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SUNMONTUEWEDTHUFRISAT
Events for the 4th week of February
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PhD Thesis Proposal - Saghar Talebipour
Tue, Feb 20, 2024 @ 01:30 AM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Saghar Talebipour
Committee Members: Nenad Medvidovic (Chair), William G.J. Halfond, Chao Wang, Mukund Raghothaman, Sandeep Gupta
Date: Tuesday, February 20, 2024, 1:30 p.m. - 3:00 p.m. Location: EEB 349
Title: Automated Usage-based Mobile Application Testing via Artifact Reuse
Abstract: Writing and maintaining UI tests for mobile applications is both time-consuming and tedious. While decades of research have led to automated methods for UI test generation, these methods have largely focused on identifying crashes or maximizing code coverage. However, recent studies have emphasized the significance of usage-based tests targeting specific app functionalities and use cases. My research introduces novel automated testing techniques that make use of existing artifacts, such as tests from similar applications or video recordings of app operations. These approaches help us move closer to achieving the goal of automated usage-based testing of mobile applications.Location: Hughes Aircraft Electrical Engineering Center (EEB) - 349
Audiences: Everyone Is Invited
Contact: CS Events
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CS Colloquium - Krishna Kant Chintalapudi (Microsoft Research Redmond) - "Leveling up Next Gen Xbox User Experience with Neural Networks and Sound"
Tue, Feb 20, 2024 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Krishna Kant Chintalapudi, Principal Researcher, Microsoft Research Redmond (MSR)
Talk Title: Leveling up Next Gen Xbox User Experience with Neural Networks and Sound
Abstract: This talk presents two groundbreaking innovations in enhancing the gaming experience on Next-Gen Xbox platforms - ADR-X (NSDI 2024) and Ekho (SIGCOMM 2023). ADR-X, is a neural network-assisted wireless link rate adaptation technique for compute-constrained embedded gaming devices. It uses a meticulously crafted NN based contextual bandit that leverages existing communication theory domain knowledge. This allows ADR-X to perform at par with state-of-the-art reinforcement learning techniques such as PPO while also running 100× faster. Ekho introduces a novel approach to synchronizing cloud gaming media over the internet - crucial for immersive gameplay. By embedding faint, human-inaudible pseudo-noise markers into game audio and detecting them through player microphones, Ekho accurately measures and compensates for inter-stream delays.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Dr. Krishna Kant Chintalapudi is a Principal Researcher in the Networking Research Group at Microsoft Research Redmond (MSR). His research interests span AI/ML, Networking & Systems, Video Analytics, AR/VR and Internet of Things. He has published more than 50 papers in reputed international conferences and journals which have been cited over 8000 times and he holds over 30 patents granted by USPTO. Krishna graduated from the University of Southern California with a Phd in Computer Science in 2006. Prior to joining MSR, Krishna was a Senior Research Engineer at Bosch Research and Technology Center in Palo Alto, CA, USA.
Host: Ramesh Govindan
Location: Hedco Neurosciences Building (HNB) - 107
Audiences: Everyone Is Invited
Contact: CS Events
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CS Colloquium - Pavithra Prabhakar (Kansas State University) - Safety Analysis of AI-enabled Cyber-Physical Systems (CPS): A Formal Approach
Tue, Feb 20, 2024 @ 02:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Prof. Pavithra Prabhakar, Kansas State University
Talk Title: Safety Analysis of AI-enabled Cyber-Physical Systems (CPS): A Formal Approach
Abstract: AI-based components have become an integral part of Cyber-Physical Systems enabling transformative functionalities. With the ubiquitous use of Machine Learning components in perception, control and decision making in safety critical application domains such as automotive and aerospace, rigorous analysis of these systems has become imperative toward real-world deployment. In this talk, we will present a formal approach to verifying the safety of AI-enabled CPS. We consider a closed-loop system consisting of a dynamical system model of the physical plant and a neural network model of the perception/control modules and analyze the safety of this system through reachable set computation.
One of the main challenges with reachable set computation of neural network-controlled CPS is the scalability of the methods to large networks and complex dynamics. We present a novel abstraction technique for neural network size reduction that provides soundness guarantees for safety analysis and indicates a promising direction for scalable analysis of the closed-loop system. Specifically, our abstraction consists of constructing a simpler neural network with fewer neurons, albeit with interval weights called interval neural network (INN), which over-approximates the output range of the given neural network. We present two methods for computing the output range analysis problem on the INNs, one by reducing it to solving a mixed integer linear programming problem, and the other a symbolic computation method using a novel data structure called the interval star set. Our experimental results highlight the trade-off between the computation time and the precision of the computed output set. We will discuss other foundational questions on neural network size reduction by exploring the notion of equivalence and approximate equivalence. We will conclude by pointing to ongoing work on incorporating a camera model along with a neural network for perception in the closed-loop system framework.
This lecture satisfies requirements for CSCI 591: Research Colloquium.
