USC - Viterbi School of Engineering (2024)

DEN@Viterbi - Online Graduate Engineering Virtual Information Session

Join USC Viterbi School of Engineering for a virtual information session via WebEx, providing an introduction to DEN@Viterbi, our top-ranked online delivery system. Discover the 40+ graduate engineering and computer science programs available entirely online. Attendees will have the opportunity to connect directly with USC Viterbi representatives during the session to discuss the admission process, program details, and the benefits of online delivery.

WebCast Link: https://uscviterbi.webex.com/weblink/register/rbb9d476a6e52c389b11625be33406157

Audiences: Everyone Is Invited

Contact:

Event Link: https://uscviterbi.webex.com/weblink/register/rbb9d476a6e52c389b11625be33406157

CSC/CommNetS-MHI Seminar: Rebbecca Thien

Speaker: Dr. Rebbecca Thien, PhD, May 2024 | Australia National University

Talk Title: Physical realizability and coherent LQG control of linear quantum systems

Series: CSC/CommNetS-MHI Seminar Series

Abstract: A linear quantum system is a special class of quantum system whose dynamics are described by the laws of quantum mechanics where quantum mechanics serves as a platform for comprehending and explaining the workings of the universe at the atomic scale. Control problems in the quantum domain are often more challenging compared to their classical counterparts, primarily due to the additional constraints imposed by quantum mechanics. A linear quantum system generally need not correspond to a physically meaningful system unless it satisfies some additional constraints which then a quantum system will be termed as a physically realizable quantum system. One way to implement a linear time-invariant (LTI) system as a physically realizable system is to include additional quantum vacuum noise channels. The presence of quantum vacuum noise channels in the controller places limits on the performance. Hence it is desirable to minimize the number (or effect) of these noises.
The first part of this talk is to improve current approaches for implementing physically realizable quantum systems. In this context, we present an optimal method to implement a strictly proper LTI system as a physically realizable quantum system. This method focuses on the extent to which the additional quantum noise affects the system output. We also give a necessary and sufficient condition for when a quantum system corresponding to a given LTI controller can be made physically realizable in the presence of both direct feedthrough quantum vacuum noise and additional quantum vacuum noise such that the additional quantum noise does not affect the controller output. Additionally, we give a frequency domain condition to physically realize a given transfer function matrix using only direct feedthrough quantum noise.
Coherent quantum control is a unique feedback control paradigm with no counterpart in classical control systems. Physical realizability and coherent quantum control are closely related concepts since the condition for a quantum controller to be considered coherent is that the controller must be physically realizable. The second part of this talk considers the quantum equalization problem. We have proposed a method to find a physically realizable suboptimal coherent linear quadratic Gaussian (LQG) controller that minimizes a cost function related to the system equalization error. We have implemented a gradient descent approach in searching for an optimal solution for the quantum equalization problem.

Biography: Rebbecca Thien completed her Bachelor’s (2016) and Master’s (2018) degrees in Mechanical and Aerospace Engineering at Gyeongsang National University, South Korea. She recently completed her PhD in Australian National University in May 2024.

Host: Dr. Edmond Jonckheere | jonckhee@usc.edu

More Info: https://csc.usc.edu/seminars/2024Spring/thien.html

More Information: 2024.06.12 CSC Seminar - Rebbecca Thien.pdf

Location: Hughes Aircraft Electrical Engineering Center (EEB) - EEB 248

Audiences: Everyone Is Invited

Contact: Miki Arlen

Event Link: https://csc.usc.edu/seminars/2024Spring/thien.html

PhD Dissertation Defense - ASM Rizvi

Title: Mitigating Attacks That Disrupt Online Services Without Changing Existing Protocols

Date and Time: Thursday, June 13th, 2024: 1:00p - 3:00p

Location: RTH 114

Commitee Members: John Heidemann (Chair), Bhaskar Krishnamachari, Harsha V. Madhyastha, Jelena Mirkovic

