I am an assistant professor in the Department of Biostatistics at Harvard T.H. Chan School of Public Health.

Papers [by Topic]

ARCH: Large-scale Knowledge Graph via Aggregated Narrative Codified Health Records Analysis Z. Gan, D. Zhou, E. Rush, V. Panickan, Y. Ho, G. Ostrouchov, Z. Xu, S. Shen, X. Xiong, K. Greco, C. Hong, C. Bonzel, J. Wen, L. Costa, T. Cai, E. Begoli, Z. Xia, M G, K. Liao, K. Cho, T. Cai, J. Lu Submitted, 2023.

Fast Distributed Principal Component Analysis of Large-Scale Federated Data S. Shen, J. Lu, X. Lin Submitted, 2023.

Knowledge Graph Embedding with Electronic Health Records Data via Latent Graphical Block Model J. Lu, T. Cai Submitted, 2023.

Multi-source Learning via Completion of Block-wise Overlapping Noisy Matrices D. Zhou, T. Cai, J. Lu Under revision, 2023.

Federated Offline Reinforcement Learning D. Zhou, Y. Zhang, A. Sonabend, Z. Wang, J. Lu, T. Cai Under revision, 2023.

Combinatorial-Probabilistic Trade-Off: Community Properties Test in the Stochastic Block Models S. Shen, J. Lu International Conference on Learning Representations (spotlight paper), 2023.

Inference on the optimal assortment in the multinomial logit model X. Chen S. Shen, E. Fang, J. Lu ACM Conference on Economics and Computation, 2023.

Lagrangian Inference for Ranking Problems Y. Liu, E.X. Fang, J. Lu Operations Research 71.1 (2023): 202-223.

Graph over-parameterization: Why the graph helps the training of deep graph convolutional network Y. Lin, S Li, J Xu, J Xu, D Huang, W Zheng, Y Cao, J. Lu Neurocomputing 534 (2023): 77-85.

Multimodal representation learning for predicting molecule–disease relations J Wen, X Zhang, E Rush, V A Panickan, X Li, T Cai, D Zhou, Y Ho, L Costa, E Begoli, C Hong, J Gaziano, K Cho, J. Lu, K Liao, M Zitnik, T Cai Bioinformatics, 2023.

StarTrek: Combinatorial Variable Selection with False Discovery Rate Control L. Zhang and J. Lu Under revision, 2023.

Multiview Incomplete Knowledge Graph Integration with Application to Cross-institutional EHR Data Harmonization D. Zhou, Z. Gan, X. Shi, A. Patwari, E. Rush, CL. Bonzel, V. A. Panickan, C. Hong, YL. Ho, T. Cai, L. Costa, X. Li, V.M. Castro, S.N. Murphy, G. Brat, G. Weber, P. Avillach, J.M. Gaziano, K. Cho, K. Liao, J. Lu*, T. Cai* (*: co-senior author) Journal of Biomedical Informatics 133 (2022): 104147.

Penalized estimation of frailty-based illness–death models for semi-competing risks H.T. Reeder, J. Lu, S.Haneuse Biometrics, 1– 13, 2022

Progression of traction bronchiectasis/bronchiolectasis in interstitial lung abnormalities is associated with increased all-cause mortality: Age Gene/Environment Susceptibility-Reykjavik Study. H. Takuya, T. Hida, M. Nishino, J. Lu, R. Putman, E.F. Gudmundsson, A. Hata European journal of radiology open 8 100334, 2022

Clinical Knowledge Extraction via Sparse Embedding Regression (KESER) with Multi-Center Large Scale Electronic Health Record Data. 11. C. Hong, E. Rush, M. Liu, D. Zhou , J. Sun, A. Sonabend, V. M. Castro, P. Schubert, V. Panickan, T. Cai, L. Costa, Z. He, N. Link, R. Hauser, J.M. Gaziano, S. Murphy, G. Ostrouchov, Y. Ho, E. Begoli, J. Lu, K. Cho, K. Liao, T. Cai , NPJ digital medicine 4, no. 1 (2021): 151.

