Maximilian Lam

1.2k total citations · 1 hit paper
7 papers, 619 citations indexed

About

Maximilian Lam is a scholar working on Artificial Intelligence, Computer Networks and Communications and Hardware and Architecture. According to data from OpenAlex, Maximilian Lam has authored 7 papers receiving a total of 619 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 2 papers in Hardware and Architecture. Recurrent topics in Maximilian Lam's work include Stochastic Gradient Optimization Techniques (4 papers), Parallel Computing and Optimization Techniques (2 papers) and Algorithms and Data Compression (2 papers). Maximilian Lam is often cited by papers focused on Stochastic Gradient Optimization Techniques (4 papers), Parallel Computing and Optimization Techniques (2 papers) and Algorithms and Data Compression (2 papers). Maximilian Lam collaborates with scholars based in United States, Switzerland and South Korea. Maximilian Lam's co-authors include Dimitris Papailiopoulos, Kannan Ramchandran, Kangwook Lee, Ramtin Pedarsani, David J. Schlegel, Andreas Noack, Keno Fischer, Jon McAuliffe, Ce Zhang and Prabhat and has published in prestigious journals such as IEEE Transactions on Information Theory, Journal of Parallel and Distributed Computing and arXiv (Cornell University).

In The Last Decade

Maximilian Lam

7 papers receiving 614 citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Speeding Up Distributed Machine Learning Using Codes

2017 497 citations Kangwook Lee, Maximilian Lam et al. IEEE Transactions on Information Theory profile →

Peers

Maximilian Lam

AI

CNC

EEE

CM

CVPR

Arya Mazumdar United States

Sebastian U. Stich Switzerland

Eirik Rosnes Norway

Himanshu Tyagi India

Vladimir Braverman United States

David Rupprecht Germany

Paishun Ting United States

Raef Bassily United States

Salim El Rouayheb United States

Panruo Wu United States

Arya Mazumdar United States

Maximilian Lam

328 ×0.7

AI

362 ×1.0

CNC

144 ×1.2

EEE

39 ×0.6

CM

36 ×0.7

CVPR

Citations per year, relative to Maximilian Lam Maximilian Lam (= 1×) peers Arya Mazumdar

Countries citing papers authored by Maximilian Lam

Since Specialization

Citations

This map shows the geographic impact of Maximilian 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 Maximilian Lam with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximilian Lam more than expected).

Fields of papers citing papers by Maximilian Lam

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Maximilian 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 Maximilian Lam. The network helps show where Maximilian Lam may publish in the future.

Co-authorship network of co-authors of Maximilian Lam

This figure shows the co-authorship network connecting the top 25 collaborators of Maximilian Lam. A scholar is included among the top collaborators of Maximilian 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 Maximilian Lam. Maximilian 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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7 of 7 papers shown

1.

Lam, Maximilian, Jeff Johnson, Wenjie Xiong, et al.. (2024). GPU-based Private Information Retrieval for On-Device Machine Learning Inference. VTechWorks (Virginia Tech). 197–214. 4 indexed citations

2.

Gálvez, Daniel, et al.. (2021). The People's Speech: A Large-Scale Diverse English Speech Recognition Dataset for Commercial Usage. arXiv (Cornell University). 2 indexed citations

3.

Regier, Jeffrey, Keno Fischer, Andreas Noack, et al.. (2019). Cataloging the visible universe through Bayesian inference in Julia at petascale. Journal of Parallel and Distributed Computing. 127. 89–104. 8 indexed citations

4.

Regier, Jeffrey, Keno Fischer, Andreas Noack, et al.. (2018). Cataloging the Visible Universe Through Bayesian Inference at Petascale. 44–53. 9 indexed citations

5.

Lee, Kangwook, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, & Kannan Ramchandran. (2017). Speeding Up Distributed Machine Learning Using Codes. IEEE Transactions on Information Theory. 64(3). 1514–1529. 497 indexed citations breakdown →

6.

Pan, Xinghao, Maximilian Lam, Stephen Tu, et al.. (2016). Cyclades: Conflict-free Asynchronous Machine Learning. arXiv (Cornell University). 29. 2568–2576. 5 indexed citations

7.

Lee, Kangwook, Maximilian Lam, Ramtin Pedarsani, Dimitris Papailiopoulos, & Kannan Ramchandran. (2016). Speeding up distributed machine learning using codes. 1143–1147. 94 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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