Eric Bradford

1.4k total citations · 1 hit paper
20 papers, 912 citations indexed

About

Eric Bradford is a scholar working on Control and Systems Engineering, Computational Theory and Mathematics and Biomedical Engineering. According to data from OpenAlex, Eric Bradford has authored 20 papers receiving a total of 912 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Control and Systems Engineering, 10 papers in Computational Theory and Mathematics and 4 papers in Biomedical Engineering. Recurrent topics in Eric Bradford's work include Advanced Control Systems Optimization (11 papers), Advanced Multi-Objective Optimization Algorithms (9 papers) and Fault Detection and Control Systems (8 papers). Eric Bradford is often cited by papers focused on Advanced Control Systems Optimization (11 papers), Advanced Multi-Objective Optimization Algorithms (9 papers) and Fault Detection and Control Systems (8 papers). Eric Bradford collaborates with scholars based in Norway, United Kingdom and Germany. Eric Bradford's co-authors include Artur M. Schweidtmann, Alexei A. Lapkin, Lars Imsland, Ehecatl Antonio del Rio‐Chanona, Richard A. Bourne, Adam D. Clayton, Nicholas Holmes, Dongda Zhang, Keju Jing and José Eduardo Alves Graciano and has published in prestigious journals such as Chemical Engineering Journal, Biotechnology and Bioengineering and ACS Sustainable Chemistry & Engineering.

In The Last Decade

Eric Bradford

20 papers receiving 881 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.

Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives

2018 255 citations Artur M. Schweidtmann, Adam D. Clayton et al. Chemical Engineering Journal profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Eric Bradford Norway	13	355	302	188	188	111	20	912
Federico Galvanin United Kingdom	17	282 0.8×	266 0.9×	165 0.9×	211 1.1×	118 1.1×	69	870
Manuel Dahmen Germany	18	152 0.4×	315 1.0×	118 0.6×	195 1.0×	80 0.7×	46	942
Debasis Sarkar India	19	278 0.8×	197 0.7×	95 0.5×	492 2.6×	192 1.7×	54	1.0k
Pieter Plehiers Belgium	9	109 0.3×	398 1.3×	281 1.5×	517 2.8×	210 1.9×	10	1.1k
Mark J. Willis United Kingdom	17	296 0.8×	187 0.6×	72 0.4×	91 0.5×	224 2.0×	53	1.1k
Xigang Yuan China	20	837 2.4×	343 1.1×	74 0.4×	83 0.4×	72 0.6×	103	1.3k
C.C. Pantelides United Kingdom	17	538 1.5×	250 0.8×	317 1.7×	82 0.4×	140 1.3×	25	1.4k
Pascal Floquet France	18	422 1.2×	200 0.7×	129 0.7×	69 0.4×	63 0.6×	64	799
Shivom Sharma Switzerland	17	313 0.9×	291 1.0×	109 0.6×	148 0.8×	71 0.6×	38	935
Zhihong Yuan China	17	480 1.4×	209 0.7×	70 0.4×	137 0.7×	73 0.7×	62	1.0k

Countries citing papers authored by Eric Bradford

Since Specialization

Citations

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

Fields of papers citing papers by Eric Bradford

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Eric Bradford

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Zhang, Dachuan, Zhanyun Wang, Christopher Oberschelp, Eric Bradford, & Stefanie Hellweg. (2024). Enhanced Deep-Learning Model for Carbon Footprints of Chemicals. ACS Sustainable Chemistry & Engineering. 12(7). 2700–2708. 17 indexed citations

Rio‐Chanona, Ehecatl Antonio del, Panagiotis Petsagkourakis, Eric Bradford, José Eduardo Alves Graciano, & Benoît Chachuat. (2021). Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation. UCL Discovery (University College London). 48 indexed citations

Kovalev, Mikhail, et al.. (2021). Pushing nanomaterials up to the kilogram scale – An accelerated approach for synthesizing antimicrobial ZnO with high shear reactors, machine learning and high-throughput analysis. Chemical Engineering Journal. 426. 131345–131345. 22 indexed citations

