Prof. Dr. Ling Liu Georgia Institute of Technology Office: KACB, room 3340 Georgia Tech, 266 Ferst Dr,
Atlanta, GA 30332-0765 USA Research.com |
If we knew what it was we were doing, |
-- Albert Einstein |
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Prof. Dr. Ling Liu is a full professor in the College of Computing at Georgia Institute of Technology and an elected IEEE Fellow. She directs the research programs in Distributed Data Intensive Systems Lab (DiSL). Her current research interests are centered on data and intelligence powered computing, such as artifical intelligence, machine learning, knowledge discovery and data mining, big data systems and analytics. Concretely, she is interested in developing innovative and efficient learning algorithms and systems for multi-modality of data, as well as algorithms and optimizations for improving performance, availability, security, privacy, trust of data and intelligence powered computing systems and applications, such as cloud and edge computing, distributed computing, Internet of smart things, Mobile computing and location based services, wireless and sensor networked computing, peer to peer and blockchain computing. Prof. Liu has published over 300 international journal and conference articles with high citations at Google scholar. Her research group has produced a number of open source
software systems, among which the most popular ones include WebCQ, XWRAPElite, PeerCrawl, GTMobSIM, SHAPE, NEAT, TripleBit (jointly with Prof. Pingpeng Yuan), MemFlex, MemPipe, XMemPod, AdaTrace, FUSE, Fed-CDP, LRBench, XEnsemble1.0, Data Poisoning in FL.
Prof. Dr. Liu is a recipient of IEEE Computer Society Technical Achievement Award (2012) and an Outstanding Doctoral Thesis Advisor award from Georgia Institute of Technology in 2012. Her research group has been a recipient of the best paper awards from numerous top venues, including ICDCS 2003, WWW 2004, 2005 Pat
Goldberg Memorial Best Paper Award, IEEE Cloud 2012, IEEE ICWS 2013, Mobiqutious 2014, APWeb 2015, IEEE/ACM CCGrid 2015, IEEE Symposium on Big Data 2016, IEEE Edge 2017 and IEEE IoT 2017.
Prof. Dr. Ling Liu has served as a general chair or a PC chair of numerous IEEE and ACM conferences in data engineering, very large databases, Big data, and distributed computing fields, and most recently, co-PC chair of the 2019 International Conference on World Wide Web. Prof. Liu has been on editorial board of over a dozen international journals, and served as the Editor In Chief of IEEE Transactions on Service Computing (2013-2016). Currently, Prof. Liu is the Editor in Chief of ACM Transactions on Internet Technology (TOIT).
Prof. Dr. Liu's current research has been primarily sponsored by NSF, IBM, Intel and CISCO edge computing.
Active Research Projects
I no longer maintain the updated list for my projects here. Interested readers may visit our github website for updated projects.
GTDLBench - Benchmarking Deep Learning Frameworks
LRBench - Benchmarking Learning Rate Policies for Deep Neural Networks
DP Model_Publishing - Differentially Private Model Publishing
CLDP - Condensed Local Differential Privacy for Privacy Preserving Data Collection
GT Attack Pod - GT Attack Pod (MODEF)
DLEdge - Deep Learning on Edge Devices (Intel Sponsored)
NGramCNN - Learning to classify graph objects with a deep convolutional neural network (incl. software download).
