Matthew Matl

1.6k total citations · 1 hit paper
11 papers, 918 citations indexed

About

Matthew Matl is a scholar working on Computer Networks and Communications, Control and Systems Engineering and Hardware and Architecture. According to data from OpenAlex, Matthew Matl has authored 11 papers receiving a total of 918 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Computer Networks and Communications, 5 papers in Control and Systems Engineering and 5 papers in Hardware and Architecture. Recurrent topics in Matthew Matl's work include Interconnection Networks and Systems (5 papers), Parallel Computing and Optimization Techniques (5 papers) and Embedded Systems Design Techniques (5 papers). Matthew Matl is often cited by papers focused on Interconnection Networks and Systems (5 papers), Parallel Computing and Optimization Techniques (5 papers) and Embedded Systems Design Techniques (5 papers). Matthew Matl collaborates with scholars based in United States and China. Matthew Matl's co-authors include Ken Goldberg, Jeffrey Mahler, Michael Danielczuk, Stephen McKinley, Vishal Satish, Xinyu Liu, Albert P. Li, David V. Gealy, Andrew C. Li and Saurabh Gupta and has published in prestigious journals such as Communications of the ACM, Science Robotics and ACM SIGPLAN Notices.

In The Last Decade

Matthew Matl

11 papers receiving 882 citations

Hit Papers

Learning ambidextrous robot grasping policies 2019 2026 2021 2023 2019 100 200 300

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Matthew Matl United States 8 584 330 221 147 142 11 918
Marc Freese Japan 6 467 0.8× 252 0.8× 379 1.7× 213 1.4× 201 1.4× 12 1.0k
Tetsuo Kotoku Japan 15 743 1.3× 197 0.6× 211 1.0× 116 0.8× 592 4.2× 79 1.0k
Torsten Kröger Germany 19 1.3k 2.2× 528 1.6× 575 2.6× 168 1.1× 395 2.8× 63 1.7k
Hsu‐Chih Huang Taiwan 19 764 1.3× 234 0.7× 568 2.6× 234 1.6× 158 1.1× 55 1.2k
Berthold Bäuml Germany 18 599 1.0× 435 1.3× 347 1.6× 95 0.6× 149 1.0× 42 960
K. Kitagaki Japan 15 992 1.7× 341 1.0× 251 1.1× 100 0.7× 514 3.6× 60 1.1k
Eitan Marder-Eppstein United States 7 419 0.7× 150 0.5× 533 2.4× 146 1.0× 224 1.6× 8 987
David C. Conner United States 15 311 0.5× 194 0.6× 434 2.0× 101 0.7× 130 0.9× 42 825
Matteo Rubagotti Kazakhstan 21 1.2k 2.1× 169 0.5× 171 0.8× 57 0.4× 228 1.6× 71 1.6k
Wim Meeussen United States 10 407 0.7× 164 0.5× 296 1.3× 145 1.0× 182 1.3× 21 736

Countries citing papers authored by Matthew Matl

Since Specialization
Citations

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

Fields of papers citing papers by Matthew Matl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Matthew Matl

This figure shows the co-authorship network connecting the top 25 collaborators of Matthew Matl. A scholar is included among the top collaborators of Matthew Matl 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 Matthew Matl. Matthew Matl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

11 of 11 papers shown
1.
Danielczuk, Michael, Matthew Matl, Saurabh Gupta, et al.. (2019). Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Data. 7283–7290. 120 indexed citations
2.
Mahler, Jeffrey, Matthew Matl, Vishal Satish, et al.. (2019). Learning ambidextrous robot grasping policies. Science Robotics. 4(26). 371 indexed citations breakdown →
3.
Balkind, Jonathan, Yaosheng Fu, Tri Minh Nguyen, et al.. (2019). OpenPiton. Communications of the ACM. 62(12). 79–87. 4 indexed citations
4.
Danielczuk, Michael, Matthew Matl, Saurabh Gupta, et al.. (2018). Segmenting Unknown 3D Objects from Real Depth Images using Mask R-CNN Trained on Synthetic Point Clouds.. arXiv (Cornell University). 10 indexed citations
5.
Mahler, Jeffrey, Matthew Matl, Xinyu Liu, et al.. (2018). Dex-Net 3.0: Computing Robust Vacuum Suction Grasp Targets in Point Clouds Using a New Analytic Model and Deep Learning. 5620–5627. 209 indexed citations
6.
Matl, Matthew, Jeffrey Mahler, Zhou Yu, et al.. (2017). A cloud robot system using the dexterity network and berkeley robotics and automation as a service (Brass). 1615–1622. 26 indexed citations
7.
Matl, Matthew, et al.. (2017). An algorithm for transferring parallel-jaw grasps between 3D mesh subsegments. 22. 756–763. 1 indexed citations
8.
Balkind, Jonathan, Yaosheng Fu, Tri Minh Nguyen, et al.. (2016). OpenPiton. ACM SIGPLAN Notices. 51(4). 217–232. 37 indexed citations
9.
Balkind, Jonathan, Matthew Matl, David Wentzlaff, et al.. (2016). OpenPiton. ACM SIGOPS Operating Systems Review. 50(2). 217–232. 6 indexed citations
10.
Balkind, Jonathan, Yaosheng Fu, Tri Minh Nguyen, et al.. (2016). OpenPiton. ACM SIGARCH Computer Architecture News. 44(2). 217–232. 19 indexed citations
11.
Balkind, Jonathan, Yaosheng Fu, Tri Minh Nguyen, et al.. (2016). OpenPiton. 217–232. 115 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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