Mark J. Willis

1.5k total citations
53 papers, 1.1k citations indexed

About

Mark J. Willis is a scholar working on Control and Systems Engineering, Molecular Biology and Artificial Intelligence. According to data from OpenAlex, Mark J. Willis has authored 53 papers receiving a total of 1.1k indexed citations (citations by other indexed papers that have themselves been cited), including 30 papers in Control and Systems Engineering, 18 papers in Molecular Biology and 11 papers in Artificial Intelligence. Recurrent topics in Mark J. Willis's work include Advanced Control Systems Optimization (19 papers), Fault Detection and Control Systems (14 papers) and Process Optimization and Integration (13 papers). Mark J. Willis is often cited by papers focused on Advanced Control Systems Optimization (19 papers), Fault Detection and Control Systems (14 papers) and Process Optimization and Integration (13 papers). Mark J. Willis collaborates with scholars based in United Kingdom, Colombia and Australia. Mark J. Willis's 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 and has published in prestigious journals such as Journal of Cleaner Production, Chemical Engineering Journal and Applied Energy.

In The Last Decade

Mark J. Willis

53 papers receiving 1.0k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Mark J. Willis United Kingdom	17	296	224	214	187	150	53	1.1k
Zhiguo Zhou China	19	195 0.7×	69 0.3×	298 1.4×	98 0.5×	137 0.9×	86	1.3k
S. Feyo de Azevedo Portugal	23	893 3.0×	379 1.7×	172 0.8×	220 1.2×	249 1.7×	66	1.7k
Chunyang Su China	11	167 0.6×	67 0.3×	378 1.8×	98 0.5×	134 0.9×	21	1.2k
Tong Qiu China	21	498 1.7×	87 0.4×	89 0.4×	210 1.1×	198 1.3×	82	1.3k
R. Russell Rhinehart United States	20	747 2.5×	95 0.4×	166 0.8×	111 0.6×	149 1.0×	95	1.3k
Yunhe Wang China	21	78 0.3×	136 0.6×	259 1.2×	93 0.5×	118 0.8×	103	1.3k
Shengli Liu China	18	92 0.3×	81 0.4×	509 2.4×	132 0.7×	96 0.6×	96	1.2k
C.C. Pantelides United Kingdom	17	538 1.8×	140 0.6×	38 0.2×	250 1.3×	270 1.8×	25	1.4k

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-authorship network of co-authors of Mark J. Willis

This figure shows the co-authorship network connecting the top 25 collaborators of Mark J. Willis. A scholar is included among the top collaborators of Mark J. Willis 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 Mark J. Willis. Mark J. Willis 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

Harper, Roy, et al.. (2022). Challenging the 50‐50 rule for the basal‐bolus insulin ratio in patients with type 2 diabetes who maintain stable glycaemic control. Diabetes Obesity and Metabolism. 25(2). 581–585. 4 indexed citations

Willis, Mark J., et al.. (2020). COVID-19: Mechanistic model calibration subject to active and varying non-pharmaceutical interventions. Chemical Engineering Science. 231. 116330–116330. 7 indexed citations

Taylor, Connor J., Jamie A. Manson, Mark J. Willis, et al.. (2020). Rapid, automated determination of reaction models and kinetic parameters. Chemical Engineering Journal. 413. 127017–127017. 53 indexed citations

Willis, Mark J., Víctor Hugo Grisales Díaz, Oscar Andrés Prado‐Rubio, & Moritz von Stosch. (2020). Insights into the dynamics and control of COVID-19 infection rates. Chaos Solitons & Fractals. 138. 109937–109937. 14 indexed citations

Goodway, Bill, et al.. (2012). Introduction to this special section: Passive seismic and microseismic—Part 2. The Leading Edge. 31(12). 1428–1435. 3 indexed citations

Searson, Dominic P., David E. Leahy, & Mark J. Willis. (2010). GPTIPS: An Open Source Genetic Programming Toolbox For Multigene Symbolic Regression. International MultiConference of Engineers and Computer Scientists. 258 indexed citations

Novakovic, Katarina, et al.. (2009). The influence of reaction temperature on the oscillatory behaviour in the palladium-catalysed phenylacetylene oxidative carbonylation reaction. Physical Chemistry Chemical Physics. 11(40). 9044–9044. 23 indexed citations

Glassey, Jarka, et al.. (2009). Application of agent‐based system for bioprocess description and process improvement. Biotechnology Progress. 26(3). 706–716. 9 indexed citations

Searson, Dominic P., et al.. (2008). Two-dimensional simple proportional feedback control of a chaotic reaction system. Physical Review E. 77(4). 46215–46215. 1 indexed citations

10.

Searson, Dominic P., et al.. (2007). Inference of Chemical Reaction Networks Using Hybrid S-system Models. Chemical Product and Process Modeling. 2(1). 8 indexed citations

11.

Searson, Dominic P., Mark J. Willis, & G.A. Montague. (2007). Co‐evolution of non‐linear PLS model components. Journal of Chemometrics. 21(12). 592–603. 70 indexed citations

12.

Novakovic, Katarina, Christophe Grosjean, Stephen K. Scott, et al.. (2007). The influence of oscillations on product selectivity during the palladium-catalysed phenylacetylene oxidative carbonylation reaction. Physical Chemistry Chemical Physics. 10(5). 749–753. 24 indexed citations

13.

Searson, Dominic P., Samantha C. Burnham, Mark J. Willis, & A.R. Wright. (2006). Identification of chemical reaction mechanism from batch process data. international conference on Modelling and simulation. 511–516. 3 indexed citations

14.

Willis, Mark J., et al.. (2003). Predictive scheduling of a bioprocess plant. international conference on Modelling and simulation. 296–301. 1 indexed citations

15.

Lennox, Barry, et al.. (2002). Automated Production Support for the Bioprocess Industry. Biotechnology Progress. 18(2). 269–275. 19 indexed citations

16.

McKay, Ben, Mark J. Willis, Dominic P. Searson, & G.A. Montague. (1999). Non-linear continuum regression using genetic programming. Genetic and Evolutionary Computation Conference. 1106–1111. 9 indexed citations

17.

Willis, Mark J., et al.. (1999). Dynamic chemical process modelling using a Multiple Basis Function Genetic Programming algorithm. Genetic and Evolutionary Computation Conference. 1782–1782. 2 indexed citations

18.

McKay, Ben, et al.. (1998). Evolving a hybrid model of a fed-batch fermentation process. Transactions of the Institute of Measurement and Control. 20(1). 4–10. 9 indexed citations

19.

McKay, Ben, Mark J. Willis, & Geoffrey W. Barton. (1997). Steady-state modelling of chemical process systems using genetic programming. Computers & Chemical Engineering. 21(9). 981–996. 105 indexed citations

20.

McKay, Ben, et al.. (1997). Modeling and control of a food extrusion process. Computers & Chemical Engineering. 21. S361–S366. 16 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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