Abstract: Service disruption is undesirable in today’s Internet connectivity due to its impacts on enterprise profits, reputation, and user satisfaction. We describe service disruption as any targeted interruptions caused by malicious parties in the regular user-to-service interactions and functionalities that affect service performance and user experience. In this thesis, we propose new methods that tackle service disruptive attacks using measurement and observation without changing existing Internet protocols. Although our methods do not guarantee defense against all the attack types, our example defense systems prove that our methods generally work to handle diverse attacks. To validate our thesis, we demonstrate defense systems against three disruptive attack types. First, we mitigate Distributed Denial-of-Service (DDoS) attacks that target an online service. Second, we handle brute-force password attacks that target the users of a service. Third, we detect malicious routing detours to secure the path from the users to the server. We provide the first public description of DDoS defenses based on anycast and filtering for the network operators. Then, we show the first moving target defense utilizing IPv6 to defeat password attacks. We also demonstrate how regular observation of latency helps cellular users, carriers, and national agencies to find malicious routing detours. As a supplemental outcome, we show the effectiveness of measurements in finding performance issues and ways to improve using existing protocols. These examples show that our idea applies to different network parts, even if we may not mitigate all the attack types.

Location: Ronald Tutor Hall of Engineering (RTH) - 114

Audiences: Everyone Is Invited

Contact: ASM Rizvi

Ph.D. Thesis Defense - Yuan Meng

PhD Thesis Defense - Yuan Meng

Committee Members: Prof Viktor K. Prasanna (Chair), Prof. Bhaskar Krishnamachari, Prof. Yue Zhao

Title: Accelerating Reinforcement Learning using Heterogeneous Platforms: Co-Designing Hardware, Algorithm, and System Solutions

Abstract: Reinforcement Learning (RL) is an area of AI that constitutes a wide range of algorithms, enabling autonomous agents to learn optimal decisions through online environment interactions, data collection, and training. Recently, certain categories of RL algorithms have witnessed widespread adoption due to their generalizability and reliability, including model-free RL based on policy/value optimizations and model-based RL using Monte Carlo Tree Search. General-purpose processors fail to optimally achieve efficient execution speed for RL due to the intrinsic heterogeneous characteristics among various RL primitives and algorithms. Optimized acceleration systems that exploit heterogeneity across different architectures to support the variations of compute kernels and memory characteristics in RL are crucial to fast and efficient application development. In this dissertation, we develop acceleration frameworks for two key categories of RL algorithms, i.e., model-free Deep RL, and model-based RL using Monte Carlo Tree Search (MCTS). We implement these frameworks by addressing two objectives: 1. We develop algorithm-hardware co-optimized accelerators for the fundamental primitives in the key categories of RL algorithms. This includes inference and training of DNN policy models, as well as dynamic tree-based operations in MCTS. 2. We create portable system solutions that identify the optimal primitive scheduling, mapping, and design configurations onto heterogeneous devices based on the task dependency, compute, and memory characteristics of the target RL algorithms. Experiments on various platforms consisting of interconnected CPUs, FPGAs, and GPUs showcase superior performance enhancements across diverse models, algorithms, hardware platforms, and benchmark environments compared to state-of-the-art RL libraries.

Bio: Yuan Meng is a fifth-year PhD candidate in Computer Engineering, advised by Professor Viktor K. Prasanna. She obtained her BS degree in electrical and computer engineering at Rensselaer Polytechnic Institute. Her research interests include parallel computing, deep learning acceleration, heterogeneous computing, and reinforcement learning.

Date: Thursday, June 20th, 2024
Time: 2pm
Location:EEB 132
Zoom Link:https://usc.zoom.us/j/8629150353

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 132

Audiences: Everyone Is Invited

Contact: CS Events

Event Link: ://usc.zoom.us/j/8629150353

PhD Dissertation Defense - Ang Li

Title: Revisiting FastMap: New Applications

Date: Thursday, June 20th, 2024 - 2:00p - 3:30p

Location: SAL 213

Committee Members: T. K. Satish Kumar (Chair), John Carlsson, Emilio Ferrara, Sven Koenig, and Aiichiro Nakano

Abstract: FastMap was first introduced in the Data Mining community for generating Euclidean embeddings of complex objects. In this talk, I will first generalize FastMap to generate Euclidean embeddings of graphs in near-linear time: The pairwise Euclidean distances approximate a desired graph-based distance function on the vertices. I will then apply the graph version of FastMap to efficiently solve various graph-theoretic problems of significant interest in AI: including facility location, top-K centrality computations, community detection and block modeling, and graph convex hull computations. I will also present a novel learning framework, called FastMapSVM, by combining FastMap and Support Vector Machines. I will then apply FastMapSVM to predict the satisfiability of Constraint Satisfaction Problems and to classify seismograms in Earthquake Science.

