Christine Ann Shoemaker

About

Christine Ann Shoemaker is the Joseph P. Ripley Professor of Engineering Emerita at Cornell University, elected to this position on July 1, 2002, by a committee of endowed professors in the College of Engineering. Her appointment recognizes her excellence in research and teaching. Her research focuses on developing cost-effective, robust solutions for environmental problems through optimization, modeling, and statistical analyses. She specializes in creating numerically efficient nonlinear and global optimization algorithms that leverage high-performance computing, including asynchronous parallelism, to address complex environmental systems. Her work encompasses applications such as groundwater remediation, carbon sequestration, pesticide management, ecology, and climate and watershed model calibration. Her algorithms aim to improve model forecasts, evaluate monitoring schemes, and compare environmental management practices, emphasizing efficiency in computationally expensive simulations. Shoemaker has contributed to multidisciplinary international efforts to protect groundwater resources and promote diversity in engineering and computational mathematics.

Research topics

• Computer science
• Mathematical optimization
• Environmental science
• Algorithm
• Mathematics

Selected publications

• Efficient parallel surrogate optimization algorithm and framework with application to parameter calibration of computationally expensive three-dimensional hydrodynamic lake PDE models

  Environmental Modelling & Software · 2020-10-26 · 36 citations

  articleCorresponding
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• A multi-fidelity RBF surrogate-based optimization framework for computationally expensive multi-modal problems with application to capacity planning of manufacturing systems

  Structural and Multidisciplinary Optimization · 2020-05-17 · 31 citations

  articleOpen accessSenior author

  Abstract This paper presents a multi-fidelity RBF (radial basis function) surrogate-based optimization framework (MRSO) for computationally expensive multi-modal optimization problems when multi-fidelity (high-fidelity (HF) and low-fidelity (LF)) models are available. The HF model is expensive and accurate while the LF model is cheaper to compute but less accurate. To exploit the correlation between the LF and HF models and improve algorithm efficiency, in MRSO, we first apply the DYCORS (dynami…

  PublisherOA PDFDOI

• GOPS: efficient RBF surrogate global optimization algorithm with high dimensions and many parallel processors including application to multimodal water quality PDE model calibration

  Optimization and Engineering · 2020-09-17 · 28 citations

  articleOpen accessSenior author

  Abstract This paper describes a new parallel global surrogate-based algorithm Global Optimization in Parallel with Surrogate (GOPS) for the minimization of continuous black-box objective functions that might have multiple local minima, are expensive to compute, and have no derivative information available. The task of picking P new evaluation points for P processors in each iteration is addressed by sampling around multiple center points at which the objective function has been previously evalua…

  PublisherOA PDFDOI

• Efficient, parallelized global optimization of groundwater pumping in a regional aquifer with land subsidence constraints

  Journal of Environmental Management · 2022-02-25 · 15 citations

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• Enhancing algal bloom forecasting: A novel framework for machine learning performance evaluation during periods of special temporal patterns

  Environmental Modelling & Software · 2024-07-26 · 3 citations

  articleSenior author
  PublisherDOI

Recent grants

• Environmental Sustainability Systems Analysis Including Multiple Objective and Fixed Cost Optimization of Management Decisions with Watershed and Groundwater Applications

  NSF · $302k · 2008–2012

• Integrated Statistical and Optimization Analysis for Computationally Expensive Models of Complex Environmental Systems

  NSF · $626k · 2004–2009

• Improving Calibration, Sensitivity and Uncertainty Analysis of Data Based Models of the Environment

  NSF · $350k · 2003–2007

Frequent coauthors

• Taimoor Akhtar

  University of Guelph

  31 shared

• Wei Xia

  Singapore-HUJ Alliance for Research and Enterprise

  25 shared

• Rommel G. Regis

  Saint Joseph's University

  23 shared

• Juliane Müller

  22 shared

• Jae-Heung Yoon

  21 shared

• David Ruppert

  Cornell University

  16 shared

• Bryan A. Tolson

  15 shared

• Jery R. Stedinger

  Cornell University

  14 shared

Awards & honors

• National Engineering Award, American Association of Engineer…
• National Academy of Engineering Member, 2012
• Distinguished (Honorary) Member, American Society of Civil E…

Similar researchers at Cornell University

• Robert A. Jarrowh-68
• Adrian Lewish-54