%% Wei Xie, PhD in Computer Science, Vanderbilt University (Nashville, TN)
%% Research: 1) (Distributed) Machine learning, Optimization, Big data, privacy-preserving machine learning; 
%%           2) Quantitative genetics and genomics, Electronic health records (EHR), Genomic data privacy, Computational biology;
%%           3) Data privacy, Secure multi-party computation (SMC), Applied cryptography, Differential privacy;


%% Keywords: meta-analysis, genome-wide association study (GWAS), genomic data privacy, summary statistics, multi-site consortia studies;
%%    Averaging of machine learning models;
%%    Distributed machine learning, secure multi-party computation (SMC), cryptography, Paillier encryption, Yao's garbled circuit
@article{xie2014securema,
  title={SecureMA: protecting participant privacy in genetic association meta-analysis},
  author={Xie, Wei and Kantarcioglu, Murat and Bush, William S and Crawford, Dana and Denny, Joshua C and Heatherly, Raymond and Malin, Bradley A},
  journal={Bioinformatics},
  volume={30},
  number={23},
  pages={3334--3341},
  year={2014},
  publisher={Oxford University Press}
}


%% Keywords: Privacy-preserving machine learning, distributed machine learning, Newton method, Iteratively Re-weighted Least Squares (IRLS), distributed numerical optimization, second-order optimization;
%%  Logistic regression, regularization, penalty, sparse features, feature selection;
%%  Data privacy, Secure multi-party computation (SMC), cryptography, Shamir's secret share scheme, Summary statistics, aggregate data
%%  Genome-wide association study (GWAS), healthcare;
@article{li2016supporting,
  title={Supporting Regularized Logistic Regression Privately and Efficiently},
  author={Li, Wenfa and Liu, Hongzhe and Yang, Peng and Xie, Wei},
  journal={PloS one},
  volume={11},
  number={6},
  pages={e0156479},
  year={2016},
  publisher={Public Library of Science}
}

%% Keywords: Logistic regression (distributed, regularization), Distributed numerical optimization, Customizing optimization for cryptography, faster algorithm, Computational complexity asymmetry;
%%    Privacy-preserving machine learning;
%%    Cryptography, secure multi-party computation, Paillier encryption, Yao's garbled circuits;
@article{xie2016privlogit,
  title={PrivLogit: Efficient Privacy-preserving Logistic Regression by Tailoring Numerical Optimizers},
  author={Xie, Wei and Wang, Yang and Boker, Steven M and Brown, Donald E},
  journal={arXiv preprint arXiv:1611.01170},
  year={2016}
}


%% Keywords: Computational phenotyping, patient cohort identification, electronic health record (EHR or EMR), case/control extraction from EHR;
%%  Machine learning, data mining, classifiers, random forest, logistic regression, naive bayesian, decision trees, expert decision rules;
%%  Type 2 diabetes, Chinese medical records, expert chart review, data labeling, genome-wide association study (GWAS), Phenome-wide association study (PheWAS);
@article{zheng2017machine,
  title={A machine learning-based framework to identify type 2 diabetes through electronic health records},
  author={Zheng, Tao and Xie, Wei and Xu, Liling and He, Xiaoying and Zhang, Ya and You, Mingrong and Yang, Gong and Chen, You},
  journal={International Journal of Medical Informatics},
  volume={97},
  pages={120--127},
  year={2017},
  publisher={Elsevier}
}


%% Keywords: Electronic health record (EHR, EMR), machine learning, probabilistic topic modeling, latent direchlet allocation (LDA), latent factors/structures;
%%  Clinical workflow optimization, workflow modeling, block-based modeling;
%%  Unsupervised machine learning, Patient cluster, Topic-based interpretation, expert chart review;
@inproceedings{chen2015inferring,
  title={Inferring Clinical Workflow Efficiency via Electronic Medical Record Utilization},
  author={Chen, You and Xie, Wei and Gunter, Carl A and Liebovitz, David and Mehrotra, Sanjay and Zhang, He and Malin, Bradley},
  booktitle={AMIA Annual Symposium Proceedings},
  volume={2015},
  pages={416},
  year={2015},
  organization={American Medical Informatics Association}
}


%% Keywords: Transfer learning, common space mapping, semi-supervised machine learning, dimensionality reduction;
%%    Machine learning, image processing, domain adaptation;
@inproceedings{liang2016novel,
  title={A novel transfer learning method based on common space mapping and weighted domain matching},
  author={Liang, Ru-Ze and Xie, Wei and Li, Weizhi and Wang, Hongqi and Wang, Jim Jing-Yan and Taylor, Lisa},
  booktitle={Tools with Artificial Intelligence (ICTAI), 2016 IEEE 28th International Conference on},
  year={2016},
  organization={IEEE}
}