” spaces in which to better learn generative PDE laws. In all illustrations presented, I will try to point out connections between the “traditional” numerical analysis we know and love, and the more modern data-driven tools and techniques we now have – and some mathematical questions they hopefully make possible for us to answer.
Biography: Yannis Kevrekidis studied Chemical Engineering at the National Technical University in Athens. He then followed the steps of many alumni of that department to the University of Minnesota, where he studied with Rutherford Aris and Lanny Schmidt (as well as Don Aronson and Dick McGehee in Math). He was a Director's Fellow at the Center for Nonlinear Studies in Los Alamos in 1985-86 (when Soviets still existed and research funds were plentiful). He then had the good fortune of joining the faculty at Princeton, where he taught Chemical Engineering and also Applied and Computational Mathematics for 31 years; seven years ago he became Emeritus and started fresh at Johns Hopkins (where he somehow is also Professor of Urology). His work always had to do with nonlinear dynamics (from instabilities and bifurcation algorithms to spatiotemporal patterns to data science in the 90s, nonlinear identification, multiscale modeling, and back to data science/ML); and he had the additional good fortune to work with several truly talented experimentalists, like G. Ertl's group in Berlin. Currently -on leave from Hopkins- he works with the Defense Sciences Office at DARPA. When young and promising he was a Packard Fellow, a Presidential Young Investigator and the Ulam Scholar at Los Alamos National Laboratory. He holds the Colburn, CAST Wilhelm and Walker awards of the AIChE, the Crawford and the Reid prizes of SIAM, he is a member of the NAE, the American Academy of Arts and Sciences, and the Academy of Athens.
Host: The School of Advanced Computing
More Info: https://sac.usc.edu/events/?hash=1m3nCA4M337
Location: Michelson Center for Convergent Bioscience (MCB) - 101
Audiences: Everyone Is Invited
Contact: Tessa Yao
Event Link: https://sac.usc.edu/events/?hash=1m3nCA4M337
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Machine Learning Center Seminar
Thu, May 02, 2024 @ 12:00 PM - 01:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Pengtao Xie , Assistant Professor, Department of Electrical and Computer Engineering - University of California, San Diego
Talk Title: Foundation Models and Generative AI for Medical Imaging Segmentation in Ultra-Low Data Regimes
Abstract: Semantic segmentation of medical images is pivotal in disease diagnosis and treatment planning. While deep learning has excelled in automating this task, a major hurdle is the need for numerous annotated masks, which are resource-intensive to produce due to the required expertise and time. This scenario often leads to ultra-low data regimes where annotated images are scarce, challenging the generalization of deep learning models on test images. To address this, we introduce two complementary approaches. One involves developing foundation models. The other involves generating high-fidelity training data consisting of paired segmentation masks and medical images. In the former, our bi-level optimization based method can effectively adapt the general-domain Segment Anything Model (SAM) to the medical domain with just a few medical images. In the latter, our multi-level optimization based method can perform end-to-end generation of high-quality training data from a minimal number of real images. On eight segmentation tasks involving various diseases, organs, and imaging modalities, our methods demonstrate strong generalization performance in both in-domain and out-of-domain settings. Our methods require 8-12 times less training data than baselines to achieve comparable performance.
Biography: Pengtao Xie is an assistant professor in the Department of Electrical and Computer Engineering at the University of California San Diego. His research interest lies in machine learning for healthcare. His PhD thesis was selected as a top-5 finalist for the Doctoral Dissertation Award of the American Medical Informatics Association (AMIA). He was recognized as Global Top-100 Chinese Young Scholars in Artificial Intelligence by Baidu, Tencent AI-Lab Faculty Award, Innovator Award by the Pittsburgh Business Times, Amazon AWS Machine Learning Research Award, among others. He serves as an associate editor for the ACM Transactions on Computing for Healthcare, senior area chair for AAAI, area chairs for ICML and NeurIPS, etc.
Host: Machine Learning Center
Location: Ronald Tutor Hall of Engineering (RTH) - 211
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
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DEN@Viterbi - 'Limited Status: How to Get Started' Virtual Info Session
Thu, May 02, 2024 @ 12:00 PM - 01:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi for our upcoming Limited Status: How to Get Started Virtual Information Session via WebEx to learn about the Limited Status enrollment option. The Limited Status enrollment option allows individuals with an undergraduate degree in engineering or related field, with a 3.0 GPA or above to take courses before applying for formal admission into a Viterbi graduate degree program. USC Viterbi representatives will provide a step-by-step guide for how to get started as a Limited Status student and enroll in courses online via DEN@Viterbi as early as the Summer 2024 semester.
