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Events for March 07, 2024
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System Safety SSC 24-2Thu, Mar 07, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
Instruction is given in system safety engineering and management, emphasizing complex, high-technology systems. Engineering methods are illustrated with practical, numerical examples. The principal system safety analysis method is taught with classroom and homework problems. The preparation of a system safety program plan and management of the system safety process in all phases of the system life are examined in depth. A classroom project allows students to apply system safety management and engineering methods while working as a team. Enrichment lectures in special areas of knowledge essential to the system safety process will also be presented. Each student should bring a calculator with statistical functions.
Location: Century Boulevard Building (CBB) - 920
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24ASSC2
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Aircraft Accident Investigation AAI 24-3
Thu, Mar 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=24AAAI3
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CS Colloquium: Ben Lengerich (MIT) - Contextualized learning for adaptive yet persistent AI in biomedicine
Thu, Mar 07, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ben Lengerich, MIT
Talk Title: Contextualized learning for adaptive yet persistent AI in biomedicine
Series: Computer Science Colloquium
Abstract: Machine learning models often exhibit diminished generalizability when applied across diverse biomedical contexts (e.g., across health institutions), leading to a significant discrepancy between expected and actual performance. To address this challenge, this presentation introduces "contextualized learning", a meta-learning paradigm designed to enhance model adaptability by learning meta-relationships between dataset context and statistical parameters. Using network inference as an illustrative example, I will show how contextualized learning estimates context-specific graphical models, offering insights such as personalized gene expression analysis for cancer subtyping. The talk will also discuss trends towards “contextualized understanding”, bridging statistical and foundation models to standardize interpretability. The primary aim is to illustrate how contextualized learning and understanding contribute to creating learning systems that are both adaptive and persistent, facilitating cross-context information sharing and detailed analysis.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ben Lengerich is a Postdoctoral Associate and Alana Fellow at MIT’s Computer Science and Artificial Intelligence Lab (CSAIL) and the Broad Institute of MIT and Harvard, where he is advised by Manolis Kellis. His research in machine learning and computational biology emphasizes the use of context-adaptive models to understand complex diseases and advance precision medicine. Through his work, Ben aims to bridge the gap between data-driven insights and actionable medical interventions. He holds a PhD in Computer Science and MS in Machine Learning from Carnegie Mellon University, where he was advised by Eric Xing. His work has been recognized with spotlight presentations at conferences including NeurIPS, ISMB, AMIA, and SMFM, financial support from the Alana Foundation, and recognition as a "Rising Star in Data Science” by the University of Chicago and UC San Diego.
Host: Willie Neiswanger
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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ECE Seminar: Sarah H. Cen
Thu, Mar 07, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Sarah H. Cen, EECS Dept, MIT
Talk Title: Paths to AI Accountability
Abstract: We have begun grappling with difficult questions related to the rise of AI, including: What rights do individuals have in the age of AI? When should we regulate AI and when should we abstain? What degree of transparency is needed to monitor AI systems? These questions are all concerned with AI accountability: determining who owes responsibility and to whom in the age of AI. In this talk, I will discuss the two main components of AI accountability, then illustrate them through a case study on social media. Within the context of social media, I will focus on how social media platforms filter (or curate) the content that users see. I will review several methods for auditing social media, drawing from concepts and tools in hypothesis testing, causal inference, and LLMs.
Biography: Sarah is a final-year PhD student at MIT in the Electrical Engineering and Computer Science Department advised by Professor Aleksander Madry and Professor Devavrat Shah. Sarah utilizes methods from machine learning, statistical inference, causal inference, and game theory to study responsible computing and AI policy. Previously, she has written about social media, trustworthy algorithms, algorithmic fairness, and more. She is currently interested in AI auditing, AI supply chains, and IP Law x Gen AI.