AdaTrace - Differentially Private and Attack Resilient Approach to Large Scale Mobile Trajectory Synthesis
DPStar - Differentially Private Publishing of Spatial Trajectories
SI-Cluster - Social Influence Analytics in Heterogeneous Information Networks (software download)
VEPathCluster - Social Influence Analytics in Heterogeneous Information Networks (software download)
GraphLens - Social Influence Analytics in Heterogeneous Information Networks (software download)
SHAPE - Semantic Hash Partitioning for Distributed Processing of big RDF datasets
TripleBit - A Fast and Compact RDF Store
NEAT - Trajectory Clustering and Spatial Pattern Mining
XMemPod
XBigPage
FastSwap
DAHI
MemFlex
MemPipe
Adversarial Deep Learning with Privacy Awareness
PPML - Privacy Preserving Machine Learning
PrivacyGuard - NSF SaTC Medium: Privacy Preserving Computations in Big Data Clouds
PPN - NSF NetSE Medium: Privacy Preserving Information Networks and Services for eHealthcare Systems and `Applications
MedVault - NSF CyberTrust Medium: Ensuring Security & Privacy for Medical Data )
GTMobiSIM - Mobility Simulation and Trace Generator (GTMobiSIM Visualizer)
MobiEyes - Distributed Computing Architecture and Algorithms for Processing
Location Queries
GeoGrid / GeoCast
- Decentralized Service Architecture for Mobile Location-based Information Delivery and Dissemination
Location Privacy - Location Privacy in
Spatial Alarms / mTriggers – High Performance Architecture and Models for
Scalable Processing of Location Triggers
Location based Access Control (LBAC) – Secure Location Determination and Verification: Models and Techniques
Distributed Computing Systems Research
GTPeers
- Peer-to-Peer and Grid Computing Research
HyperBee / Apoidea /
PeerCrawl
- Peer-to-Peer Web Crawling and Search
SGuard
- Secure Guards for Massively Distributed Computing Systems
MedVault
- Ensuring Security and Privacy for Electronic Medical Records
Guarding the Next Internet
Frontier: Countering Denial of Information
Distributed Data Management and Large Scale
XWrapElite
- An Automated Wrapper Generation System for Web Sources
XWrapComposer - A Wrapper Generation System for
Extracting Information from Multiple Web Pages
WebCQ
- Continual Queries for Information Monitoring on the Web
Privacy-preserving
Data Classification using Geometric Transformations
Enterprise Workflow and ServiceOriented Computing
Privacy Preserving in
Data Stream and Event Stream Mining
Storage as Service:
Architectures and Models for Performance, Failure Resilience, and Security
Past Research Projects
PeerCQ
- Internet Information Monitoring Using a Peer-to-Peer network
PeerTrust Trusted Computing in Peer to Peer
Systems
TrustMe
- Anonimity Support in Distributed Trust Management
Systems
Edge Caching Grid for Dynamic Content
Delivery
Scientific Data Management
and BioComputing
VISTA - Effective Cluster
Rendering of Very Large Data Sets and an application of
iVIBRATE - Interactive
Visualization Based Framework for Clustering Large Datasets
BestK:
the Critical Clustering Structure in Categorical Datasets
Infosphere
- Infopipes Technology for Fresh Information Delivery
THOR - Deep Web Data
Extraction
Athena - Web Service
Discovery: A Source Biased Approach
OpenCQ
- Continual Queries for Logistic Applications
XWrap
Original - A Semi-Automated Wrapper Generation System for Structured or
Semi-structured Data Sources
Omini
- A Fast Object Extraction System for Web Sources
AQR - Distributed Query
Routing
Ginga
- Adaptive Query Processing with varying resource
availabilities and constraints
PageDigest
Efficient Encoding Scheme for Web Documents
Sdiff
- Structurally aware change detection algorithms for HTML and XML documents
Context Cube - A
Context Aware Methodology for Managing and Accessing Sensor Data - GT Aware
Home
TAM -
Restructuring and Self-Configuring of Transactional Workflow Systems
Diorama/DIOM
EVOLVE
DISIMA
Data Mining and Data
Warehousing
Courses
I have taught the following courses
from 1999 to present. I have also created the course cs6220 (used to be cs8803 BDS since 2015. From 2021, all courses will be made available on Canvas instead.
, 2009, 2008, Spring 2007, Spring
2006, Spring
2005, Spring
2004)
CS6220: Big Data Systems and Analytics (
From Fall 2021, the course is accessible on canvas,
Fall 2020,
Fall 2019,
Fall 2018,
Fall 2017,
Fall 2016,
Fall 2015)
CS6675/CS4675:
Advanced Internet Computing Systems and Application Development
(
From Spring 2021 forward, the course is accessible on Canvas,
Spring 2020,
Spring 2019,
Spring 2018,
Spring 2017,
Spring 2016,
Spring 2015,
Spring 2014,
Spring 2013,
Spring 2012,
I also supervisee A list of cs7001
mini-projects each year.