WebCast Link: https://uscviterbi.webex.com/weblink/register/r80d33c78db1dcbf7f1baca0b7fd56d3b
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
Event Link: https://uscviterbi.webex.com/weblink/register/r80d33c78db1dcbf7f1baca0b7fd56d3b
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PhD Thesis Defense - Matthew Ferland
Thu, May 02, 2024 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense: Matthew Ferland
Committee: Shanghua Teng (Chair), David Kempe, Jiapeng Zhang, Larry Goldstein (Math)
Title: Exploring the Computational Frontier of Combinatorial Games
Abstract: People have been playing games since before written history, and many of the earliest games were combinatorial games, that is to say, games of perfect information and no chance. This type of game is still widely played today, and many popular games of this type, such as Chess and Go, are some of the most studied games of all time. This proposed work resolves around a game-independent systemic study of these games. More specifically, computational properties involving evaluating mathematical analysis tools for combinatorial games, such as Grundy values and confusion intervals, as well as identifying what can be determined about these games using simple oracle models.Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: CS Events
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Semiconductors & Microelectronics Technology Seminar - Ke Du, Thursday, May 2nd at 2pm in EEB 248
Thu, May 02, 2024 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Ke Du, UC Riverside
Talk Title: Micro- and Nanofluidic Systems for Molecular Biosensing, Nanotoxicity, and Optogenetics
Series: Semiconductors & Microelectronics Technology
Abstract: Micro- and nanofluidic systems, in conjunction with biochemistry, microscopy, nanomaterials, and machine learning components, serve as potent tools with a wide array of applications in the biomedical field. These applications encompass crucial areas like molecular diagnosis, biophysics, and optogenetics. In this presentation, we shed light on an innovative pneumatic-controlled nano-sieve device. This device is packed with magnetic beads and facilitates the rapid concentration of drug-resistant bacteria from blood samples. Subsequently, an isothermal amplification and CRISPR assay are conducted. This system achieves an on-chip concentration factor of 20x, effectively pushing the bacterial detection threshold to 100 cfu/mL. To make sensing automatic and devoid of the need for specialized instruments, a computer vision program is developed. This program exhibits an approximate accuracy rate of 100% in discerning both positive and negative samples within the microfluidic chip. This attribute renders it particularly suitable for on-site detection in resource-limited environments. Furthermore, we delve into our recent strides in comprehending the interactions between nanomaterials and eukaryotic organisms. This understanding is facilitated by a deformable microfluidic platform, advanced microscopy, and molecular dynamic simulations. Within this context, we explore a range of clinical applications. These applications span from in vivo bioimaging employing optofluidics to addressing dentine hypersensitivity and advancing the realm of synthetic biology.
Biography: Dr. Ke Du is an assistant professor of chemical and environmental engineering at UC-Riverside and leads the Nanobiosensing, Nanomanufacturing, and Nanomaterials (3N) Lab. He received his Ph.D. degree at Stevens Institute of Technology in 2015. Following post- doctoral training at UC-Berkeley with Richard A. Mathies, he started his independent career at the Rochester Institute of Technology in 2018. In 2022, Du's lab moved back to California and joined UC-Riverside. Du's research interests include in vitro molecular diagnostics, in vivo bioimaging, nanotoxicity, and nanomanufacturing. He is recipient of numerous awards and honors such as the EIPBN Best Journal Paper Award (2022), the NIH Maximizing Investigators' Research Award (2021), the Burroughs Wellcome Fund (BWF) Collaborative Travel Grant (2019), the James H. Potter Award for the outstanding Ph.D. students (2014), and the NSF Graduate Student Fellowship (2012). He has been recognized as a global rising starin sensing by ACS Sensors and a finalist for the MINE 2020 Young Scientists Award. Du's research has been supported by NIGMS, NIAID, NSF, USDA, DOE, BWF, the UNYTE Translational Research Network, and industry partners such as L3Harris, Mammoth Biosciences, Colgate Palmolive, and Biological Mimetics. Additionaly, he serves as an early career editorial advisory member for Biomicrofluidics (AIP Publishing) and Sensors and Actuators Reports (Elsevier).
