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Pages

Posts

Future Blog Post

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

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This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

portfolio

publications

talks

EAGER: Spectral Analysis for Fraud Detection in Large-scale Networks

Published:

This project takes a unified spectral transformation approach to address challenges of analyzing network topology and identifying fraud patterns in large-scale dynamic networks by using data spectral transformation with network topology visualization. Large-scale social and communication networks contain rich topological information embedded inside, in addition to various structured, semi-structured, and unstructured data. The research is characterizing patterns of various attacks in the spectral projection space of graph topology and developing spectrum based methods to identify these attacks. The approach, which exploits the spectral space of the underlying interaction structure of the network, is orthogonal to traditional approaches using content profiling. The ability to perform this spectral analysis is dependent upon the development of complex mathematical techniques. Critical issues that are being explored include the scalability of the methods to very large data sets, the determination of the dimensionality of the node representation in spectral space, and the interpretation of patterns in spectral space.

SMASH: Semantic Mining of Activity, Social, and Health data

Published:

Two thirds of the US population are now overweight or obese. This incurs significant health risks and financial costs to society. Traditionally, support groups and other social reinforcement approaches have been popular and effective in dealing with unhealthy behaviors including overweight. Of the factors associated with sustained weight loss one of the most important is continued intervention with frequent social contacts. Research in the design and implementation of the SMASH (Semantic Mining of Activity, Social, and Health data) system will address a critical need for data mining tools to help understanding the influence of healthcare social networks, such as YesiWell, on sustained weight loss where the data are multi-dimensional, temporal, semantically heterogeneous, and very sensitive. System design and implementation rest on specific aims including to develop a novel data mining and statistical learning approach to understand key factors that enable spread of healthy behaviors in a social network and to protect the privacy of human subjects during the data mining process for social network and health data. We consider the enforcement of differential privacy through a privacy preserving analysis layer. We develop novel solutions to preserve differential privacy for mining dynamic health data and social activities of human subjects.

Preserving Privacy in Human Genomic Data

Published:

Genome-wide association studies (GWAS) have received intensive attention due to the rapid decrease of genotyping costs and promising potential in genetic diagnostics. GWAS typically focus on associations between single-nucleotide polymorphisms (SNPs) and human traits like common diseases. However, sharing de-identified raw data, or only summary statistics from GWAS studies, can incur privacy disclosure for GWAS participants and potentially for regular individuals whose genetic data are collected by organizations such as hospitals or gene banks.

teaching

Program Committee Member

Conference, Program Committee, 2018

Dr. Wang Served as the Program Committee Member or Reviewer in the Program Committee for the following international conferences:

PAKDD 2019
23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining

IJCAI 2019
28th International Joint Conference on Artificial Intelligence

AAAI 2019
The Thirty-Third AAAI Conference on Artificial Intelligence

ICDM 2018
IEEE International Conference on Data Mining

CIKM 18
The 27th ACM International Conference on Information and Knowledge Management

CPD 2018
Workshop on Combining Physical and Data-Driven Knowledge in Ubiquitous Computing

Review Editor or Editorial Board

Journal, Data Mining and Data Management, 2018

Dr. Wang Served as the Review Editor or Reviewer in the Editorial Boards for the following journals:

Frontiers in Big Data

Social Network Analysis and Mining

Journal of Intelligent Information Systems

International Journal of Data Science and Analytics