Henry Lam

2.2k total citations
116 papers, 1.2k citations indexed

About

Henry Lam is a scholar working on Management Science and Operations Research, Statistics and Probability and Statistics, Probability and Uncertainty. According to data from OpenAlex, Henry Lam has authored 116 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 81 papers in Management Science and Operations Research, 42 papers in Statistics and Probability and 30 papers in Statistics, Probability and Uncertainty. Recurrent topics in Henry Lam's work include Probability and Risk Models (30 papers), Simulation Techniques and Applications (30 papers) and Risk and Portfolio Optimization (25 papers). Henry Lam is often cited by papers focused on Probability and Risk Models (30 papers), Simulation Techniques and Applications (30 papers) and Risk and Portfolio Optimization (25 papers). Henry Lam collaborates with scholars based in United States, China and Hong Kong. Henry Lam's co-authors include Ding Zhao, David J. LeBlanc, Huei Peng, José Blanchet, Zhiyuan Huang, Kazutoshi Nobukawa, Shan Bao, Christopher S. Pan, Xianan Huang and Enlu Zhou and has published in prestigious journals such as Management Science, The American Journal of Gastroenterology and Operations Research.

In The Last Decade

Henry Lam

99 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Henry Lam United States	15	502	462	321	208	204	116	1.2k
Bong‐Jin Yum South Korea	21	43 0.1×	246 0.5×	76 0.2×	474 2.3×	113 0.6×	62	1.2k
Yizhong Ma China	19	63 0.1×	357 0.8×	106 0.3×	302 1.5×	39 0.2×	87	1.1k
Tsung-Jung Hsieh Taiwan	14	61 0.1×	235 0.5×	99 0.3×	295 1.4×	271 1.3×	30	951
E. Fernández-Gaucherand United States	14	21 0.0×	191 0.4×	254 0.8×	93 0.4×	298 1.5×	48	1.1k
Jau‐Chuan Ke Taiwan	30	198 0.4×	848 1.8×	73 0.2×	1.3k 6.2×	14 0.1×	165	3.0k
Zaiming Liu China	18	47 0.1×	489 1.1×	86 0.3×	131 0.6×	72 0.4×	126	1.0k
Suprasad V. Amari United States	27	187 0.4×	39 0.1×	185 0.6×	1.5k 7.1×	85 0.4×	67	2.0k
Kostas Margellos United Kingdom	19	101 0.2×	190 0.4×	649 2.0×	79 0.4×	128 0.6×	77	1.5k
Ahmad M. Alshamrani Saudi Arabia	14	29 0.1×	157 0.3×	91 0.3×	39 0.2×	102 0.5×	94	825
Maryam Kamgarpour Switzerland	24	181 0.4×	178 0.4×	716 2.2×	33 0.2×	208 1.0×	103	1.7k

Countries citing papers authored by Henry Lam

Since Specialization

Citations

This map shows the geographic impact of Henry Lam's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Henry Lam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Henry Lam more than expected).

Fields of papers citing papers by Henry Lam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Henry Lam. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Henry Lam. The network helps show where Henry Lam may publish in the future.

Co-authorship network of co-authors of Henry Lam

This figure shows the co-authorship network connecting the top 25 collaborators of Henry Lam. A scholar is included among the top collaborators of Henry Lam based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Henry Lam. Henry Lam is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Lam, Henry & J H Zhang. (2024). Distributionally Constrained Black-Box Stochastic Gradient Estimation and Optimization. Operations Research. 73(5). 2680–2694.

Song, Eunhye, Henry Lam, & Russell R. Barton. (2024). A Shrinkage Approach to Improve Direct Bootstrap Resampling Under Input Uncertainty. INFORMS journal on computing. 36(4). 1023–1039.

Blanchet, José, Henry Lam, Yang Liu, & Ruodu Wang. (2024). Convolution Bounds on Quantile Aggregation. Operations Research. 73(5). 2761–2781. 2 indexed citations

Huang, Zhiyuan, et al.. (2023). Overconservativeness of Variance-Based Efficiency Criteria and Probabilistic Efficiency in Rare-Event Simulation. Management Science. 70(10). 6852–6873. 2 indexed citations

Lam, Henry, et al.. (2023). Adaptive Importance Sampling for Efficient Stochastic Root Finding and Quantile Estimation. Operations Research. 72(6). 2612–2630. 3 indexed citations

Dong, Jing, et al.. (2023). Uncertainty Quantification and Exploration for Reinforcement Learning. Operations Research. 72(4). 1689–1709. 1 indexed citations

Arief, Mansur, Zhenyuan Liu, Zhiyuan Huang, et al.. (2022). Certifiable Evaluation for Autonomous Vehicle Perception Systems using Deep Importance Sampling (Deep IS). 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 1736–1742. 2 indexed citations

Lam, Henry, et al.. (2022). General Feasibility Bounds for Sample Average Approximation via Vapnik--Chervonenkis Dimension. SIAM Journal on Optimization. 32(2). 1471–1497. 2 indexed citations

Lam, Henry, Xinyu Zhang, & Xuhui Zhang. (2022). Enhanced Balancing of Bias-Variance Tradeoff in Stochastic Estimation: A Minimax Perspective. Operations Research. 71(6). 2352–2373.

10.

Lam, Henry, et al.. (2021). Subsampling to Enhance Efficiency in Input Uncertainty Quantification. Operations Research. 70(3). 1891–1913. 7 indexed citations

11.

Peng, Yijie, Li Xiao, Bernd Heidergott, L. Jeff Hong, & Henry Lam. (2021). A New Likelihood Ratio Method for Training Artificial Neural Networks. INFORMS journal on computing. 34(1). 638–655. 6 indexed citations

12.

Huang, Zhiyuan, et al.. (2021). Model calibration via distributionally robust optimization: On the NASA Langley Uncertainty Quantification Challenge. Mechanical Systems and Signal Processing. 164. 108211–108211. 5 indexed citations

13.

Lam, Henry, Haidong Li, & Xuhui Zhang. (2020). Minimax efficient finite-difference stochastic gradient estimators using black-box function evaluations. Operations Research Letters. 49(1). 40–47. 2 indexed citations

14.

Goeva, Aleksandrina, et al.. (2019). Optimization-Based Calibration of Simulation Input Models. Operations Research. 67(5). 1362–1382. 8 indexed citations

15.

Lam, Henry, et al.. (2018). On efficiencies of stochastic optimization procedures under importance sampling. Winter Simulation Conference. 1862–1873. 1 indexed citations

16.

Huang, Zhiyuan, Henry Lam, & Ding Zhao. (2018). Rare-event simulation without structural information: a learning-based approach. Winter Simulation Conference. 1826–1837. 3 indexed citations

17.

Huang, Zhiyuan, Henry Lam, & Ding Zhao. (2017). Sequential experimentation to efficiently test automated vehicles. arXiv (Cornell University). 3078–3089. 4 indexed citations

18.

Choe, Youngjun, Henry Lam, & Eunshin Byon. (2017). Uncertainty Quantification of Stochastic Simulation for Black-box Computer Experiments. Methodology And Computing In Applied Probability. 20(4). 1155–1172. 14 indexed citations

19.

Lam, Henry, et al.. (2017). Tail Analysis Without Parametric Models: A Worst-Case Perspective. Operations Research. 65(6). 1696–1711. 18 indexed citations

20.

Lam, Henry & Enlu Zhou. (2015). Quantifying uncertainty in sample average approximation. Winter Simulation Conference. 3846–3857. 10 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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