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Li Yuhong. Daniel
Contact
Information
Avenida da Universidade, Taipa,
University of Macau, Macau, China
(+86)-153-635-892-16
[email protected]
Research
Interests
Spatial and Temporal Data Analysis, High Performance Computing, GPGPU
Job Objective
A research position in the field of efficient correlation and causality mining for big data.
Technical
Skills
Programming skills: C/C++, Python, PHP, JavaScript, Latex
Databases: Mysql, SqlServer, CouchDB
Platforms: Windows, Unix/Linux, VMWare Workstation
Education
University of Macau, Macau, China
Ph.D, Software Engineering, Ongoing
• Topic: Efficient Query Processing in Time Series
• Advisors: Prof. Gong Zhiguo & Dr. Leong Hou U
Xidian University, Xi’an, China
B.S., Software Engineering, Sept 2006 - July 2010
• Thesis Topic: Web Game Based On IVR
Working
Experience
Project
Intern
Technology Strategy Group,
Microsoft Research Asia, Bei Jing
Mentor: Dr. Yu Zheng
Research Assistant
Database Research Group,
The Hong Kong Polytechnic University, Hong Kong
Supervisor: Dr. Man Lung Yiu
Mar 2015 to Present
July 2014 to Oct 2014
Discovering Longest-lasting Correlation in Sequence Databases
- In this work, we propose several techniques such that longest-lasting correlated
subsequences can be discovered efficiently.
- Two core techniques, i.e., α-skipping technique and diamond cover index, are
proposed and their performance are evaluated thoroughly in this work. Given
reasonable space overhead, our best method (i.e., SKIP+DCI) is up to one order of
magnitude faster than the state-of-the-art adaption.
Quick-Motif: An Efficient and Scalable Framework for Exaxt Motif Discovery
- Discovering motifs in sequence databases has been receiving abundant attentions
from both database and data mining communities, where the motif is the most
correlated pair of subsequences in a sequence object.
-In this work, we propose a novel framework named Quick-Motif which adopts a twolevel approach to enable batch pruning at the outer level and enable fast correlation
calculation at the inner level. We further propose two optimization techniques for
the outer and the inner level. According to our experimental study, our method is
up to three orders of magnitude faster than the state-of-the-art methods.
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A Real-time Attention Detection and Treatment System Based On HRV
- HRV (i.e., Heart Rate Variability) biofeedback therapy is widely used in the
treatment of ADHD (i.e., Attention Deficit Hyperactivity Disorder). In this work,
we captures user’s attention and design several computer games such that user’s
performance is mainly affected by his attention. As a remark, the user’s attention
is measure by his HRV.
Publications
1. Yuhong Li, Leong Hou U, Man Lung Yiu, Zhiguo Gong, ”Discovering Longestlasting Correlation in Sequence Databases”. Proceedings of the VLDB Endowment
(PVLDB), 6(14): 1666-1677 (2013).
2. Leong Hou U, Hongjun Zhao, Man Lung Yiu, Yuhong Li, Zhiguo Gong, ” Towards
Online Shortest Paths Computation”. IEEE Trans. Knowl. Data Eng. (TKDE)
26(4):1012-1025 (2014).
3. Yuhong Li, Leong Hou U, Man Lung Yiu, Zhiguo Gong, ”Quick-Motif: An
Efficient and Scalable Framework for Exact Motif Discovery”. ICDE 2015.
Workshop
Honors &
Scholarships
1. Yuhong Li, ”Efficient Query Processing in Time Series”. SIGMOD 2015 PhD
Symposium.
University of Macau
• SIGMOD 2015 Student Travel Award
• ICDE 2015 Student Travel Award
• UMAC Postgraduate Scholarship
• Macau SAR Postgraduate Scholarship
April
April
Sept
June
2015
2015
2012
2010
Xidian University
• Technology Talents
• 2nd Price in 7th Shaanxi Challenge Cup
• Excellent Project in National Innovation Experiment Plan
• Outstanding Winner of Spark Cup
Jan
June
May
Dec
2010
2009
2009
2008
Community
Service
Reviewer :
• Journal: TKDE’14
External Reviewer :
• Conference: KDD’12,15,SIGMOD’13-14,WAIM’13-14,CIKM’13-14,DASFAA’14-15
• Journal: TKDE’12, IS’14-15
References
Zhiguo Gong
Associate Professor
Faculty of Science and Technology
University of Macau
Phone: (+853)-8822-4962
E-mail: fstzgg A-T umac.mo
Leong Hou U, Ryan
Assitant Professor
Faculty of Science and Technology
University of Macau
Phone: (+853)-8822-4493
E-mail: ryanlhu A-T umac.mo
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