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Events for March 28, 2024
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Gas Turbine Engine Accident Investigation GTAI 24-2Thu, Mar 28, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
This specialized accident investigation course is directed to fixed-wing turbojet and turboprop as well as turbine-powered rotary-wing aircraft. The course examines specific turbine engine investigation methods and provides technical information related to material factors and metallurgical failure investigation. This is a fundamental accident investigation course. Individuals with many years of engine investigations may find this course too basic. It is assumed that the attendee has a basic understanding of jet engines.
Location: Century Boulevard Building (CBB) - 960
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AGTAI2
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Helicopter Accident Investigation HAI 24-2
Thu, Mar 28, 2024 @ 08:00 AM - 04:00 PM
Aviation Safety and Security Program
University Calendar
The course examines the investigation of helicopter accidents to include processes used to determine the cause. The course includes interactive lectures, various case studies, examination of component wreckage in the classroom, and helicopter wreckage examination in the laboratory. The course includes an examination of helicopter rotor systems, controls, performance variables, flight hazards, and material characteristics involved in helicopter operations and accidents. Although Aircraft Accident Investigation (AAI) is not a prerequisite, it is assumed that the attendee has either completed AAI or has some previous experience in aircraft accident investigation.
Location: Century Boulevard Building (CBB) - 920
Audiences: Everyone Is Invited
Contact: Daniel Scalese
Event Link: https://avsafe.usc.edu/wconnect/CourseStatus.awp?&course=24AHAI2
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CS Colloquium: Yangsibo Huang - Auditing Policy Compliance in Machine Learning Systems
Thu, Mar 28, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yangsibo Huang, Princeton University
Talk Title: Auditing Policy Compliance in Machine Learning Systems
Abstract: As the capabilities of large-scale machine learning models expand, so too do their associated risks. There is an increasing demand for policies that mandate these models to be safe, privacy-preserving, and transparent regarding data usage. However, there are significant challenges with developing enforceable policies and translating the qualitative mandates into quantitative, auditable, and actionable criteria. In this talk, I will present my work on addressing the challenges. I will first share my exploration of privacy leakage and mitigation strategies in distributed training. Then, I will explore strategies for auditing compliance with data transparency regulations. I will also examine methods to quantify and assess the fragility of safety alignments in Large Language Models. Finally, I will discuss my plans for future research directions, including collaboration with policy researchers and policymakers. This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Yangsibo Huang is a Ph.D. candidate and Wallace Memorial Fellow at Princeton University. She has been doing research at the intersection of machine learning, systems, and policy, with a focus on auditing and improving machine learning systems’ compliance with policies, from the perspectives of privacy, safety, and data usage. She interned at Google AI, Meta AI, and Harvard Medical School and was named an EECS rising star in 2023.
Host: Yue Zhao
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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ECE-S Seminar - Dr. Amrita Roy Chowdhury
Thu, Mar 28, 2024 @ 10:00 AM - 11:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Amrita Roy Chowdhury, CRA/CCC CIFellow, University of California, San Diego
Talk Title: Data Privacy in the Decentralized Era
Abstract: Data is today generated on smart devices at the edge, shaping a decentralized data ecosystem comprising multiple data owners (clients) and a service provider (server). Clients interact with the server with their personal data for specific services, while the server performs analysis on the joint dataset. However, the sensitive nature of the involved data, coupled with inherent misalignment of incentives between clients and the server, breeds mutual distrust. Consequently, a key question arises: How to facilitate private data analytics within a decentralized data ecosystem, comprising multiple distrusting parties?
My research shows a way forward by designing systems that offer strong and provable privacy guarantees while preserving complete data functionality. I accomplish this by systematically exploring the synergy between cryptography and differential privacy, exposing their rich interconnections in both theory and practice. In this talk, I will focus on two systems, CryptE and EIFFeL, which enable privacy-preserving query analytics and machine learning, respectively.
Biography: Amrita Roy Chowdhury is a CRA/CCC CIFellow at University of California-San Diego, working with Prof. Kamalika Chaudhuri. She graduated with her PhD from University of Wisconsin-Madison and was advised by Prof. Somesh Jha. She completed her Bachelor of Engineering in Computer Science from the Indian Institute of Engineering Science and Technology, Shibpur where she was awarded the President of India Gold Medal. Her work explores the synergy between differential privacy and cryptography through novel algorithms that expose the rich interconnections between the two areas, both in theory and practice. She has been recognized as a Rising Star in EECS in 2020 and 2021, and a Facebook Fellowship finalist, 2021. She has also been selected as a UChicago Rising Star in Data Science, 2021.