Interstitial lung abnormalities in patients with stage I non-small cell lung cancer are associated with shorter overall survival: the Boston lung cancer study. H. Tomoyuki, A. Hata, J. Lu, V. Valtchinov, T. Hino, M. Nishino, H. Honda, N. Tomiyama, D. C. Christiani, H. Hatabu. Cancer Imaging 21, no. 1 1-7.

Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation A. Sonabend W., J. Lu, L.A. Celi, T. Cai, Peter Szolovits NeurIPS 2020.

Computational and Statistical Tradeoffs in Inferring Combinatorial Structures of Ising Model J. Ying, Z. Wang, J. Lu In International Conference on Machine Learning, pp. 4901-4910

Estimating and inferring the maximum degree of stimulus-locked time-varying brain connectivity networks. KM Tan, J. Lu, T. Zhang, H. Liu Biometrics. Jun;77(2):379-390.

Combinatorial Inference for Graphical Models Matey Neykov*, Junwei Lu*, Han Liu (*: equal contribution) Annals of Statistics, 47(2), pp.795-827. [Arxiv]

Distributed Testing and Estimation under Sparse High Dimensional Models Heather Battey, Jianqing Fan, Han Liu, Junwei Lu, Ziwei Zhu (alphabetical order) Annals of Statistics, 46(3), 1352-1382. [Arxiv]

Post-Regularization Inference for Dynamic Nonparanormal Graphical Models Junwei Lu, Mladen Kolar, Han Liu Journal of Machine Learning Research, 2018 [Arxiv]

Provable Sparse Tensor Decomposition Wei Sun, Junwei Lu, Han Liu, Guang Cheng Journal of the Royal Statistical Society: Series B, 2016 [Arxiv][Link]

Nonparametric Heterogeneity Testing For Massive Data Junwei Lu, Guang Cheng, Han Liu Technical report [Arxiv]

Graphical Fermat's Principle and Triangle-Free Graph Estimation Junwei Lu, Han Liu Technical report [Arxiv]

Symmetry, Saddle Points, and Global Geometry of Nonconvex Matrix Factorization Xingguo Li, Zhaoran Wang, Junwei Lu, Raman Arora, Jarvis Haupt, Han Liu, Tuo Zhao IEEE Transactions on Information Theory, 65(6):3489-3514, 2019. [Arxiv]

Adaptive Inferential Method for Monotone Graph Invariants Junwei Lu, Matey Neykov, Han Liu Techinical Report, 2017 [Arxiv] [R package] ICSA 2017 Student Paper Award

Inter-Subject Analysis: Inferring Sparse Interactions with Dense Intra-Graphs Cong Ma, Junwei Lu, Han Liu ." Journal of the American Statistical Association (2020): 1-57. [Arxiv] ICSA 2017 Student Paper Award

Kernel Meets Sieve: Post-Regularization Confidence Bands for Sparse Additive Model Junwei Lu, Mladen Kolar, Han Liu Journal of the American Statistical Association, 92:4, pages 875-893. [Arxiv] [PDF] ASA Best Student Paper in Nonparametric Statistics Finalist

Robust Scatter Matrix Estimation for High Dimensional Distributions with Heavy Tails Junwei Lu, Fang Han, Han Liu IEEE Transactions on Information Theory. vol. 67, no. 8, pp. 5283-5304, Aug. 2021, doi: 10.1109/TIT.2021.3088381. [PDF]

Application of the Strictly Contractive Peaceman-Rachford Splitting Method to Multi-block Separable Convex Programming Bingsheng He, Han Liu, Junwei Lu, Xiaoming Yuan (alphabetical order) Splitting Methods in Communication, Imaging, Science, and Engineering (In Roland Glowinski, Stanley J. Osher, Wotao Yin (Eds.)), Springer, 2017 [Optimization Online]