Select a calendar:
Filter May Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for the 2nd week of May
-
Aircraft Accident Investigation AAI 24-4Mon, May 06, 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
-
Aircraft Accident Investigation AAI 24-4
Tue, May 07, 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
-
PhD Defense- Yilei Zeng
Tue, May 07, 2024 @ 10:30 AM - 12:00 PM
Thomas Lord Department of Computer Science
Student Activity
PhD Defense- Yilei Zeng
Title: Learning Social Sequential Decision-Making in Online Games
Committee: Emilio Ferrara (chair), Dmitri Williams, Michael Zyda
Abstract:
A paradigm shift towards human-centered intelligent gaming systems is gradually setting in. This dissertation explores the complexities of social sequential decision-making within online gaming environments and presents comprehensive AI solutions to enhance personalized single and multi-agent experiences. The three core contributions of the dissertation are intricately interrelated, creating a cohesive framework for understanding and improving AI in gaming. I begin by delving into the dynamics of gaming sessions and sequential in-game individual and social decision-making, which establishes a baseline of how decisions evolve, providing the necessary context for the subsequent integration of diverse information sources; two, the integration of heterogeneous information and multi-modal trajectories, which enhances decision-making generation models; and three, the creation of a reinforcement learning with human feedback framework to train gaming AIs that effectively align with human preferences and strategies, which enables the system not only learning but also interacting with humans. Collectively, this dissertation combines innovative data-driven, generative AI, representation learning, and human-AI collaboration solutions to help advance both the fields of computational social science and artificial intelligence applications of gaming.
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Yilei Zeng
-
Alfred E.Mann Department of Biomedical Engineering - Seminar series
Tue, May 07, 2024 @ 10:45 AM - 11:45 AM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Rong Li, Professor of Mechanobiology Institute, National University of Singapore Department of Cell Biology and Department of Chemical and Biomolecular Engineering, Johns Hopkins University School of Medicine
Talk Title: Mechanics and stress in cellular development, adaptation, and aging
Abstract: Mechanical processes are central to diverse cellular functions but can also be sources of cellular stress leading to aging phenotypes. My lab currently investigates three problems related to cell mechanics and stress: 1) how intracellular fluid dynamics coupled with cytoskeletal forces drive early mammalian development and reproductive aging; 2) how stress-induced protein aggregation and subsequent disaggregation are orchestrated by and affect organelles such as mitochondria and ER; and 3) the interplay between biophysical stress and chromosome instability and its contribution to cellular adaptation and cancer evolution. I will present a combination of recent findings in the first two areas of our research.
Biography: Professor Rong Li came from Johns Hopkins University where she served as the Director of the Centre for Cell Dynamics in the Johns Hopkins School of Medicine. She was recruited to NUS in 2019 as the second Director of Mechanobiology Institute (MBI). Professor Li is a globally respected leader in the study of cellular dynamics and mechanics. Her interdisciplinary research integrates genetics, quantitative imaging, biophysical measurements, mathematical modelling, genomics and proteomics — to understand how eukaryotic cells transmit their genomes, adapt to the environment, and establish distinct organisation to perform specialised functions. The diverse projects in Professor Rong Li’s lab contribute to two main research thrusts: cell and tissue aging; cellular and organismal adaptation. The study on aging focuses on understanding dynamic changes of crucial cellular components during the aging process and how these changes alter the mechanical functions of cells and tissues. The insights gained will be applied to the development of new methods for prolonging healthy aging and the repair and regeneration of deteriorated functions. The study of adaptation aims to understand the dynamics of genetic and epigenetic determinants of cells and tissues under acute or chronic stress which lead to adaptive behaviors ultimately beneficial or detrimental to the fitness of the organism. A potential application of the discoveries in this area is the prevention of cancer associated with chronic inflammatory diseases.
Location: Corwin D. Denney Research Center (DRB) - 145
Audiences: Everyone Is Invited
Contact: Carla Stanard
-
PhD Dissertation Defense - I-Hung Hsu
Tue, May 07, 2024 @ 02:10 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Towards Generalized Event Understanding in Text via Generative Models
Committee Members: Dr. Prem Natarajan (Chair), Dr. Nanyun Peng (Co-Chair), Dr. Dan O'Leary, Dr. Emilio Ferrara
Date and Time: May 7th, 2024 - 2:10p - 4:00p
Abstract: Human languages in the world, such as news or narratives, are structured around events. Focusing on these events allows Natural Language Processing (NLP) systems to better understand plots, infer motivations, consequences, and the dynamics of situations. Despite the rapidly evolving landscape of NLP technology, comprehending complex events, particularly those rarely encountered in training such as in niche domains or low-resource languages, remains a formidable challenge. This thesis explores methods to enhance NLP model generalizability for better adaptability to unfamiliar events and languages unseen during training.
