Junwei Lu

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

My research aims to develop a new generation of inference methods and theory for modern statistics and machine learning, especially focusing on:
  • Combinatorial functionals like connectivity, degree, and other topological structures of graphs, ranking, clustering, hyper graphs, etc;
  • Complex data structures like high dimensionality, heterogeneity, nonlinearity, heavy-tailness, time-dependency, etc;
  • Complicated algorithms like distributed algorithms, nonconvex optimization, kernel methods, etc.
With the problems above, I am interested in studying the uncertainty assessment methodology, probabilistic universality phenomenon, and information-theoretical lower bound theory. My research finds main applications in computational neuroscience, medical knowledge graph, and dynamic treatment.

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.
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.
Post-Regularization Inference for Dynamic Nonparanormal Graphical Models
Junwei Lu, Mladen Kolar, Han Liu
Journal of Machine Learning Research, 2018
Provable Sparse Tensor Decomposition
Wei Sun, Junwei Lu, Han Liu, Guang Cheng
Journal of the Royal Statistical Society: Series B, 2016
Nonparametric Heterogeneity Testing For Massive Data
Junwei Lu, Guang Cheng, Han Liu
Technical report
Graphical Fermat's Principle and Triangle-Free Graph Estimation
Junwei Lu, Han Liu
Technical report
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.
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.
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.
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]