Bradford, Eric, Lars Imsland, Dongda Zhang, & Ehecatl Antonio del Rio‐Chanona. (2020). Stochastic data-driven model predictive control using gaussian processes. Computers & Chemical Engineering. 139. 106844–106844. 84 indexed citations

Bradford, Eric, et al.. (2020). Gaussian processes modifier adaptation with uncertain inputs for distributed learning and optimization of wind farms. IFAC-PapersOnLine. 53(2). 12626–12631. 2 indexed citations

Bradford, Eric, et al.. (2020). Distributed learning for wind farm optimization with Gaussian processes. 4058–4064. 4 indexed citations

Bradford, Eric, et al.. (2019). Economic stochastic nonlinear model predictive control of a semi-batch polymerization reaction. IFAC-PapersOnLine. 52(1). 667–672. 9 indexed citations

Bradford, Eric & Lars Imsland. (2019). Output feedback stochastic nonlinear model predictive control for batch processes. Computers & Chemical Engineering. 126. 434–450. 15 indexed citations

Rio‐Chanona, Ehecatl Antonio del, José Eduardo Alves Graciano, Eric Bradford, & Benoît Chachuat. (2019). Modifier-Adaptation Schemes Employing Gaussian Processes and Trust Regions for Real-Time Optimization. IFAC-PapersOnLine. 52(1). 52–57. 23 indexed citations

10.

Bradford, Eric, Lars Imsland, & Ehecatl Antonio del Rio‐Chanona. (2019). Nonlinear model predictive control with explicit back-offs for Gaussian process state space models. 4747–4754. 11 indexed citations

11.

Bradford, Eric, Marcus Reble, & Lars Imsland. (2019). Output feedback stochastic nonlinear model predictive control of a polymerization batch process. Zenodo (CERN European Organization for Nuclear Research). 3144–3151. 6 indexed citations

12.

Bradford, Eric & Lars Imsland. (2018). Stochastic Nonlinear Model Predictive Control Using Gaussian Processes. Zenodo (CERN European Organization for Nuclear Research). 1 indexed citations

13.

Bradford, Eric, Artur M. Schweidtmann, Dongda Zhang, Keju Jing, & Ehecatl Antonio del Rio‐Chanona. (2018). Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. Computers & Chemical Engineering. 118. 143–158. 60 indexed citations

14.

Rio‐Chanona, Ehecatl Antonio del, et al.. (2018). Review of advanced physical and data‐driven models for dynamic bioprocess simulation: Case study of algae–bacteria consortium wastewater treatment. Biotechnology and Bioengineering. 116(2). 342–353. 45 indexed citations

15.

Bradford, Eric, Artur M. Schweidtmann, & Alexei A. Lapkin. (2018). Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. Journal of Global Optimization. 71(2). 439–440. 9 indexed citations

16.

Schweidtmann, Artur M., Adam D. Clayton, Nicholas Holmes, et al.. (2018). Machine learning meets continuous flow chemistry: Automated optimization towards the Pareto front of multiple objectives. Chemical Engineering Journal. 352. 277–282. 255 indexed citations breakdown →

17.

Bradford, Eric, Artur M. Schweidtmann, & Alexei A. Lapkin. (2018). Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. Journal of Global Optimization. 71(2). 407–438. 183 indexed citations

18.

Bradford, Eric & Lars Imsland. (2018). Stochastic Nonlinear Model Predictive Control Using Gaussian Processes. 1027–1034. 25 indexed citations

19.

Bradford, Eric & Lars Imsland. (2018). Economic Stochastic Model Predictive Control Using the Unscented Kalman Filter. IFAC-PapersOnLine. 51(18). 417–422. 27 indexed citations

20.

Bradford, Eric, et al.. (2017). Mixing Performance Evaluation for Commercially Available Micromixers Using Villermaux–Dushman Reaction Scheme with the Interaction by Exchange with the Mean Model. Organic Process Research & Development. 21(6). 816–820. 66 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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