Host: J Yang, H Wang, C Zhou, S Cronin, W Wu
More Information: Ke Du_2024-05-02.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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Aircraft Accident Investigation AAI 24-4
Fri, May 03, 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
Fri, May 03, 2024 @ 08:00 AM - 12:00 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 Defense- Julie Jiang
Fri, May 03, 2024 @ 10:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Student Activity
PhD Defense- Julie Jiang
Title: Socially-informed content analysis of online human behavior
Committee: Emilio Ferrara (CS and Communication, tenure, chair), Kristina Lerman (CS), Marlon Twyman II (Communication, external), Pablo Barberá (Poli Sci)
Abstract: The explosive growth of social media has not only revolutionized communication but also brought challenges such as political polarization, misinformation, hate speech, and echo chambers. This dissertation employs computational social science techniques to investigate these issues, understand the social dynamics driving negative online behaviors, and propose data-driven solutions for healthier digital interactions. I begin by introducing a scalable social network representation learning method that integrates user-generated content with social connections to create unified user embeddings, enabling accurate prediction and visualization of user attributes, communities, and behavioral propensities. Using this tool, I explore three interrelated problems: 1) COVID-19 discourse on Twitter, revealing polarization and asymmetric political echo chambers; 2) online hate speech, suggesting the pursuit of social approval motivates toxic behavior; and 3) moral underpinnings of COVID-19 discussions, uncovering patterns of moral homophily and echo chambers, while also indicating moral diversity and plurality can improve message reach and acceptance across ideological divides. These findings contribute to the advancement of computational social science and provide a foundation for understanding human behavior through the lens of social interactions and network homophily.
Location: Grace Ford Salvatori Hall Of Letters, Arts & Sciences (GFS) - 104
Audiences: Everyone Is Invited
Contact: Julie Jiang
Event Link: https://usc.zoom.us/j/5152754393?pwd=V1pzUnpEc0JtTVZlS0l5R1VMRWlRdz09&omn=91709345144
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AI Seminar- Understanding LLMs through their Generative Behavior, Successes and Shortcomings
Fri, May 03, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Swabha Swayamdipta, USC
Talk Title: Understanding LLMs through their Generative Behavior, Successes and Shortcomings
Series: AI Seminar
Abstract: Abstract: Generative capabilities of large language models have grown beyond the wildest imagination of the broader AI research community, leading many to speculate whether these successes may be attributed to the training data or model design. I will present some work from my group which sheds light on understanding LLMs by studying their generative behavior, successes and shortcomings. First, I will show that standard inference algorithms work well because of the particular design behind LLMs. Next, I will discuss recently found successes and failures of LLMs on a combination of tasks, requiring world and domain-specific knowledge, linguistic capabilities and awareness of human and social utility. Overall, these findings paint a partial yet complex picture of our understanding of LLMs and provide a guide to the next steps forward.
This event will be recorded.
It will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.
Biography: Swabha Swayamdipta is an Assistant Professor of Computer Science and a Gabilan Assistant Professor at the University of Southern California. Her research interests are in natural language processing and machine learning, with a primary interest in the estimation of dataset quality, understanding and evaluation of generative models of language, and using language technologies to understand social behavior. At USC, Swabha leads the Data, Interpretability, Language and Learning (DILL) Lab. She received her PhD from Carnegie Mellon University, followed by a postdoc at the Allen Institute for AI. Her work has received outstanding paper awards at ICML 2022, NeurIPS 2021 and an honorable mention for the best paper at ACL 2020. Her research is supported by awards from the Allen Institute for AI and Intel Labs.
Host: Jay Pujara and Karen Lake
More Info: https://www.isi.edu/events/4684/ai-seminar-understanding-llms-through-their-generative-behavior-successes-and-shortcomings/
Webcast: https://usc.zoom.us/j/95888595423?pwd=VHBLa041dUJWcWx0NEhuYmQrV29ZQT09Location: Information Science Institute (ISI) - Conf Rm#1135-37
WebCast Link: https://usc.zoom.us/j/95888595423?pwd=VHBLa041dUJWcWx0NEhuYmQrV29ZQT09
Audiences: Everyone Is Invited
Contact: Pete Zamar
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Alfred E.Mann Department of Biomedical Engineering - Seminar series
Fri, May 03, 2024 @ 11:00 AM - 12:00 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Kate Havens, Ph.D., Associate Professor of Clinical Physical Therapy Division of Biokinesiology & Physical Therapy, USC
Talk Title: Oh Baby! Integrating Anatomy, Biomechanics, and Engineering to Address Postpartum Pain
Abstract: In this presentation, Dr. Havens will introduce the biomechanics underlying pelvic girdle pain and dysfunction in postpartum mothers, integrating musculoskeletal anatomical, orthopedic biomechanical, and engineering principles. She will delve into the unique adaptations during pregnancy and postpartum, focusing on posture, gait, and balance activities, alongside an exploration of the anatomy of the region. This knowledge informs innovative engineering solutions for mitigating perinatal biomechanical challenges, particularly the unique demands of infant caregiving tasks.
Biography: Dr. Kate Havens is an Associate Professor in the Division of Biokinesiology and Physical Therapy and specializes in biomechanics and anatomical sciences. Her research interest is perinatal health. She studies biopsychosocial aspects of new motherhood and focuses her laboratory work on biomechanics underlying lumbopelvic pain and dysfunction in postpartum mothers.
Host: Megan McCain
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Carla Stanard