Zoom Link: https://usc.zoom.us/j/92402869565?pwd=L0dwc0xRZVNrT3UrQWZCcERmVlBqQT09

Location: Henry Salvatori Computer Science Center (SAL) - 213

Audiences: Everyone Is Invited

Contact: Ang Li

Event Link: https://usc.zoom.us/j/92402869565?pwd=L0dwc0xRZVNrT3UrQWZCcERmVlBqQT09

2024 ASEE Conference and Exposition

Speaker: -, -

Talk Title: 2024 Conference of the American Society For Engineering Education

Abstract: The 2024 Conference of the American Society For Engineering Education is taking place on June 23-26, 2024 in Portland, Oregon. The theme of the conference is "The Future of Engineering Education."

Host: Doug Tougaw

More Info: https://www.asee.org/events/Conferences-and-Meetings/2024-Annual-Conference

Location: Portland, Oregon

Audiences: Everyone Is Invited

Contact: Helen Choi

Event Link: https://www.asee.org/events/Conferences-and-Meetings/2024-Annual-Conference

Six Sigma Green Belt for Process Improvement

Speaker: IISE Faculty, IISE Faculty

Talk Title: Six Sigma Green Belt for Process Improvement

Abstract: USC Viterbi School of Engineering's Six Sigma Green Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Green Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Green Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's green belt exam (administered on the final day of the course). If you have any questions, please visit: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

Host: USC Viterbi Corporate and Professional Programs

More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

Audiences: Six Sigma Green Belt Students

Contact: VASE Executive Education

Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

Six Sigma Green Belt for Process Improvement

Speaker: IISE Faculty, IISE Faculty

Talk Title: Six Sigma Green Belt for Process Improvement

Abstract: USC Viterbi School of Engineering's Six Sigma Green Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Green Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Green Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's green belt exam (administered on the final day of the course). If you have any questions, please visit: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

Host: USC Viterbi Corporate and Professional Programs

More Info: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

Audiences: Six Sigma Green Belt Students

Contact: VASE Executive Education

Event Link: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

Six Sigma Green Belt for Process Improvement

Speaker: IISE Faculty, IISE Faculty

Talk Title: Six Sigma Green Belt for Process Improvement

Abstract: USC Viterbi School of Engineering's Six Sigma Green Belt for Process Improvement, offered in partnership with the Institute of Industrial and Systems Engineers, allows professionals to learn how to integrate principles of business, statistics, and engineering to achieve tangible results. Master the use of Six Sigma to quantify the critical quality issues in your company. Once the issues have been quantified, statistics can be applied to provide probabilities of success and failure. Six Sigma methods increase productivity and enhance quality. As a USC Six Sigma Green Belt, you will be equipped to support and champion a Six Sigma implementation in your organization. To earn the USC Six Sigma Green Belt Certificate, you will be required to pass the Institute of Industrial and Systems Engineer's green belt exam (administered on the final day of the course). If you have any questions, please visit: https://viterbiexeced.usc.edu/engineering-program-areas/six-sigma-lean-certification/six-sigma-green-belt-process-improvement/

Host: USC Viterbi Corporate and Professional Programs

Audiences: Six Sigma Green Belt Students

Contact: VASE Executive Education

DEN@Viterbi - 'Limited Status: How to Get Started' Virtual Info Session

Join USC Viterbi for our upcoming Limited Status: How to Get Started Virtual Information Session via WebEx to learn about the Limited Status enrollment option. The Limited Status enrollment option allows individuals with an undergraduate degree in engineering or related field, with a 3.0 GPA or above to take courses before applying for formal admission into a Viterbi graduate degree program. USC Viterbi representatives will provide a step-by-step guide for how to get started as a Limited Status student and enroll in courses online via DEN@Viterbi as early as the Summer 2024 semester.