Host: Dr. Viktor Prasanna, prasanna@usc.edu
More Info: https://usc.zoom.us/j/94200520726?pwd=U1ZSd3VUVzIrMVI3QUE3d25hVzIvZz09
Webcast: https://usc.zoom.us/j/94200520726?pwd=U1ZSd3VUVzIrMVI3QUE3d25hVzIvZz09More Information: 2024.03.28 ECE-S Seminar - Amrita Roy Chowdhury.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248
WebCast Link: https://usc.zoom.us/j/94200520726?pwd=U1ZSd3VUVzIrMVI3QUE3d25hVzIvZz09
Audiences: Everyone Is Invited
Contact: Miki Arlen
Event Link: https://usc.zoom.us/j/94200520726?pwd=U1ZSd3VUVzIrMVI3QUE3d25hVzIvZz09
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NL Seminar-Informative Example Selection for In-Context Learning
Thu, Mar 28, 2024 @ 11:00 AM - 12:00 PM
Information Sciences Institute
Conferences, Lectures, & Seminars
Speaker: Shivanshu Gupta, UCI
Talk Title: Informative Example Selection for In-Context Learning
Series: NL Seminar
Abstract: 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 inform us at (nlg-seminar-host(at)isi.edu) beforehand so we’ll be aware of your attendance and let you in. In-person attendance will be permitted for USC/ISI faculty, staff, students only. Open to the public virtually via the zoom link. For more information on the NL Seminar series and upcoming talks, please visit: https://nlg.isi.edu/nl-seminar/ In-context Learning (ICL) uses large language models (LLMs) for new tasks by conditioning them on prompts comprising a few task examples. With the rise of LLMs that are intractable to train or hidden behind APIs, the importance of such a training-free interface cannot be overstated. However, ICL is known to be critically sensitive to the choice of in-context examples. Despite this, the standard approach for selecting in-context examples remains to use general-purpose retrievers due to the limited effectiveness and training requirements of prior approaches. In this talk, I'll posit that good in-context examples demonstrate the salient information necessary to solve a given test input. I'll present efficient approaches for selecting such examples, with a special focus on preserving the training-free ICL pipeline. Through results with a wide range of tasks and LLMs, I'll demonstrate that selecting informative examples can indeed yield superior ICL performance.
Biography: Shivanshu Gupta is a Computer Science Ph.D. Candidate at the University of California Irvine, advised by Sameer Singh. Prior to this, he was a Research Fellow at LinkedIn and Microsoft Research India, and completed his B.Tech. and M.Tech. in Computer Science at IIT Delhi. His primary research interests are systematic generalization, in-context learning, and multi-step reasoning capabilities of large language models. If speaker approves to be recorded for this NL Seminar talk, it will be posted on the 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://www.youtube.com/watch?v=Vqvy4XIOtcELocation: Information Science Institute (ISI) - Virtual and ISI-Conf Rm#689
WebCast Link: https://www.youtube.com/watch?v=Vqvy4XIOtcE
Audiences: Everyone Is Invited
Contact: Pete Zamar
Event Link: https://nlg.isi.edu/nl-seminar/
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PhD Dissertation Defense - Chuizheng Meng
Thu, Mar 28, 2024 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Committee Members: Yan Liu (Chair), Willie Neiswanger, and Assad A Oberai (external member)
Title: Trustworthy Spatiotemporal Prediction Models
Abstract: With the great success of data-driven machine learning methods, concerns with the trustworthiness of machine learning models have been emerging in recent years. From the modeling perspective, the lack of trustworthiness amplifies the effect of insufficient training data. Purely data-driven models without constraints from domain knowledge tend to suffer from over-fitting and losing the generalizability of unseen data. Meanwhile, concerns with data privacy further obstruct the availability of data from more providers. On the application side, the absence of trustworthiness hinders the application of data-driven methods in domains such as spatiotemporal forecasting, which involves data from critical applications including traffic, climate, and energy. My dissertation constructs spatiotemporal prediction models with enhanced trustworthiness from both the model and the data aspects. For model trustworthiness, the dissertation focuses on improving the generalizability of models via the integration of physics knowledge. For data trustworthiness, the proposal proposes a spatiotemporal forecasting model in the federated learning context, where data in a network of nodes is generated locally on each node and remains decentralized. Furthermore, the dissertation amalgamates the trustworthiness from both aspects and combines the generalizability of knowledge-informed models with the privacy preservation of federated learning for spatiotemporal modeling.Location: Waite Phillips Hall Of Education (WPH) - B26
