Mark J. Willis

1.5k citations
58 papers · 1.2k · h-index 18

Impact in

Papers in

    • Advanced Control Systems Optimization 20
    • Fault Detection and Control Systems 15
    • Process Optimization and Integration 13
    • Microbial Metabolic Engineering and Bioproduction 14
    • Viral Infectious Diseases and Gene Expression in Insects 7

Mark J. Willis

57 papers receiving 1.2k citations

Peers

Mark J. Willis
Comparison fields: 5 of 118
  • Control and Systems Engineering 325
  • Artificial Intelligence 261
  • Analytical Chemistry 49
  • Civil and Structural Engineering 108
  • Catalysis 34
Replace Emad Mahrous Awwad with:
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Ian T. Cameron Australia
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Citations per field
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Citations per year

Countries citing papers authored by Mark J. Willis

Since Specialization
Citations

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

Fields of papers citing papers by Mark J. Willis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mark J. Willis. 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 Mark J. Willis. The network helps show where Mark J. Willis may publish in the future.

Co-authors

The 25 scholars most cited alongside Mark J. Willis, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Mark J. Willis Line = papers co-authored together Mark J. Willis links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 58 papers — load more, or switch the sort, to bring in the rest.

#Work
1
GPTIPS: An Open Source Genetic Programming Toolbox For Multigene Symbolic Regression
2010278
2 1997118
3 200777
4 202056
5 201641
6 199739
7 200336
8 200333
9 202130
10 200829
11 201726
12 200724
13 200724
14 200923
15 200221
16 201621
17 201921
18 199719
19 202017
20 201017

About Mark J. Willis

Mark J. Willis is a scholar working on Control and Systems Engineering, Molecular Biology, Artificial Intelligence, Biomedical Engineering and Analytical Chemistry, having authored 58 papers that have together received 1.2k indexed citations. Recurring topics across this work include Advanced Control Systems Optimization (20 papers), Fault Detection and Control Systems (15 papers), Microbial Metabolic Engineering and Bioproduction (14 papers), Process Optimization and Integration (13 papers), Evolutionary Algorithms and Applications (10 papers), Viral Infectious Diseases and Gene Expression in Insects (7 papers), Spectroscopy and Chemometric Analyses (6 papers) and Biofuel production and bioconversion (5 papers). The work is most often cited by research in Control and Systems Engineering (325 citations), Artificial Intelligence (261 citations), Analytical Chemistry (49 citations), Civil and Structural Engineering (108 citations) and Catalysis (34 citations). Mark J. Willis has collaborated with scholars based in United Kingdom, Colombia and Australia. Frequent co-authors include Dominic P. Searson, David E. Leahy, Ben McKay, Moritz von Stosch, Geoffrey W. Barton, G.A. Montague, Víctor Hugo Grisales Díaz, A.R. Wright, Katarina Novakovic and Brian Taylor. Their work appears in journals such as Computers & Chemical Engineering, Chemical Engineering Science, Applied Energy, Biotechnology Progress and Physical Chemistry Chemical Physics.

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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