Ling Huang
Researcher
Intel Research at Berkeley
2150 Shattuck Avenue, Penthouse Suite
Berkeley, CA 94704
Phone: 510-495-3406
Fax: 510-495-3049
Email:
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Introduction
I am a researcher in
Intel Research at Berkeley
.
I completed my Ph.D. in
Computer Science
at
University of California at Berkeley
in September 2007. My dissertation is on
D-Trigger: A general framework for efficient online detection
under the supervision of
Professor Anthony Joseph
. My dissertation work focuses on online detection by bringing together the best techniques from continuous data streaming, online machine learning, and distributed signal processing. During my Ph.D. study, I was affiliated with
RadLab
, and before I joined
RadLab
, I was a member of
OceanStore group
and mainly worked on
Tapestry
projects.
I received my BS and MS degree from
Beijing University of Aeronautics and Astroautics (BUAA)
, Beijing, China. Before I came to Berkeley, I worked more than four years as a model developer and project manager at
Bei Hang Haire CAXA
, the No.1 CAD/CAM software company in China.
Rsearch Interests
Resource constrained reasoning, efficient and distributed machine learning
Statistical learning, inference and decision-making under computation and communication constraints
Continuous online network monitoring, traffic analysis, modeling and prediction.
Efficient in-network anomaly detection, distributed signal processing.
Distributed data streaming and triggering.
Secure, reliable and adaptive network system.
Peer-to-Peer network, scalable and fault-resilient network system, application-level multicast, web caching, etc.
Recent Projects
Resource Constrained Reasoning
Communication-Efficient Online Detection of Network-wide Anomalies
Distributed Machine Learning and In-Network Anomaly Detection
Communication-Efficient Tracking of Distributed Triggers
Previous Projects
Probabilistic Data Aggregation and Multi-Scale Prediction
Geometric Modeling for CAD/CAM system
Fault-resilient Overlay Routing
Tapestry: Scalable and Resilient Peer-to-Peer network infrastructure
SpamWatch: A Peer-to-peer Spam Filtering System