Lam M. Nguyen
- Industrial and Manufacturing Engineering top 2%
- Mechanical Engineering
- Automotive Engineering top 10%
- Artificial Intelligence top 10%
- Information Systems top 10%
- Co-authors
- Markus Bambach�Johannes BuhlClint SaidyChristopher SaccoRamy HarikMax KirkpatrickEoghan CaseyDzung T. Phan
- Topics
- Stochastic Gradient Optimization Techniques (7 papers)Sparse and Compressive Sensing Techniques (6 papers)Adversarial Robustness in Machine Learning (5 papers)
- Partner nations
- United StatesVietnamGermany
In The Last Decade
Lam M. Nguyen
29 papers receiving 501 citations
Hit Papers
Peers
Comparison fields: 5 of 73
- Industrial and Manufacturing Engineering 217
- Mechanical Engineering 134
- Automotive Engineering 121
- Artificial Intelligence 107
- Information Systems 68
Countries citing papers authored by Lam M. Nguyen
This map shows the geographic impact of Lam M. Nguyen'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 Lam M. Nguyen with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lam M. Nguyen more than expected).
Fields of papers citing papers by Lam M. Nguyen
This network shows the impact of papers produced by Lam M. Nguyen. 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 Lam M. Nguyen. The network helps show where Lam M. Nguyen may publish in the future.
Co-authorship network of co-authors of Lam M. Nguyen
This figure shows the co-authorship network connecting the top 25 collaborators of Lam M. Nguyen. A scholar is included among the top collaborators of Lam M. Nguyen 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 Lam M. Nguyen. Lam M. Nguyen is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 5 | |
| 4 | 1 | |
| 5 | 0 | |
| 6 | 1 | |
| 7 | 1 | |
| 8 | 9 | |
| 9 | 3 | |
| 10 | 3 | |
| 11 | 18 | |
| 12 | 2 | |
| 13 | Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function | 3 |
| 14 | A Hybrid Stochastic Policy Gradient Algorithm for Reinforcement Learning | 1 |
| 15 | 20 | |
| 16 | PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach | 12 |
| 17 | 1 | |
| 18 | SGD and Hogwild! Convergence Without the Bounded Gradients Assumption | 14 |
| 19 | 6 | |
| 20 | 44 |
About Lam M. Nguyen
Lam M. Nguyen is a scholar working on Artificial Intelligence, Automotive Engineering and Computational Mechanics, having authored 31 papers that have together received 533 indexed citations. Recurring topics across this work include Stochastic Gradient Optimization Techniques (7 papers), Sparse and Compressive Sensing Techniques (6 papers) and Adversarial Robustness in Machine Learning (5 papers). The work is most often cited by research in Industrial and Manufacturing Engineering (217 citations), Automotive Engineering (121 citations) and Mechanical Engineering (134 citations). Lam M. Nguyen has collaborated with scholars based in United States, Vietnam and Germany. Frequent co-authors include Markus Bambach�, Johannes Buhl, Clint Saidy, Christopher Sacco, Ramy Harik, Max Kirkpatrick, Eoghan Casey, Dzung T. Phan, Jayant Kalagnanam and Alexander Stolyar. Their work appears in journals such as IEEE Access, Journal of Machine Learning Research and Mathematical Programming.
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.