Host: Drs. Urbashi Mitra (ubli@usc.edu) and Mahdi Soltanolkotabi (soltanol@usc.edu)
Webcast: https://usc.zoom.us/j/97190024349?pwd=a0NTY2J5WjdKQUsvL3BtdTBSNGZTQT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
WebCast Link: https://usc.zoom.us/j/97190024349?pwd=a0NTY2J5WjdKQUsvL3BtdTBSNGZTQT09
Audiences: Everyone Is Invited
Contact: Mayumi Thrasher
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NL Seminar - Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Thu, Mar 07, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Zixiang Chen, UCLA
Talk Title: Self-Play Fine-Tuning Converts Weak Language Models to Strong Language Models
Series: NL Seminar
Abstract: REMINDER: This talk will be a live presentation only, it will not be recorded. Meeting hosts only admit 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 provide your: Full Name, Title and Name of Workplace to (nlg-seminar-host(at)isi.edu) beforehand so we’ll be aware of your attendance. Also, let us know if you plan to attend in-person or virtually. More Info for NL Seminars can be found at: https://nlg.isi.edu/nl-seminar/. Harnessing the power of human-annotated data through Supervised Fine-Tuning (SFT) is pivotal for advancing Large Language Models (LLMs). In this talk, I will introduce our newest fine-tuning method, Self-Play Fine-Tuning (SPIN), which improves LLMs without the need for additional human-annotated data. SPIN utilizes a self-play mechanism, where the LLM enhances its capabilities by generating its own training data through interactions with instances of itself. Specifically, the LLM generates its own training data from its previous iterations, refining its policy by discerning these self-generated responses from those obtained from human-annotated data. As a result, SPIN unlocks the full potential of human-annotated data for SFT. Our empirical results show that SPIN can improve the LLM’s performance across a variety of benchmarks and even outperform models trained through direct preference optimization (DPO) supplemented with extra GPT-4 preference data. Additionally, I will outline the theoretical guarantees of our method. For more details and access to our codes, visit our GitHub repository (https://github.com/uclaml/SPIN).
Biography: Zixiang Chen is currently a Ph.D. student in computer science at the Department of Computer Science, University of California, Los Angeles (UCLA), advised by Prof. Quanquan Gu. He obtained his bachelor’s degree in mathematics from Tsinghua University. He is broadly interested in the theory and applications of deep learning, optimization, and control, with a focus on generative models, representation learning, and multi-agent reinforcement learning. Recently, he has been utilizing AI to enhance scientific discovery in the domain of public health. He was a visiting graduate student in the theory of reinforcement learning program at the Simons Institute for the Theory of Computing. If speaker approves to be recorded for this NL Seminar talk, it will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI. Subscribe here to learn more about upcoming seminars: https://www.isi.edu/events/
Host: Jon May and Justin Cho
More Info: https://nlg.isi.edu/nl-seminar/
Webcast: https://youtu.be/Fg4C6YZcqQ4Location: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://youtu.be/Fg4C6YZcqQ4
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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TCS Celebrating Women: Empowerment and Innovation
Thu, Mar 07, 2024 @ 02:00 PM - 03:00 PM
Viterbi School of Engineering Career Connections
Workshops & Infosessions
Join Tata Consultancy Services (TCS) as we celebrate International Women's Day and learn about career opportunities with a global leader.
TCS is delighted to bring together a panel of technology leaders who make an impact in the business and consulting spaces in TCS. Come hear from Courtney Wood, Director of Mergers & Acquisitions, and Chandrika Shrinivasan, Business Unit Head for Banking and Financial Services, on their career journey and learn more about their business units at TCS. The 1 hour virtual session will be interactive and allow you the opportunity to participate in a live Q&A session with senior leaders and learn more about their careers and how you can amplify your impact at TCS!
Date: Thursday, March 7th 2024
Time: 2:00-3:00 pm PST
RSVP using the attached registration link
Don't miss this opportunity to be part of a transformative conversation and contribute to building a more inclusive and equitable future. Register now to secure your spot and join us in celebrating the remarkable achievements of women on International Women's Day!
Location: Virtual Event
Audiences: Everyone Is Invited
Contact: RTH 218 Viterbi Career Connections
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DEN@Viterbi - Online Graduate Engineering Virtual Information Session
Thu, Mar 07, 2024 @ 05:00 PM - 06:00 PM
DEN@Viterbi, Viterbi School of Engineering Graduate Admission
Workshops & Infosessions
Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top-ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online. Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details, and the benefits of online delivery.
WebCast Link: https://uscviterbi.webex.com/weblink/register/r65e189f49680890639e9b60462958a27
Audiences: Everyone Is Invited
Contact: Corporate & Professional Programs
Event Link: https://uscviterbi.webex.com/weblink/register/r65e189f49680890639e9b60462958a27
