Computer Engineering/Networks at USC/Viterbi School of Engineering

Networks Related Faculty (and courses) EE Faculty • Bhaskar Krishnamachari (EE 579, EE 597, EE 652) • Caulighi Raghavendra (Vice Dean) (EE 450) • John Silvester (EE 450, EE 485, EE 503, EE 549, EE 550, EE 555) • Kai Hwang (EE 532) • Konstantinos Psounis (EE 503, EE 597, EE 650) • Michael Neely (EE 503, EE 550, EE 649, EE 549) • Murali Annavaran (EE 579) • Rahul Jain (EE 512, EE 599 (Network Economics) • Viktor Prasanna (EE 657) • Ali Zahid (EE 450, EE 555) (Part Time) • Shahin Nazarian (EE 450) (Lecturer) • Young Cho (EE 533 – Network Processor, CS 558L) (Research Faculty) CS Faculty : • Ramesh Govindan John Heidemann / William Cheng / Clifford Neuman (CS 551, CS

558L, CS 530), Minlan Yu (CS 599 – cloud computing) Plus several researcher faculty at ISI teach / supervise DR (Google “ISI Networks Division”)

NETWORKS RELATED FACULTY RESEARCH AREAS

Design, analysis and implementation of algorithms and protocols for Next-Generation Wireless Networks Low-Power Wireless Sensor Networks Examples: Backpressure Collection Protocol (BCP), a novel dynamic routing protocol; and Ecolocation, a sequence‐based indoor localization algorithm that requires no prior calibration. Vehicular Networks In collaboration with General Motors Research, developing new architecture and mechanisms for cars to talk to each other. Cognitive Radio Novel algorithms for opportunistic spectrum access using tools from stochastic optimization theory, such as multi-armed bandits.

Robotic Networks Investigating the use of robotic swarms to maintain connectivity and high-quality of communication in wireless networks.

Autonomous Networks Research Group Bhaskar Krishnamachari

http://anrg.usc.edu

Wireless and Sensor Networks • Energy efficient algorithms and protocols • Body area sensor networks and energy management • Delay tolerant networks – routing, congestion control, and applications Failure Prediction in Oil Fields • Predictive analytics for smart oil fields • Failure prediction in oil wells in large fields • Application of machine learning techniques to oil field problems

Big Data – Time Series Event Graphs • Data layouts and storage for large graph data • Graph partitioning, analytics on graphs

Networks, Big Data, and Machine Learning Cauligi Raghavendra

High-Performance Computing and Networking John Silvester

Areas of Interest: • High Performance Networking • Software Defined Networking

Current Projects: • “Condo-of-Condos” – a collaboration for sharing High Performance

Computing (HPC) Resources among a set of 10+ Universities • TEN-II – Next Generation High Performance USC Campus Network - On-

demand 10G and 100G connectivity to the Researcher utilizing SDN Technology

• International Research Networking Collaboration – project to identify and support international research collaborations requiring high-performance networks

Computing Clouds, Internet of Things and BigData Security Kai Hwang

Cloud Ecosystem for Internet of Things • Cloud services, benchmarks and IoT/SN apps. • Wireless and satellite Internet infrastructure

for mobile and pervasive computing • RFID, sensor networks and assisted GPS

services in cyber-physical and IoT systems

Internet Computing with trusted clouds, IoT, P2P networks and automated datacenters

Bigdata Integrity, Privacy, and Analytics for Trusted Computing • Community clouds with associative big-data

sharing over virtual disks • Privileged access protocols based on roles and

attributed key management • Reputation and accountability systems for

trusted P2P, grid and cloud Computing

Networked Systems Performance and Design Lab Konstantinos Psounis

• Modeling and analyzing the performance of a variety of wired and wireless networks

– including the Internet, WiFi, cellular, mobile, delay and disruptive tolerant, sensor, mesh, and peer to peer networks, as well as the web

• Designing algorithms and protocols to solve problems related to such systems • Implementing in software and hardware real-world testbeds to access

performance in practice

– e.g. building and using software defined radios to experiment with advanced physical layer techniques, such as distributed multiuser and massive MIMO, in order to address the wireless bandwidth crunch

NETworks Control, Optimization & Games Rahul Jain

ECON Layer Internet as an economically-viable eco-system

Network Market Design Theory*

SERV Layer Network Architectures for QoS-provisioning & Security

Incentivized Network Architecture Designs*

NET Layer Distributed Control of Large-Scale Networks

Network Utility Maximization Theory+

PHY Layer Increase Capacity through Multi-user schemes

Non-cooperative Multi-User Comm. Theory*

• The economics of incentives for resource sharing, quality of service provisioning, security and cooperation in networks, wireless and power systems

– Design of market mechanisms for bandwidth and spectrum sharing to enable Internet and wireless networks as economically-viable eco-systems

– Design of incentivized network architectures for quality of service provisioning and security

– Design and analysis of mechanisms for network utility maximization for distributed control of large scale networks in competitive environments, including power systems

– Implementation of cooperative communication schemes to increase network capacity with non-cooperative users

Parallel and Distributed Computing Viktor K. Prasanna

Application Specific Accelerators on FPGAs

• High-Speed networking • Terabit IP forwarding • Virtualized routers • Security firewalls

• Data center networks • Energy efficiency

http://ceng.usc.edu/~prasanna

Multi-core/Heterogeneous Architectures

• Multithreaded applications • Software routers • Graph analytics

• GPU computing • Scalable parallel algorithms

Big Data Platforms and Applications

• Time series graph analytics • Cloud resource management • Social network analysis • Analytics for smart infrastructure (smartgrid, smart oil fields,..)

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GENERATION • Multimodal Harvesting • Flexible and Scalable • Highly Efficient Conditioning • Industrial Wireless Sensor Network

MEASUREMENT • On-chip/On-board Measurement • High Accuracy and High Sampling Rate • Digital Instrumentation/Low Overhead • Account for Process Variation • In-Situ Dynamic and Static Power

MANAGEMENT • Scalable Smart Power Grid

Management • High Throughput/Low Latency • Software Defined Network Driven

Power Generation/Measurement/Management Young H. Cho

Sub-circuit power estimates w/error < 1.25%

Intelligently Managing Power Grid Network with Software

Efficient Power Harvesting for WSN