Max Mowbray

744 total citations
23 papers, 470 citations indexed

About

Max Mowbray is a scholar working on Control and Systems Engineering, Mechanical Engineering and Industrial and Manufacturing Engineering. According to data from OpenAlex, Max Mowbray has authored 23 papers receiving a total of 470 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Control and Systems Engineering, 5 papers in Mechanical Engineering and 4 papers in Industrial and Manufacturing Engineering. Recurrent topics in Max Mowbray's work include Advanced Control Systems Optimization (12 papers), Fault Detection and Control Systems (10 papers) and Mineral Processing and Grinding (4 papers). Max Mowbray is often cited by papers focused on Advanced Control Systems Optimization (12 papers), Fault Detection and Control Systems (10 papers) and Mineral Processing and Grinding (4 papers). Max Mowbray collaborates with scholars based in United Kingdom, Belgium and Canada. Max Mowbray's co-authors include Dongda Zhang, Ehecatl Antonio del Rio‐Chanona, Thomas Savage, Bovinille Anye Cho, Ziqi Song, Panagiotis Petsagkourakis, Mattia Vallerio, Robin Smith, Philip A. Martin and César Mendoza and has published in prestigious journals such as SHILAP Revista de lepidopterología, British Journal of Cancer and Industrial & Engineering Chemistry Research.

In The Last Decade

Max Mowbray

23 papers receiving 462 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Max Mowbray United Kingdom 12 189 110 75 60 58 23 470
Saxena Nikita India 12 148 0.8× 215 2.0× 108 1.4× 26 0.4× 37 0.6× 30 470
Sándor Németh Hungary 12 119 0.6× 64 0.6× 44 0.6× 69 1.1× 71 1.2× 60 621
Jong Woo Kim South Korea 14 320 1.7× 152 1.4× 47 0.6× 105 1.8× 43 0.7× 41 603
Yannis A. Guzman United States 12 157 0.8× 37 0.3× 55 0.7× 22 0.4× 87 1.5× 16 411
Maria M. Papathanasiou United Kingdom 15 269 1.4× 262 2.4× 106 1.4× 11 0.2× 35 0.6× 42 691
Panagiotis Petsagkourakis United Kingdom 8 206 1.1× 84 0.8× 48 0.6× 43 0.7× 23 0.4× 19 341
Thomas Savage United Kingdom 11 97 0.5× 129 1.2× 96 1.3× 22 0.4× 46 0.8× 22 427
Stephen Goldrick United Kingdom 13 140 0.7× 248 2.3× 115 1.5× 24 0.4× 74 1.3× 28 441
Martin F. Luna Argentina 9 133 0.7× 333 3.0× 110 1.5× 18 0.3× 40 0.7× 20 469

Countries citing papers authored by Max Mowbray

Since Specialization
Citations

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

Fields of papers citing papers by Max Mowbray

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Max Mowbray

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

All Works

20 of 20 papers shown
1.
Mercangöz, Mehmet, et al.. (2025). PC-Gym: Benchmark environments for process control problems. Computers & Chemical Engineering. 204. 109363–109363. 1 indexed citations
2.
Zhou, Yue, et al.. (2024). A review of reinforcement learning based approaches for industrial demand response. ORCA Online Research @Cardiff (Cardiff University). 1 indexed citations
3.
Mowbray, Max, et al.. (2024). Integrating transfer learning within data-driven soft sensor design to accelerate product quality control. SHILAP Revista de lepidopterología. 10. 100142–100142. 4 indexed citations
4.
Mowbray, Max, et al.. (2024). Constructing a Symbolic Regression-Based Interpretable Soft Sensor for Industrial Data Analytics and Product Quality Control. Industrial & Engineering Chemistry Research. 63(9). 4083–4092. 3 indexed citations
5.
Mowbray, Max, et al.. (2024). Machine learning for viscoelastic constitutive model identification and parameterisation using Large Amplitude Oscillatory Shear. Chemical Engineering Science. 294. 120075–120075. 5 indexed citations
6.
Mowbray, Max, et al.. (2022). Probabilistic machine learning based soft-sensors for product quality prediction in batch processes. Chemometrics and Intelligent Laboratory Systems. 228. 104616–104616. 23 indexed citations
7.
Mowbray, Max, et al.. (2022). Industrial data science – a review of machine learning applications for chemical and process industries. Reaction Chemistry & Engineering. 7(7). 1471–1509. 60 indexed citations
8.
Mowbray, Max, et al.. (2022). A reinforcement learning‐based hybrid modeling framework for bioprocess kinetics identification. Biotechnology and Bioengineering. 120(1). 154–168. 20 indexed citations
9.
Mowbray, Max, et al.. (2022). Distributional reinforcement learning for inventory management in multi-echelon supply chains. SHILAP Revista de lepidopterología. 6. 100073–100073. 17 indexed citations
10.
Mowbray, Max, Thomas Savage, Ziqi Song, et al.. (2021). Machine learning for biochemical engineering: A review. Biochemical Engineering Journal. 172. 108054–108054. 125 indexed citations
11.
Johnston, Matthew L., et al.. (2021). A two-step multivariate statistical learning approach for batch process soft sensing. SHILAP Revista de lepidopterología. 1. 100003–100003. 10 indexed citations
12.
Mowbray, Max, et al.. (2021). Development and Characterization of a Probe Device toward Intracranial Spectroscopy of Traumatic Brain Injury. ACS Biomaterials Science & Engineering. 7(3). 1252–1262. 11 indexed citations
13.
Mowbray, Max, Panagiotis Petsagkourakis, Ehecatl Antonio del Rio‐Chanona, & Dongda Zhang. (2021). Safe chance constrained reinforcement learning for batch process control. Computers & Chemical Engineering. 157. 107630–107630. 28 indexed citations
14.
Mowbray, Max, Robin Smith, Ehecatl Antonio del Rio‐Chanona, & Dongda Zhang. (2021). Using process data to generate an optimal control policy via apprenticeship and reinforcement learning. AIChE Journal. 67(9). 34 indexed citations
15.
Petsagkourakis, Panagiotis, et al.. (2021). Constrained Q-Learning for Batch Process Optimization. IFAC-PapersOnLine. 54(3). 492–497. 4 indexed citations
16.
Savage, Thomas, Dongda Zhang, Max Mowbray, & Ehecatl Antonio del Rio‐Chanona. (2021). Model-free safe reinforcement learning for chemical processes using Gaussian processes. IFAC-PapersOnLine. 54(3). 504–509. 11 indexed citations
17.
Mowbray, Max, Nicholas Bredenkamp, L. Declercq, et al.. (2010). Arginase is overactive in psoriatic skin. British Journal of Dermatology. 163(1). 193–196. 43 indexed citations
18.
Mowbray, Max, et al.. (2008). Exogenous nitric oxide mildly reduces ultraviolet B-induced apoptosis in human epidermis, but has no influence on DNA damage or repair. British Journal of Dermatology. 158(4). 900–900. 1 indexed citations
19.
Mowbray, Max, et al.. (2007). Changes in the site distribution of malignant melanoma in South East Scotland (1979–2002). British Journal of Cancer. 96(5). 832–835. 10 indexed citations
20.
Mowbray, Max, et al.. (2006). Quantification of enzyme independent stores of nitric oxide in human skin. Journal of Investigative Dermatology. 126. 102–102. 2 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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