My approach includes two main strategies: (1) Model Perspective: I propose a novel generation-based event extraction framework, largely different from typical solutions that make predictions by learning to classify input tokens. This new framework utilizes indirect supervision from natural language generation, leveraging large-scale unsupervised data without requiring additional training modules dependent on limited event-specific data. Hence, it facilitates the models’ ability on understanding general event concepts. I further explore advanced methods to extend this framework for cross-lingual adaptation and to utilize cross-domain robust resources effectively. (2) Data Perspective: I develop techniques to generate pseudo-training data broaden the training scope for event understanding models. This includes translating structured event labels into other languages with higher accuracy and fidelity, and synthesizing novel events for the existing knowledge base.
Overall, my work introduces a novel learning platform to the NLP community, emphasizing an innovative modeling paradigm and comprehensive data preparation to foster more generalized event understanding models.
Location: Information Science Institute (ISI) - 727
Audiences: Everyone Is Invited
Contact: I-Hung Hsu
Event Link: https:/usc.zoom.us/j/95785927723?pwd=dFlGbEcwbXlGalJ6OVk3YW41RDMrdz09
-
Aircraft Accident Investigation AAI 24-4
Wed, May 08, 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
-
Aircraft Accident Investigation AAI 24-4
Thu, May 09, 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
-
NL Seminar-Event Extraction for Epidemic Prediction
Thu, May 09, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Tanmay Parekh, UCLA
Talk Title: Event Extraction for Epidemic Prediction
Abstract: *Meeting hosts only admit on-line guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please inform us at (nlg-seminar-host(at)isi.edu) to make us aware of your attendance so we can admit you. Specify if you will attend remotely or in person at least one business day prior to the event Provide your: full name, job title and professional affiliation and arrive at least 10 minutes before the seminar begins. If you do not have access to the 6th Floor for in-person attendance, please check in at the 10th floor main reception desk to register as a visitor and someone will escort you to the conference room location.
Tanmay Parekh is a third-year PhD student in Computer Science at the University of California Los Angeles (UCLA). He is advised by Prof. Nanyun Peng and Prof. Kai-Wei Chang. Previously, he completed his Masters at the Language Technologies Institute at Carnegie Mellon University (CMU) where he worked with Prof. Alan Black and Prof. Graham Neubig. He has completed his undergraduate studies at the Indian Institute of Technology Bombay (IITB). He has also worked in the industry at Amazon and Microsoft. He has worked on a wide range of research topics in multilingual, code-switching, controlled generation, and speech technologies. His current research focuses on improving the utilization and generalizability of Large Language Models (LLMs) for applications in Information Extraction (specifically Event Extraction) across various languages and domains.
Biography: Early warnings and effective control measures are among the most important tools for policymakers to be prepared against the threat of any epidemic. Social media is an important information source here, as it is more timely than other alternatives like news and public health and is publicly accessible. Given the sheer volume of daily social media posts, there is a need for an automated system to monitor social media to provide early and effective epidemic prediction. To this end, I introduce two works to aid the creation of such an automated system using information extraction. In my first work, we pioneer exploiting Event Detection (ED) for better preparedness and early warnings of any upcoming epidemic by developing a framework to extract and analyze epidemic-related events from social media posts. We curate an epidemic event ontology comprising seven disease-agnostic event types and construct a Twitter dataset SPEED focused on the COVID-19 pandemic. Experimentation reveals how ED models trained on COVID-based SPEED can effectively detect epidemic events for three unseen epidemics of Monkeypox, Zika, and Dengue. Furthermore, we show that reporting sharp increases in the extracted events by our framework can provide warnings 4-9 weeks earlier than the WHO epidemic declaration for Monkeypox.
Since epidemics can originate across the globe, social media posts discussing them can be in varied languages. However, training supervised models on every language is a tedious and resource-expensive task. The alternative is the usage of zero-shot cross-lingual models. In this work, we introduce a new approach for label projection that can be used to generate synthetic training data in any language using the translate-train paradigm. This novel approach, CLaP, translates text to the target language and performs contextual translation on the labels using the translated text as the context, ensuring better accuracy for the translated labels. We leverage instruction-tuned language models with multilingual capabilities as our contextual translator, imposing the constraint of the presence of translated labels in the translated text via instructions. We benchmark CLaP with other label projection techniques on zero-shot cross-lingual transfer across 39 languages on two representative structured prediction tasks — event argument extraction (EAE) and named entity recognition (NER), showing over 2.4 F1 improvement for EAE and 1.4 F1 improvement for NER.
