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Events for the 2nd week of March
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CS Colloquium: Emily Tseng (Cornell University) - Digital Safety and Security for Survivors of Technology-Mediated Harms
Mon, Mar 04, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Emily Tseng, Cornell University
Talk Title: Digital Safety and Security for Survivors of Technology-Mediated Harms
Series: Computer Science Colloquium
Abstract: Platforms, devices, and algorithms are increasingly weaponized to control and harass the most vulnerable among us. Some of these harms occur at the individual and interpersonal level: for example, abusers in intimate partner violence (IPV) use smartphones and social media to surveil and stalk their victims. Others are more subtle, at the level of social structure: for example, in organizations, workplace technologies can inadvertently scaffold exploitative labor practices. This talk will discuss my research (1) investigating these harms via online measurement studies, (2) building interventions to directly assist survivors with their security and privacy; and (3) instrumenting these interventions as observatories, to enable scientific research into new types of harms as attackers and technologies evolve. I will close by sharing my vision for centering inclusion and equity in digital safety, security and privacy, towards brighter technological futures for us all.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Emily Tseng is a PhD candidate in Information Science at Cornell University. Her research develops the systems, interventions, and design principles we need to make digital technology safe and affirming for everyone. Emily’s work has been published at top-tier venues in human-computer interaction (ACM CHI, CSCW) and computer security and privacy (USENIX Security, IEEE Oakland). For 5 years, she has worked as a researcher-practitioner with the Clinic to End Tech Abuse, where her work has enabled specialized security services for over 500 survivors of intimate partner violence (IPV). Emily is the recipient of a Microsoft Research PhD Fellowship, Rising Stars in EECS, Best Paper Awards at CHI, CSCW, and USENIX Security, and third place in the Internet Defense Prize. She has interned at Google and with the Social Media Collective at Microsoft Research. She holds a B.A. from Princeton University.
Host: Jesse Thomason
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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CS Colloquium: Angelina Wang (Princeton University) - Operationalizing Responsible Machine Learning: From Equality Towards Equity
Tue, Mar 05, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Angelina Wang, Princeton University
Talk Title: Operationalizing Responsible Machine Learning: From Equality Towards Equity
Abstract: With the widespread proliferation of machine learning, there arises both the opportunity for societal benefit as well as the risk of harm. Approaching responsible machine learning is challenging because technical approaches may prioritize a mathematical definition of fairness that correlates poorly to real-world constructs of fairness due to too many layers of abstraction. Conversely, social approaches that engage with prescriptive theories may produce findings that are too abstract to effectively translate into practice. In my research, I bridge these approaches and utilize social implications to guide technical work. I will discuss three research directions that show how, despite the technically convenient approach of considering equality acontextually, a stronger engagement with societal context allows us to operationalize a more equitable formulation. First, I will introduce a dataset tool that we developed to analyze complex, socially-grounded forms of visual bias. Then, I will provide empirical evidence to support how we should incorporate societal context in bringing intersectionality into machine learning. Finally, I will discuss how in the excitement of using LLMs for tasks like human participant replacement, we have neglected to consider the importance of human positionality. Overall, I will explore how we can expand a narrow focus on equality in responsible machine learning to encompass a broader understanding of equity that substantively engages with societal context.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Angelina Wang is a Computer Science PhD student at Princeton University advised by Olga Russakovsky. Her research is in the area of machine learning fairness and algorithmic bias. She has been recognized by the NSF GRFP, EECS Rising Stars, Siebel Scholarship, and Microsoft AI & Society Fellowship. She has published in top machine learning (ICML, AAAI), computer vision (ICCV, IJCV), interdisciplinary (Big Data & Society), and responsible computing (FAccT, JRC) venues, including spotlight and oral presentations. Previously, she has interned with Microsoft Research and Arthur AI, and received a B.S. in Electrical Engineering and Computer Science from UC Berkeley.
