Eric Bradford

Engineering · Physical Sciences

17h-index1.4kcitations42works11.52yr avg
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Research Topics

Advanced Control Systems Optimization(28), Fault Detection and Control Systems(18), Advanced Multi-Objective Optimization Algorithms(11), Control Systems and Identification(11), Process Optimization and Integration(10)

Publications42 total

Crop disease detection with machine learning
Journal of Student Research·2025Open Access
Enhanced Deep-Learning Model for Carbon Footprints of Chemicals
ACS Sustainable Chemistry & Engineering·2024· 23 citedOpen Access
Poor data and outdated methods sabotage the decarbonization efforts of the chemical industry
2023· 16 citedOpen Access
Chance constrained policy optimization for process control and optimization
Journal of Process Control·2022· 11 citedOpen Access
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·2021· 28 citedOpen Access
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation
UCL Discovery (University College London)·2021· 69 cited
Combining Gaussian processes and polynomial chaos expansions for stochastic nonlinear model predictive control
arXiv (Cornell University)·2021· 5 citedOpen Access
Real-time optimization meets Bayesian optimization and derivative-free optimization: A tale of modifier adaptation
Computers & Chemical Engineering·2021· 25 citedOpen Access
Hybrid Gaussian Process Modeling Applied to Economic Stochastic Model Predictive Control of Batch Processes
Lecture notes in control and information sciences·2021· 8 citedOpen Access
Modifier Adaptation Meets Bayesian Optimization and Derivative-Free Optimization.
arXiv (Cornell University)·2020· 5 citedOpen Access
Pushing Nanomaterials past the Kilogram Scale—a Targeted Approach Integrating Scalable Microreactors, Machine Learning and High-Throughput Analysis
2020Open Access
Distributed learning for wind farm optimization with Gaussian processes
2020· 4 cited
Stochastic data-driven model predictive control using gaussian processes
Computers & Chemical Engineering·2020· 127 citedOpen Access
Stochastic nonlinear model predictive control for chemical batch processes
Duo Research Archive (University of Oslo)·2020Open Access
Gaussian processes modifier adaptation with uncertain inputs for distributed learning and optimization of wind farms
IFAC-PapersOnLine·2020· 3 citedOpen Access
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty
IFAC-PapersOnLine·2020· 3 citedOpen Access
An accelerated approach for efficient development and scaling of new material technologies, combining flow synthesis with machine learning. Case study: Nanostructured ZnO for antibacterial coatings
2020
Nonlinear model predictive control with explicit back-offs for Gaussian process state space models
Zenodo (CERN European Organization for Nuclear Research)·2019Open Access
Nonlinear model predictive control with explicit back-offs for Gaussian process state space models
2019· 20 cited
Reinforcement learning for batch bioprocess optimization
Computers & Chemical Engineering·2019· 6 citedOpen Access
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Frequent Co-authors

Lars Imsland(19), Ehecatl Antonio del Rio‐Chanona(14), Artur M. Schweidtmann(9), Alexei A. Lapkin(8), Dongda Zhang(7), Panagiotis Petsagkourakis(7), José Eduardo Alves Graciano(4), Benoît Chachuat(4), Ilya Orson Sandoval(4), Nicholas A. Jose(3), Mikhail Kovalev(3), Hua Chun Zeng(3), Marcus Reble(3), Richard A. Bourne(2), Keju Jing(2), Zhanyun Wang(2), Christopher Oberschelp(2), Stefanie Hellweg(2), Leif Erik Andersson(2), Adam D. Clayton(1)