Host: Jon May and Justin Cho
More Info: https://www.isi.edu/research-groups-nlg/nlg-seminars/
Webcast: https://usc.zoom.us/j/93924756526Location: Information Science Institute (ISI) - Conf Rm#689
WebCast Link: https://usc.zoom.us/j/93924756526
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://www.isi.edu/research-groups-nlg/nlg-seminars/
-
PhD Thesis Defense - Qinyi Luo
Thu, May 09, 2024 @ 11:00 AM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Defense - Qinyi (Chelsea) Luo
Committee members: Xuehai Qian (co-chair), Viktor Prasanna (co-chair), Ramesh Govindan, Chao Wang, Feng Qian
Title: High-Performance Heterogeneity-Aware Distributed Machine Learning Model Training
Abstract: The increasing size of machine learning models and the ever-growing amount of data result in days or even weeks of time required to train a machine learning model. To accelerate training, distributed training with parallel stochastic gradient descent is widely adopted as the go-to training method. This thesis targets four challenges in distributed training: (1) performance degradation caused by large amount of data transfer among parallel workers, (2) heterogeneous computation and communication capacities in the training devices, i.e., the straggler issue, (3) huge memory consumption during training caused by gigantic model sizes, and (4) automatic selection of parallelization strategies. This thesis first delves into the topic of decentralized training and proposes system support and algorithmic innovation that strengthen tolerance against stragglers in data-parallel training. On the system side, a unique characteristic of decentralized training, the iteration gap, is identified, and a queue-based synchronization mechanism is proposed to efficiently support decentralized training as well as common straggler-mitigation techniques. In the experiments, the proposed training protocol, Hop, can provide strong tolerance against stragglers and train much faster than standard decentralized training when stragglers are present. On the algorithm side, a novel communication primitive, randomized partial All-Reduce, is proposed to enable fast synchronization in decentralized data-parallel training. The proposed approach, Prague, can achieve a 1.2x speedup against All-Reduce in a straggler-free environment and a 4.4x speedup when stragglers are present. Then, on the topic of memory optimization for training Deep Neural Networks (DNNs), an adaptive during-training model compression technique, FIITED, is proposed to reduce the memory consumption of training huge recommender models. FIITED adapts to dynamic changes in data and adjusts the dimension of each individual embedding vector continuously during training. Experiments show that FIITED is able to reduce the memory consumption of training significantly more than other embedding pruning methods, while maintaining the trained model's quality. In the end, in the aspect of automatic parallelization of training workloads, a novel unified representation of parallelization strategies, incorporating Data Parallelism (DP), Model Parallelism (MP) and Pipeline Parallelism (PP), is proposed, as well as a search algorithm that selects superior parallel settings in the vast search space. An ideal stage partition ratio for synchronous pipelines is derived for the first time, to the best of my knowledge, and it is theoretically proven that unbalanced partitions are better than balanced partitions. In addition, by examining the pipeline schedule, a trade-off between memory and performance is uncovered and explored. Experiments show that hybrid parallel strategies generated with the aforementioned optimizations consistently outperform those without such considerations.
Date: May 9, 2024
Time: 11:00 a.m. - 1:00 p.m.
Location: EEB 110
Zoom link: https://usc.zoom.us/j/95741130954?pwd=dkRkblNlNGt0TlkwOU51SlRNS0hPZz09Location: Hughes Aircraft Electrical Engineering Center (EEB) -
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/95741130954?pwd=dkRkblNlNGt0TlkwOU51SlRNS0hPZz09
-
Aircraft Accident Investigation AAI 24-4
Fri, May 10, 2024 @ 08:00 AM - 12:00 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
-
AI Seminar- Causal Inference to Inform Curation Practices in Online Platforms
Fri, May 10, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Giuseppe Russo, EPFL- Ecole Polytechnique Fédérale de Lausanne
Talk Title: Causal Inference to Inform Curation Practices in Online Platforms
Abstract: Digital platforms like Facebook, Wikipedia, Amazon, and LinkedIn play a foundational role in our society. They engage in content curation through moderation, recommendations, and monetization efforts, impacting individuals positively or negatively. In this talk, I will highlight the critical need for improving the existing methodologies used in these curation practices. I’ll make a case for the essential role of academic research in shaping policy and establishing best practices, drawing on two significant projects from my doctoral research.
First, I will delve into an observational study on Reddit that uncovered a mechanism potentially driving the proliferation of extremist communities online. Following that, I will detail the outcomes of a study assessing the impact of removing entire extremist groups from Reddit. To conclude, I will examine potential research paths aimed at improving digital platforms, with a special focus on both the promises and challenges introduced by the emergence of generative AI technologies. My research demonstrates that investigating the direct effects of content curation practices with rigor can significantly enhance the quality of online platforms.
Biography: I am a Postdoctoral Researcher at EPFL, guided by Professor Robert West. My research spans causal inference, machine learning, and the broader impacts of AI on both society and individuals. Currently, my focus is on understanding the effects of content moderation in online social networks. My research extends to the applying causal methods to decision-making processes related to health and sustainability.
I earned both my PhD and MSc from ETH Zurich, under the mentorship of Professor Frank Schweitzer, and completed my Bachelor's degree at the Politecnico di Milano. My work has been showcased at several academic conferences, including ACL, EMNLP, ICWSM, WWW, and IC2S2. Notably, it has been featured in the enlightening talk series at the International Conference of Computational Social Science (IC2S2).
Host: Fred Mortatter and Pete Zamar
More Info: https://www.isi.edu/events/4871/ai-seminar-causal-inference-to-inform-curation-practices-in-online-platforms/
Webcast: https://usc.zoom.us/webinar/register/WN_wV7p-C8-TF-MIyBOce7N4wLocation: Information Science Institute (ISI) - Virtual Only
WebCast Link: https://usc.zoom.us/webinar/register/WN_wV7p-C8-TF-MIyBOce7N4w
Audiences: Everyone Is Invited
Contact: Pete Zamar