WebCast Link: https://uscviterbi.webex.com/weblink/register/rd3f714d4411f3338b11a3afced3c24bd

Audiences: Everyone Is Invited

Contact:

Event Link: https://uscviterbi.webex.com/weblink/register/rd3f714d4411f3338b11a3afced3c24bd

AI Seminar-On Robustness and Generative Modeling

Speaker: Iacopo Masi, Sapienza University of Rome

Talk Title: On Robustness and Generative Modeling

Abstract: The bloom of AI capabilities and their practical implication in our lives raised concerns regarding AI's robustness to an adversary. A way to improve the robustness of the prediction is adversarial training (AT), which trains the predictor on adversarial data. Although AT is mainly associated with discriminative models, in this talk, I will show how we can shed light on some mysterious behaviors using generative modeling. By reinterpreting a robust discriminative classifier as an Energy-based Model (EBM), we offer a new take on the dynamics of adversarial training. On the ground of our thorough analysis, we present new theoretical and practical results that show how interpreting AT energy dynamics unlocks a better understanding of (i) robust overfitting, (ii) distinct features of different AT methods, such as SAT and TRADES (iii) their generative capabilities; also offering a simple process to lift these capabilities without training for generative modeling. In the last part of the talk, I will discuss how we can use off-the-shelf robust classifiers to help generative modeling by inverting them. REMINDER: If you do not have access to the 11th Floor, please check in at the main reception desk on 10th floor and someone will escort you to the conference room location prior to the start of the talk. Meeting hosts only admit guests that they know to the Zoom meeting. Hence, you’re highly encouraged to use your USC account to sign into Zoom. If you’re an outside visitor, please provide your: Full Name, Title and Name of Workplace to (aiseminars-poc(at)isi.edu) beforehand so we’ll be aware of your attendance. REGISTRATION IS REQUIRED https://usc.zoom.us/webinar/register/WN_ontXffySQ7mOaBn75fp8xg
Zoom Webinar Info:
https://usc.zoom.us/s/95834774243?pwd=U1k0S3A5Y0VERTZCM3RuQnhvdEJwUT09
Meeting ID: 958 3477 4243
Passcode: 945662

Biography: Dr. Iacopo Masi is an Associate Professor in the Computer Science Department at Sapienza, University of Rome. He is also the Principal Investigator and founder of the OmnAI Lab. Until August 2022, he held the position of Adjunct Research Assistant Professor in the Computer Science Department at the University of Southern California (USC). Previously, Dr. Masi was a Research Assistant Professor and Research Computer Scientist at the USC Information Sciences Institute (ISI). He has served as an Area Chair for several computer vision conferences (WACVs, ICCV'21, ECCV'22, CVPR'24) and is an Associate Editor for The Visual Computer - International Journal of Computer Graphics. Additionally, he organized an International Workshop on Human Identification at ICCV'17, co-organized the Unlearning and Model Editing (U&Me) workshop at ECCV'24, and is a general chair for ICIAP'25. In 2018, Dr. Masi was honored with the prestigious Rita Levi Montalcini Award by the Italian government. His primary research interests revolve around the intersection of machine learning, computer vision, and biometrics. Currently, he is exploring various interconnected lines of research, including adversarial robustness, proactive defense against image manipulation, inverse problems, and generative AI in both the vision and NLP domains. To learn more, visit https://omnai.di.uniroma1.it andhttps://iacopomasi.github.io"

Host: Fred Morstatter and Craig Knoblock

More Info: https://www.isi.edu/events/4998/ai-seminar-on-robustness-and-generative-modeling/

Webcast: https://usc.zoom.us/webinar/register/WN_ontXffySQ7mOaBn75fp8xg

Location: Information Science Institute (ISI) - Conf Rms #1135-37 and Virtual

WebCast Link: https://usc.zoom.us/webinar/register/WN_ontXffySQ7mOaBn75fp8xg

Audiences: Everyone Is Invited

Contact: Pete Zamar

Event Link: https://www.isi.edu/events/4998/ai-seminar-on-robustness-and-generative-modeling/