Miao Lu's Homepage!

• Strong interdisciplinary background in Statistics, Machine Learning and Data Mining, with wide applications in biomedical science and internet technology.

• Published peer-reviewed papers in journals/conferences like WWW, NIPS TSW, SDM MLREC, PLOS NTDs, EbioMedicine, Biomarker Research, mBio, Respiratory Medicine, etc, with global impact, particularly on child health and online advertisement.

• Reviewed numerous top conferences and journals, such as KDD, IJCAI, CIKM, ECML-PKDD, AAAI, FG, WWW, SIGIR, SDM MLREC, TNNLS, TKDE, IEEE Access, Maternal & Child Nutrition, etc.

Dr. Miao Lu, LinkedIn, Google Scholar, Yahoo Research

Email: ml4ey@virginia.edu

Employment

  • Machine Learning Engineer, Snap Inc, Mar 2020 - Current

  • Sr. Research Scientist, Yahoo Research, Jan 2017 - Mar 2020

  • Infrastructure Data Scientist Intern, Facebook, May - Aug 2016

  • Research Intern, Genentech, May - Aug 2015

Education

  • Ph.D., Statistics, University of Virginia, 2016

  • M.S., Statistics, University of Virginia, 2014

  • B.S., Statistics, Zhejiang University, 2012

Publications

• X Wu, S Cetintas, D Kong, M Lu, J Yang, N Chawla, (2020) Learning from Cross-Modal Behavior Dynamics with Graph-Regularized Neural Contextual Bandit, Proceedings of The Web Conference (WWW 2020).

• M Lu, LY Tai, J Yang, (2018) An Efficient Time Series Forecasting Framework for Online Traffic, SIAM International Conference on Data Mining (SDM 2018) Workshop on Machine Learning Methods for Recommender Systems.

• H Gao, D Kong, M Lu, X Bai, J Yang, (2018) Attention Convolutional Neural Network for Advertiser-level Click-through Rate Forecasting, World Wide Web (WWW 2018).

• A Zhang, M Lu, D Kong, J Yang, (2017) Bayesian time series forecasting with change point and anomaly detection, Neural Information Processing Systems (NIPS 2017) Time Series Workshop.

• M Lu, J Zhou, C Naylor, BD Kirkpatrick, R Haque, WA Petri, JZ Ma, (2017) Application of penalized linear regression methods to the selection of environmental enteropathy biomarkers, Biomarker Research 5 (1), 9.

• M Lu, J Zhou, (2016) A single index model for censored quantile regression.

• SL Burgess, M Lu, JZ Ma, C Naylor, JR Donowitz, BD Kirkpatrick, R Haque, WA Petri, (2016) Inflammatory markers predict episodes of wheezing during the first year of life in Bangladesh, Respiratory Medicine 110, 53-57.

• JR Donowitz, R Haque, BD Kirkpatrick, M Alam, M Lu, M Kabir, SH Kakon, ..., WA Petri, (2016) Small intestine bacterial overgrowth and environmental enteropathy in Bangladeshi children, MBio 7 (1), e02102-15.

• PS Korpe, R Haque, C Gilchrist, C Valencia, F Niu, M Lu, ..., WA Petri, (2016) Natural history of cryptosporidiosis in a longitudinal study of slum-dwelling Bangladeshi children: association with severe malnutrition, PLoS Neglected Tropical Diseases 10 (5), e0004564.

• C Naylor, M Lu, R Haque, D Mondal, E Buonomo, U Nayak, JC Mychaleckyj, BD Kirkpatrick, ..., WA Petri, (2015)) Environmental enteropathy, oral vaccine failure and growth faltering in infants in Bangladesh, EBioMedicine 2 (11), 1759-1766.

Working Papers

• Z Yao, D Kong, M Lu, J Yang, Estimating Conversion for Cold-Start Ads: Campaign Representation Learning with Heterogeneous Information.

• D Kong, M Lu, K Shmakov, J Yang, Robust Consensus Clustering and its Applications for Advertiser Segmentation and Forecasting.

• A Zhang, M Lu, D Kong, J Yang, Bayesian time series forecasting with change point and anomaly detection.

Summer Interns

• Anderson Ye Zhang (Yale University)

• Zijun Yao (Rutgers University)

• Hongchang Gao (University of Pittsburgh)

• Yuhao Wang (Massachusetts Institute of Technology)

• Xian Wu (University of Notre Dame)

Talks

• Campaign Representation Learning in Advertisement,

Applied AI Summit, San Francisco, 2019.

• An Efficient Time Series Forecasting Framework for Online Traffic,

SDM Workshop on Machine Learning Methods for Recommender Systems, San Diego, 2018.

• Deep Learning in Advertisement,

Deep Learning Summit, San Francisco, 2018.

• Bayesian time series forecasting with change point and anomaly detection,

Neural Information Processing Systems (NIPS) Time Series Workshop, Long Beach, 2017.

• Robust time series forecasting with change points and anomaly points,

Yahoo Tech Pulse, Santa Clara, 2017.

• Estimating Treatment Effect in Time to Relapse When Patients Switch Treatment,

Eastern North American Region (ENAR), Austin, 2016.

• A Single Index Model for Censored Quantile Regression,

Computational and Methodological Statistics (CMStatistics), London, 2015.

• Overview of Variable Selection and Its Application in Biomarker Data Analysis,

Joint Statistical Meetings (JSM), Seattle, 2015.

• Cryptosporidiosis: Epidemiology and Correlates of Immunity in Bangladeshi Children,

Proceedings of American Society of Tropical Medicine and Hygiene (ASTMH), New Orleans, 2014.

• Markers of OPV Failure: Inflammatory and Nutritional Environment Critical Mediators of Vaccine Response,

Proceedings of American Society of Tropical Medicine and Hygiene (ASTMH), New Orleans, 2014.