Ling Huang
Intel Research at Berkeley
2150 Shattuck Avenue, Penthouse Suite
Berkeley, CA 94704
Phone: 510-495-3406
Fax: 510-495-3049
Email: ling dot huang at intel dot com
[
Publications
] [
Talks
] [
Contact information
]
New
: Our paper on
Mining Console Logs for Large-Scale System Problem Detection
will apprear in
SysML 2008
. I will put a copy online soon.
New
: Our paper on
Spectral Clustering with Perturbed Data
will apprear in
NIPS 2008
.
New
: I am running the
IRB/UCB joint system seminar
this Fall. Please check it out and join us to welcome all speakers.
New
: Our paper on
Approximate Support Vector Machines
appears in
ECML/PKDD 2008
.
Introduction
I am a research scientist 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.
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