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

• Asynchronous parallel surrogate optimization aided neural network design with variable evaluation runtime for streamflow and pollutant forecast

  Journal of Hydrology · 2025-10-08

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• Learning active subspaces and discovering important features with Gaussian radial basis functions neural networks

  Neural Networks · 2024-04-29 · 11 citations

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• Enhanced watershed model evaluation incorporating hydrologic signatures and consistency within efficient surrogate multi-objective optimization

  Environmental Modelling & Software · 2024-02-14 · 6 citations

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• Solving higher dimensional expensive black box global optimization problems using sparse directional search on surrogates

  Mathematical Programming Computation · 2024-10-16 · 1 citations

  articleOpen access

  Abstract We present a new algorithm Directional Optimization Search with Surrogate (DOSS), for optimizing problems with box constraints and a computationally expensive black-box objective function. DOSS is a radial basis function (RBF) based method that mainly focuses on higher dimensional and computationally expensive objective functions that can be multimodal. DOSS introduces three new techniques not previously used in earlier RBF algorithms, including using a combination of the coordinates knowledge level, fewer initial sampling points, and the surrogate’s gradient information. Numerical results on a test set including 14 test problems with 36, 48, and 60 dimensions show that DOSS outperforms two recently published algorithms RBFOpt and TuRBO, and earlier RBF algorithms such as DYCORS. TuRBO is a Gaussian process based optimization algorithm, which outperformed earlier state-of-the-art methods. DOSS algorithm also has a good performance on real-world optimization problems, including robot pushing and rover trajectory planning problems. Almost sure convergence for the DOSS algorithm is also proven in this paper. An implementation of DOSS is available at https://doi.org/10.5281/zenodo.13731558 .

  PublisherOA PDFDOI

• Calibrating Tropical Forest Coexistence in Ecosystem Demography Models Using Multi‐Objective Optimization Through Population‐Based Parallel Surrogate Search

  Journal of Advances in Modeling Earth Systems · 2024-08-01 · 2 citations

  articleOpen accessSenior author

  Abstract Tropical forest diversity governs forest structures, compositions, and influences the ecosystem response to environmental changes. Better representation of forest diversity in ecosystem demography (ED) models within Earth system models is thus necessary to accurately capture and predict how tropical forests affect Earth system dynamics subject to climate changes. However, achieving forest coexistence in ED models is challenging due to their computational expense and limited understanding of the mechanisms governing forest functional diversity. This study applies the advanced Multi‐Objective Population‐based Parallel Local Surrogate‐assisted search (MOPLS) optimization algorithm to simultaneously calibrate ecosystem fluxes and coexistence of two physiologically distinct tropical forest species in a size‐ and age‐structured ED model with realistic representation of wood harvest. MOPLS exhibits satisfactory model performance, capturing hydrological and biogeochemical dynamics observed in Barro Colorado Island, Panama, and robustly achieving coexistence for the two representative forest species. This demonstrates its effectiveness in calibrating tropical forest coexistence. The optimal solution is applied to investigate the recovery trajectories of forest biomass after various intensities of clear‐cut deforestation. We find that a 20% selective logging can take approximately 40 years for aboveground biomass to return to the initial level. This is due to the slow recovery rate of late successional trees, which only increases by 4% over the 40‐year period. This study lays the foundation to calibrate coexistence in ED models. MOPLS can be an effective tool to help better represent tropical forest diversity in Earth system models and inform forest management practices.

  PublisherOA PDFDOI

• Quick large-scale spatiotemporal flood inundation computation using integrated Encoder-Decoder LSTM with time distributed spatial output models

  Journal of Hydrology · 2024-03-15 · 24 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

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• Contextual Optimizer through Neighborhood Estimation for prescriptive analysis

  arXiv (Cornell University) · 2023-08-20

  preprintOpen accessSenior author

  We address the challenges posed by heteroscedastic noise in contextual decision-making. We propose a consistent Shrinking Neighborhood Estimation (SNE) technique that successfully estimates contextual performance under unpredictable variances. Furthermore, we propose a Rate-Efficient Sampling rule designed to enhance the performance of the SNE. The effectiveness of the combined solution ``Contextual Optimizer through Neighborhood Estimation"(CONE) is validated through theorems and numerical benchmarking. The methodologies have been further deployed to address a staffing challenge in a hospital call center, exemplifying their substantial impact and practical utility in real-world scenarios.

  PublisherOA PDFDOI

• Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural Networks

  arXiv (Cornell University) · 2023-07-11 · 2 citations

  preprintOpen accessSenior author

  Providing a model that achieves a strong predictive performance and is simultaneously interpretable by humans is one of the most difficult challenges in machine learning research due to the conflicting nature of these two objectives. To address this challenge, we propose a modification of the radial basis function neural network model by equipping its Gaussian kernel with a learnable precision matrix. We show that precious information is contained in the spectrum of the precision matrix that can be extracted once the training of the model is completed. In particular, the eigenvectors explain the directions of maximum sensitivity of the model revealing the active subspace and suggesting potential applications for supervised dimensionality reduction. At the same time, the eigenvectors highlight the relationship in terms of absolute variation between the input and the latent variables, thereby allowing us to extract a ranking of the input variables based on their importance to the prediction task enhancing the model interpretability. We conducted numerical experiments for regression, classification, and feature selection tasks, comparing our model against popular machine learning models, the state-of-the-art deep learning-based embedding feature selection techniques, and a transformer model for tabular data. Our results demonstrate that the proposed model does not only yield an attractive prediction performance compared to the competitors but also provides meaningful and interpretable results that potentially could assist the decision-making process in real-world applications. A PyTorch implementation of the model is available on GitHub at the following link. https://github.com/dannyzx/Gaussian-RBFNN

  PublisherOA PDFDOI

• Efficient multi-objective optimization through parallel surrogate-assisted local search with tabu mechanism and asynchronous option

  Engineering Optimization · 2023-06-21 · 5 citations

  articleOpen accessSenior author

  Multi-objective optimization is challenging for computationally expensive objectives because most optimization methods rely on a large number of function evaluations to approach the Pareto-optimal front. To this end, this article develops a novel multi-objective optimization algorithm, the Multi-Objective Parallel Local Surrogate-assisted search (MOPLS) algorithm, which combines surrogate approximation and parallel computing. In each iteration, MOPLS incorporates a tabu mechanism to determine new points for expensive evaluations via a series of independent surrogate-assisted local searches. A master–worker architecture in MOPLS allows the algorithm to conduct either synchronous or asynchronous parallel processing. On a number of benchmark problems, MOPLS outperforms recent surrogate-assisted algorithms within a limited computational budget. Empirical results from parallel experiments indicate that MOPLS can significantly improve its efficiency through parallelism, and the asynchronous MOPLS shows advantages in handling objectives with non-constant evaluation times. Finally, the better performance of MOPLS over alternatives in calibrating a complex watershed simulation model demonstrates its competitiveness in solving real-world engineering applications.

  PublisherOA PDFDOI

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

• Investigating Contaminant Transport in Large Watersheds with New Methods for Automatic Calibration, Sensitivity and Uncertainty Analysis Including Application to Design of Sensor

  NSF · $300k · 2008–2012

• ALGORITHMS: Multi-Algorithm Parallel Optimization of Costly Functions

  NSF · $449k · 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…