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Events for April 02, 2025
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EiS Communications Hub - Tutoring for Engineering Ph.D. StudentsWed, Apr 02, 2025 @ 10:00 AM - 12:00 PM
Viterbi School of Engineering Student Affairs
Workshops & Infosessions
Viterbi Ph.D. students are invited to drop by the Hub for instruction on their writing and speaking tasks! All tutoring is one-on-one and conducted by Viterbi faculty.
Location: Ronald Tutor Hall of Engineering (RTH) - 222A
Audiences: Viterbi Ph.D. Students
Contact: Helen Choi
Event Link: https://sites.google.com/usc.edu/eishub/home
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
CS Colloquium: Amir Houmansadr (UMass Amherst) - The Road Not Taken: Towards Proactive Research on Internet Censorship
Wed, Apr 02, 2025 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Amir Houmansadr, UMass Amherst
Talk Title: The Road Not Taken: Towards Proactive Research on Internet Censorship
Abstract: Internet censorship poses a major threat to free speech and open access to information worldwide. While numerous tools exist to bypass censorship, they often fail to provide censored users with effective and reliable solutions. A key reason for this inefficacy is the reactive nature of circumvention tool development—developers modify their tools in response to censorship tactics, allowing censors to maintain the upper hand in this ongoing arms race. In this talk, I will make the case for a proactive approach to censorship circumvention research and share insights from our ongoing efforts towards proactive circumvention.
As AI continues to transform Internet services, I argue that the future of Internet security is inextricably linked to AI. I will also outline my vision for safeguarding online freedom and security in the age of AI, exploring both its potential and the challenges it presents.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Amir Houmansadr is an Associate Professor of computer science at UMass Amherst. He received his Ph.D. from the University of Illinois at Urbana-Champaign, and was a postdoctoral researcher at the University of Texas at Austin. Amir is broadly interested in the security and privacy of networked/AI systems. To that end, he designs and deploys privacy-enhancing technologies, analyzes network protocols and services (e.g., messaging apps and machine learning APIs) for privacy leakage, and performs theoretical analysis to derive bounds on privacy (e.g., using game theory and information theory). Amir has received several awards including the 2013 IEEE S&P Best Practical Paper Award, a 2015 Google Faculty Research Award, a 2016 NSF CAREER Award, a 2022 DARPA Young Faculty Award (YFA), the 2023 Best Practical Paper Award from the FOCI Community, the first place at CSAW 2023 Applied Research Competition, a Distinguished Paper Award from ACM CCS 2023, a 2024 Applied Networking Research Prize (ANRP), and a 2024 DARPA Directors Award.
Host: Harsha V. Madhyastha
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone (USC) is invited
Contact: CS Faculty Affairs
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor. -
PhD Dissertation Defense - Hanchen Xie
Wed, Apr 02, 2025 @ 12:00 PM - 02:00 PM
Thomas Lord Department of Computer Science
University Calendar
Dissertation Title: Mitigating Environment Misalignment And Discovering Intrinsic Relations Via Symbolic Alignment
Date and Time: April 2, 12 pm to 2 pm.
Location: RTH 217
Committee: Yue Wang (Chair), Wael Abd-Almageed, Aram Galstyan, Emilio Ferrara, Peter Beerel
Abstract: Deep learning models have achieved remarkable success on various computer vision tasks. Modern state-of-the-art methods can not only recognize the visual appearance of objects but also discover intrinsic relations of objects (e.g., dynamics or causal relations). However, collecting sufficient training data for the intrinsic relations can be expensive or infeasible in many scenarios, such as car incident videos in the real world. As an alternative, one can generate data in a different environment, such as synthetic data, that depicts the same intrinsic relations. Yet, end-to-end models may suffer from environment misalignment challenges, such as visual domain or environment context shift, so the model generality is limited. To mitigate such misalignment challenges, we propose symbolic alignment, a novel learning strategy that utilizes a common symbolic space to align various environments. We first conduct a case study on dynamics prediction to reveal the environment misalignment challenges on our proposed datasets. Next, to obtain insight into the challenge, we provide an investigation of the implicit position encoding in the dynamics prediction model. Then, we present a learning framework that separates the learning of appearance recognition and dynamics relations discovery to improve the generality of the dynamics prediction model. Then, we generalize the symbolic alignment strategy and introduce a novel framework, Look, Learn, and Leverage L3. L3 decomposes the learning process into three distinct phases and achieves promising results on three intrinsic relations discovery tasks. Finally, we extend the environment misalignment discussion to video classification and demonstrate the potential of symbolic alignment to mitigate the video content inconsistency between training and inference.Location: Ronald Tutor Hall of Engineering (RTH) - 217
Audiences: Everyone Is Invited
Contact: Hanchen Xie
Event Link: https://usc.zoom.us/j/95803531086?pwd=LwnaIMsMv44jIIkvvlUEXD3gAbqb2N.1
This event is open to all eligible individuals. USC Viterbi operates all of its activities consistent with the University's Notice of Non-Discrimination. Eligibility is not determined based on race, sex, ethnicity, sexual orientation, or any other prohibited factor.
