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University Calendar
Events for March
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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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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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PhD Thesis Proposal - Yuzhong Huang
Tue, Mar 19, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
University Calendar
Committee Members: Fred Morstatter (Chair), Yue Wang, Aiichiro Nakano, & Antonio Ortega
Date & Time: Tuesday, March 19, 2024, 11:00 a.m. - 12:00 p.m. (PST) - PHE 325
Title: Explicit Control in the Understanding and Generation of 3D world
Abstract: Understanding and recreating our living environment has been a key topic in scientific research, ranging from virtual reality, autonomous driving, and generative AI tools. Recent advancements have significantly improved machine model’s capability to recognize and generate visually similar 3D objects. However, existing approaches often lack explicit control mechanisms, limiting their adaptability and interpretability. This thesis proposal addresses this gap by focusing on three crucial aspects: (1) Explicit control in understanding 3D worlds, achieved through the imposition of planar priors and plane-splatting volume rendering method. (2) Explicit control in generating 3D worlds, enabled by an orientation-conditioned diffusion model. (3) Explicit control in modifying 3D objects, enabled by projecting text-guided 2D segmentation map onto 3D models. These advancements pave the way for more intuitive and precise manipulation of 3D environments.
Zoom Link: https://usc.zoom.us/j/99330288526Location: Charles Lee Powell Hall (PHE) - 325
WebCast Link: https://usc.zoom.us/j/99330288526
Audiences: Everyone Is Invited
Contact: Thomas Lord Department of Computer Science
Event Link: https://usc.zoom.us/j/99330288526
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PhD Thesis Defense - Kushal Chawla
Thu, Mar 21, 2024 @ 01:30 PM - 03:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Defense - Kushal Chawla
Title: Computational Foundations for Mixed-Motive Human-Machine Dialogue
Committee Members: Gale Lucas (Chair), Jonathan Gratch, Jonathan May, Peter Kim, Maja Mataric
Abstract: Success in a mixed-motive interaction demands a balance between self-serving and other-serving behaviors. For instance, in a typical negotiation, a player must balance maximizing their own goals with the goals of their partner so as to come to an agreement. If the player asks for too much, this can push the partner to walk away without an agreement, hence, hurting the outcomes for all the parties involved. Such interactions are ubiquitous in everyday life, from deciding who performs household chores to customer support and high-stakes business deals. Consequently, AI tools capable of comprehending and participating in such mixed-motive or other social influence interactions (such as argumentation or therapy) find broad applications in pedagogy and conversational AI.
In this thesis, we present our foundational work for enabling mixed-motive human-machine dialogue. I will discuss our progress in three key areas: 1) The design of a novel task and dataset of grounded human-human negotiations that has fueled our investigations into the impact of emotion expression and linguistic strategies, 2) Techniques for mixed motive dialogue systems that learn to strike a balance between self and partner interests, and 3) Promoting a research community for dedicated efforts and discussion in this area.
https://usc.zoom.us/j/96411089883?pwd=WDNuMjF1NDNTTXV5cDdGaWJzOG9Gdz09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 110
Audiences: Everyone Is Invited
Contact: CS Events
Event Link: https://usc.zoom.us/j/96411089883?pwd=WDNuMjF1NDNTTXV5cDdGaWJzOG9Gdz09
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PhD Dissertation Defense - Arvin Hekmati
Thu, Mar 21, 2024 @ 02:30 PM - 04:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Dissertation Defense - Arvin Hekmati
Committee: Prof. Bhaskar Krishnamachari (Chair), Prof. Cauligi Raghavendra, and Prof. Aiichiro Nakano
Title: AI-Enabled DDoS Attack Detection in IoT Systems
Abstract:
"In this thesis, we develop AI-enabled mechanisms for detecting Distributed Denial of Service (DDoS) attacks in Internet of Things (IoT) systems. We introduce a novel, tunable DDoS attack model that emulates benign IoT device behavior using a truncated Cauchy distribution. We investigate these futuristic DDoS attacks that use large numbers of IoT devices and camouflage their attack by having each node transmit at a volume typical of benign traffic. We propose innovative correlation-aware, learning-based frameworks that leverage IoT node correlation data for enhanced detection accuracy. We extensively analyze the proposed architectures by evaluating five different neural network models trained on a dataset derived from a 4060-node real-world IoT system. We observe that long short-term memory (LSTM) and a transformer-based model, in conjunction with the architectures that use correlation information of the IoT nodes, provide higher detection performance than the other models and architectures, especially when the attacker camouflages itself by following benign traffic distribution on each IoT node. We evaluated our findings through practical implementation on a Raspberry Pi-based testbed. In order to address the challenge of leveraging massive IoT device arrays for DDoS attacks, we introduce heuristic solutions for selective correlation information sharing among IoT devices. To overcome the challenge of fixed input limitations in conventional machine learning, we propose a model based on the Graph Convolutional Network (GCN) to manage incomplete data in IoT devices caused by network losses. We introduce various IoT device graph topologies, including Network, Peer-to-Peer, and Hybrid topologies with scenarios of both directed and undirected edges. Our simulations reveal that the Hybrid topology, employing correlation-based peer-to-peer undirected edges, achieves the highest detection performance with at most 2% drop in the performance despite a 50% network connection loss, highlighting the proposed GCN-based model's effectiveness in detecting DDoS attacks under lossy network conditions. Finally, we explore the application of Large Language Models (LLMs) for detecting DDoS attacks and explaining the detection rationale, demonstrating the potential of fine-tuning and few-shot prompt engineering methods to achieve high accuracy and provide insightful detection reasoning."Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: Ellecia Williams
Event Link: https://usc.zoom.us/j/4677088430
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Ph.D. Thesis Defense - Ali Omrani
Mon, Mar 25, 2024 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
Ph.D. Thesis Defense - Ali Omrani
Committee: Morteza Dehghani (Chair), Xiang Ren, Robin Jia, Payam Piray, and Jeffrey Sorensen
Title: Countering Problematic Content in Digital Space: Bias Reduction and Dynamic Content Adaptation
Abstract: Problematic content, such as hate speech, poses a significant challenge to society, leading to discrimination and exclusion while undermining inclusivity and well-being. This thesis proposal outlines my efforts to create adaptable solutions for combating problematic content in digital space through a theory-motivated approach that bridges language technology and social sciences. I will begin by presenting an innovative group-agnostic method for bias mitigation in language models, which is grounded in a deep understanding of stereotyping from social psychology. Subsequently, I will introduce a novel continual learning framework for problematic content detection that captures the ever-evolving nature of this issue. Afterward, I discuss my work that extends this framework to multilingual settings, with a specific emphasis on two key aspects: 1. Harnessing cultural diversity for cross-lingual transfer of offensive language detection and 2. Investigating the challenges posed by disparities in data quality across various languages.Date and Time: March 25th, 2:00 PM - 4:00 PM
Location: Room 266, USC Brain and Creativity Institute 605, 3620 McClintock Ave, Los Angeles, CA 90089
Location: Dornsife Neuroscience Imaging Center (DNI) - 266
Audiences: Everyone Is Invited
Contact: CS Events
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PhD Dissertation Defense - Aniruddh Puranic
Tue, Mar 26, 2024 @ 03:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Dissertation Defense - Aniruddh Puranic Committee: Jyotirmoy V. Deshmukh (Chair), Gaurav Sukhatme, Stefanos Nikolaidis, and Stephen Tu Title: Sample-Efficient and Robust Neurosymbolic Learning from Demonstrations Abstract: Learning-from-demonstrations (LfD) is a popular paradigm to obtain effective robot control policies for complex tasks via reinforcement learning (RL) without the need to explicitly design reward functions. However, it is susceptible to imperfections in demonstrations and also raises concerns of safety and interpretability in the learned control policies. To address these issues, this thesis develops a neurosymbolic learning framework which is a hybrid method that integrates neural network-based learning with symbolic (e.g., rule, logic, graph) reasoning to leverage the strengths of both approaches. Specifically, this framework uses Signal Temporal Logic (STL) to express high-level robotic tasks and its quantitative semantics to evaluate and rank the quality of demonstrations. Temporal logic-based specifications allow us to create non-Markovian rewards and are also capable of defining interesting causal dependencies between tasks such as sequential task specifications. This dissertation presents the LfD-STL framework that learns from even suboptimal/imperfect demonstrations and STL specifications to infer reward functions; these reward functions can then be used by reinforcement learning algorithms to obtain control policies. Experimental evaluations on several diverse set of environments show that the additional information in the form of formally specified task objectives allows the framework to outperform prior state-of-the-art LfD methods. Many real-world robotic tasks consist of multiple objectives (specifications), some of which may be inherently competitive, thus prompting the need for deliberate trade-offs. This dissertation then further extends the LfD-STL framework by a developing metric - performance graph - which is a directed graph that utilizes the quality of demonstrations to provide intuitive explanations about the performance and trade-offs of demonstrated behaviors. This performance graph also offers concise insights into the learning process of the RL agent, thereby enhancing interpretability, as corroborated by a user study. Finally, the thesis discusses how the performance graphs can be used as an optimization objective to guide RL agents to potentially learn policies that perform better than the (imperfect) demonstrators via apprenticeship learning (AL). The theoretical machinery developed for the AL-STL framework examines the guarantees on safety and performance of RL agents. https://usc.zoom.us/j/98964159897?pwd=a2ljaGNEOGcvMkl1WU9yZENPc0M1dz09
Location: Ronald Tutor Hall of Engineering (RTH) - 306
Audiences: Everyone Is Invited
Contact: Aniruddh Puranic
Event Link: https://usc.zoom.us/j/98964159897?pwd=a2ljaGNEOGcvMkl1WU9yZENPc0M1dz09
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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