Audiences: Everyone Is Invited
Contact: Chuizheng Meng
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ECE-EP Faculty Candidate - Srujan Meesala, Thursday, March 28th at 2pm in EEB 248
Thu, Mar 28, 2024 @ 02:00 PM - 03:30 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Srujan Meesala, Caltech
Talk Title: Generating quantum correlations between light and Microwaves with a chip-scale device
Series: ECE-EP Seminar
Abstract: Experimental capabilities in modern quantum science and engineering allow the control of quantum states in a variety of solid-state systems such as superconducting circuits, atomic-scale defect centers, and chip-scale optical and acoustic structures. Controlling interactions between physically different qubits across such platforms is a frontier in the quest to build quantum hardware at scale and to probe the coherence limits of solid-state devices. I will present recent progress on constructing a quantum interconnect between superconducting qubits and optical photons. By integrating specially engineered optical, mechanical, and superconducting microwave components in a chip-scale transducer, we made a photon pair source and used it to generate single optical and microwave photons in entangled pairs. Such devices can be used to connect superconducting qubits in distant cryogenic nodes using room-temperature fiber-optic communication channels. I will discuss open challenges with such transducers and a few near-term routes to address them. I will conclude with results from a different set of experiments where we used nanomechanical devices to control the electronic structure and coherence limits of a spin qubit in an atomic-scale defect center.
Biography: Srujan Meesala is an IQIM Postdoctoral Scholar at Caltech in Oskar Painter's research group. He received his PhD from Harvard where he worked in Marko Loncar's research group.
Host: ECE-EP
More Information: Srujan Meesala Seminar Announcement.pdf
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Marilyn Poplawski
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CS Colloquium: Ram Sundara Raman - Global Investigation of Network Connection Tampering
Thu, Mar 28, 2024 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ram Sundara Raman, University of Michigan
Talk Title: Global Investigation of Network Connection Tampering
Abstract: As the Internet's user base and criticality of online services continue to expand daily, powerful adversaries like Internet censors are increasingly monitoring and restricting Internet traffic. These adversaries, powered by advanced network technology, perform large-scale connection tampering attacks seeking to prevent users from accessing specific online content, compromising Internet availability and integrity. In recent years, we have witnessed recurring censorship events affecting Internet users globally, with far-reaching social, financial, and psychological consequences, making them important to study. However, characterizing tampering attacks at the global scale is an extremely challenging problem, given intentionally opaque practices by adversaries, varying tampering mechanisms and policies across networks, evolving environments, sparse ground truth, and safety risks in collecting data. In this talk, I will describe my research on building empirical methods to characterize connection tampering globally and investigate the network technology enabling tampering. First, I will describe a modular design for the Censored Planet Observatory that enables it to remotely and sustainably measure Internet censorship longitudinally in more than 200 countries. I will introduce time series analysis methods to detect key censorship events in longitudinal Censored Planet data, and reveal global censorship trends. I will also briefly describe methods to detect connection tampering using purely passive data. Next, I will introduce novel network measurement methods for locating and examining network devices that perform censorship. Finally, I will describe exciting ongoing and future research directions, such as building intelligent measurement platforms. This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Ram Sundara Raman is a PhD candidate in Computer Science and Engineering at the University of Michigan, advised by Prof. Roya Ensafi. His research lies in the intersection of computer security, privacy, and networking, employing empirical methods to study large-scale Internet attacks. Ram has been recognized as a Rising Star at the Workshop on Free and Open Communications on the Internet (FOCI), and was awarded the IRTF Applied Networking Research Prize in 2023. His work has helped produce one of the biggest active censorship measurement platforms, the Censored Planet Observatory, and has helped prevent large-scale attacks on end-to-end encryption.
Host: Jyo Deshmukh
Location: Ronald Tutor Hall of Engineering (RTH) - 109
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
