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Events for March 28, 2024
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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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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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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
