Select a calendar:
Filter June Events by Event Type:
SUNMONTUEWEDTHUFRISAT
University Calendar
Events for June
-
PhD Dissertation Defense - ASM Rizvi
Thu, Jun 13, 2024 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
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
Thu, Jun 20, 2024 @ 02:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
University Calendar
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/8629150353Location: 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
Thu, Jun 20, 2024 @ 02:00 PM - 03:30 PM
Thomas Lord Department of Computer Science
University Calendar
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=L0dwc0xRZVNrT3UrQWZCcERmVlBqQT09Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Ang Li
Event Link: https://usc.zoom.us/j/92402869565?pwd=L0dwc0xRZVNrT3UrQWZCcERmVlBqQT09