Biography: Pavithra Prabhakar is professor in the department of computer science, and the Peggy and Gary Edwards Chair in Engineering at Kansas State University. She is currently serving the National Science Foundation as a Program Director in the Software and Hardware Foundations Cluster in the Computer and Information Science and Engineering Directorate, where she manages formal methods and verification portfolio. Specifically, she leads the Formal Methods in the Field (FMitF) program, has been a founding program director for the Safe Learning Enabled Systems (SLES) program and is a cognizant program director for the Foundations of Robotics Research (FRR) and the Cyber-Physical Systems (CPS) program.
She obtained her doctorate in computer science and a master's degree in applied mathematics from the University of Illinois at Urbana-Champaign, followed by a CMI postdoctoral fellowship at the California Institute of Technology. Prior to coming to K-State, she spent four years at the IMDEA Software Institute in Spain as a tenure-track assistant professor. She is the recipient of a Marie Curie Career Integration Grant from the European Union (2014), an NSF CAREER Award (2016), an ONR Young Investigator Award (2017), NITW distinguished young alumnus award (2021), and an Amazon Research Award (2022).
Host: Jyotirmoy Deshmukh
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: CS Events
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PhD Thesis Proposal - Qinyi Luo
Wed, Feb 21, 2024 @ 11:00 AM - 12:30 PM
Thomas Lord Department of Computer Science
University Calendar
PhD Thesis Proposal - Qinyi Luo
Title: High-Performance Heterogeneity-Aware Distributed Machine Learning Model Training
Committee members: Xuehai Qian (co-chair), Viktor Prasanna (co-chair), Ramesh Govindan, Chao Wang, Salman Avestimehr
Abstract: The increasing size of machine learning models and the ever-growing amount of data result in days or even weeks of time required to train a machine learning model. To accelerate training, distributed training with parallel stochastic gradient descent is widely adopted as the go-to training method. This thesis proposal targets four challenges in distributed training: (1) performance degradation caused by large amount of data transfer among parallel workers, (2) heterogeneous computation and communication capacities in the training devices, i.e., the straggler problem, (3) huge memory consumption during training caused by huge model sizes, and (4) automatic selection of parallelization strategies. The proposal first introduces our work in decentralized training, including system support and algorithmic innovation that strengthen tolerance against stragglers in data-parallel training. Then, an adaptive during-training model compression technique is proposed to reduce the memory consumption of training huge recommender models. In the end, in the aspect of automatic parallelization of training workloads, a novel unified representation of parallelization strategies is proposed, as well as a search algorithm that selects superior parallel settings in the vast search space, and preliminary findings are discussed.
Date and time: Feb 21 11am-12:30pm
Location: EEB 110
Zoom link: https://usc.zoom.us/j/97299158202?pwd=bVlnRVFhTjJlZjVCY1hVNy9yWWE1UT09Location: Hughes Aircraft Electrical Engineering Center (EEB) - 110
Audiences: Everyone Is Invited
Contact: CS Events
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Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute for Electrical & Computer Engineering Joint Seminar Series: Dengwang Tang (USC)
Thu, Feb 22, 2024 @ 10:00 AM - 11:00 AM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dengwang Tang, University of Southern California
Talk Title: Informed Posterior Sampling Based Algorithms for Markov Decision Processes
Series: Center of Autonomy and AI, Center for Cyber-Physical Systems and the Internet of Things, and Ming Hsieh Institute for Electrical & Computer Engineering Joint Seminar Series
Abstract: The traditional paradigm of RL often features an agent who learns to control the system only through interaction. However, such a paradigm can be impractical since
learning can be very slow. In many engineering applications, there's often an offline dataset available before the application of the online learning algorithm. We proposed
the informed posterior sampling-based reinforcement learning (iPSRL) to use offline datasets to bootstrap online RL algorithms in both episodic and continuing MDP
learning problems. In this algorithm, the learning agent forms an informed prior with the offline data along with the knowledge about the offline policy that generated the data.
This informed prior is then used to initiate the posterior sampling procedure. Through a novel prior-dependent regret analysis of the posterior sampling procedure, we showed
that when the offline data is informative enough, the iPSRL algorithm can significantly reduce the learning regret compared to the baseline. Based on iPSRL, we then
proposed the more practical iRLSVI algorithm and we showed that in episodic MDP learning problems, it can significantly reduce regret via empirical results.
Biography: Dengwang Tang is currently a postdoctoral researcher at University of Southern California. He obtained his B.S.E in Computer Engineering from University of Michigan,
Ann Arbor in 2016. He earned his Ph.D. in Electrical and Computer Engineering (2021), M.S. in Mathematics (2021), and M.S. in Electrical and Computer Engineering (2018) all
from University of Michigan, Ann Arbor. Before joining USC, he was a postdoctoral researcher at University of California, Berkeley. His research interests involve control
and learning algorithms in stochastic dynamic systems, multi-agent systems, queuing theory, and dynamic games.
Host: Pierluigi Nuzzo
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132
Audiences: Everyone Is Invited
Contact: CS Events