Host: Bistra Dilkina
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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PhD Thesis Proposal - Shao-Hung Chan
Tue, Mar 05, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Shao-Hung Chan
Committee members: Sven Koenig (chair), T.K. Satish Kumar, Lars Lindemann, John Carlsson, and Daniel Harabor
Title: Flex Distribution for Bounded-Suboptimal Multi-Agent Path Finding
Time: Mar. 5th, 2:00 PM - 3:00 PM
Location: EEB 349
Abstract:
Multi-Agent Path Finding (MAPF) is the problem of finding collision-free paths for multiple agents that minimize the sum of path costs. Explicit Estimation Conflict-Based Search (EECBS) is a leading two-level algorithm that solves MAPF bounded-suboptimally, i.e., within some factor w away from the minimum sum of path costs C*. It uses Focal Search to find bounded-suboptimal paths on the low level and Explicit Estimation Search (EES) to resolve collisions on the high level. To solve MAPF bounded-suboptimally, EES keeps track of a lower bound LB on C* to find paths whose sum of path costs is at most w times LB. However, the costs of many paths are often much smaller than w times their minimum path costs, meaning that the sum of path costs is much smaller than w times C*. Thus, in this proposal, we hypothesize that one can improve the efficiency of EECBS via Flex Distribution. That is, one can use the flex of the path costs (that relaxes the requirement to find bounded-suboptimal paths on the low level) to reduce the number of collisions that need to be resolved on the high level while still guaranteeing to solve MAPF bounded suboptimally. We also discuss the limitations of Flex Distribution and propose some techniques to overcome them.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 349
Audiences: Everyone Is Invited
Contact: CS Events
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CS Colloquium: Chang Xiao (Adobe Research) - Augmented Interaction Between Physical and Digital Realm
Wed, Mar 06, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Chang Xiao, Adobe Research
Talk Title: Augmented Interaction Between Physical and Digital Realm
Series: Computer Science Colloquium
Abstract: Today's computing devices, including mobile phones, wearable devices, and VR/AR headsets, have become increasingly powerful and accessible to almost everyone. They offer a direct and immersive interaction with digital worlds. But what if we could use these devices to access interactive physical worlds as well, expanding our interaction space and unlocking greater interactive potential? In this talk, I will discuss our work on integrating both physical and digital systems to create a new computing environment. Leveraging techniques from AI/ML, Computer Vision, and Computational Design, we propose several interactive systems and sensing techniques that provide users with unified, low-cost, tangible, and intuitive experiences. These approaches unlock the potential of using the physical environment as computer interfaces in the era of Extended Reality (XR) and spatial computing, bridging the gap between physical and digital spaces.
This lecture satisfies requirements for CSCI 591: Research Colloquium
Biography: Chang Xiao is currently a Research Scientist at Adobe Research. He obtained his PhD from Columbia University in 2021. His broad interests lie at the intersection of HCI, AI/ML, and AR/VR, with a special focus on leveraging AI/ML to develop novel interaction and sensing techniques. His work has been published in a wide spectrum of top computer science venues, including CHI, UIST, SIGGRAPH, NeurIPS, CVPR, and ICLR. His research has gained impact beyond academia, having been successfully integrated into multiple Adobe products and receiving widespread attention, including media interviews and coverage by CNN, Adweek, CACM, and IEEE Spectrum. During his PhD studies, he received the Snap Research Fellowship and the Cheung-Kong Innovation Doctoral Fellowship.
Host: Heather Culbertson
Location: Olin Hall of Engineering (OHE) - 132
Audiences: Everyone Is Invited
Contact: CS Faculty Affairs
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PhD Dissertation Defense - Sina Shaham
Wed, Mar 06, 2024 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Dissertation Defense - Sina Shaham
Committee: Prof. Bhaskar Krishnamachari, Prof. Cyrus Shahabi, Prof. Cauligi Raghavendra
Title: Responsible AI in SpatioTemporal Data Processing
Abstract: In this presentation, we systematically investigate the design and development of algorithms to improve privacy and fairness in the processing of spatio-temporal data. Beginning with an essential background introduction and a review of cutting-edge advancements, the discussion progresses to introduce a novel algorithm for safeguarding privacy in the dissemination of Origin-Destination (OD) Matrices. This algorithm, rooted in Differential Privacy (DP) principles, aims to protect user privacy during the collection and sharing of OD-matrices in 2D and higher dimensions. Subsequently, our focus shifts to the domain of user energy consumption, where we develop a methodology that ensures user privacy when disclosing electricity time series to third parties and entities that may not be fully trusted. Following this, we propose an incentive-based program aimed at balancing electricity demand, taking into account socio-economic family attributes and ensuring fair treatment. Through comprehensive evaluations, the presentation demonstrates the progress made over previous works and also sheds light on potential areas for future studies, particularly in the realm of responsible handling of complex spatio-temporal data.
Zoom Link: https://usc.zoom.us/j/98092705100Location: Hughes Aircraft Electrical Engineering Center (EEB) - 539
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/98092705100
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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
