Christopher R. Laughman
- Phone: 617-621-7545
- Email:
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Position:
Research / Technical Staff
Senior Principal Research Scientist, Senior Team Leader -
Education:
Ph.D., Massachusetts Institute of Technology, 2008 -
Research Areas:
- Multi-Physical Modeling
- Data Analytics
- Control
- Optimization
- Dynamical Systems
- Machine Learning
- Human-Computer Interaction
External Links:
Chris' Quick Links
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Biography
Christopher's interests lie in the intersection of the modeling of physical systems and the experimental construction and testing of these systems, including simulation, numerical methods, and fault detection. He has worked on a variety of multi-physical systems, such as thermo-fluid systems and electromechanical energy conversion systems.
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Recent News & Events
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TALK Universal Differential Equations for Scientific Machine Learning Date & Time: Thursday, May 7, 2020; 12:00 PM
Speaker: Christopher Rackauckas, MIT
MERL Host: Christopher R. Laughman
Research Areas: Machine Learning, Multi-Physical Modeling, OptimizationBrief- In the context of science, the well-known adage "a picture is worth a thousand words" might well be "a model is worth a thousand datasets." Scientific models, such as Newtonian physics or biological gene regulatory networks, are human-driven simplifications of complex phenomena that serve as surrogates for the countless experiments that validated the models. Recently, machine learning has been able to overcome the inaccuracies of approximate modeling by directly learning the entire set of nonlinear interactions from data. However, without any predetermined structure from the scientific basis behind the problem, machine learning approaches are flexible but data-expensive, requiring large databases of homogeneous labeled training data. A central challenge is reco nciling data that is at odds with simplified models without requiring "big data". In this talk we discuss a new methodology, universal differential equations (UDEs), which augment scientific models with machine-learnable structures for scientifically-based learning. We show how UDEs can be utilized to discover previously unknown governing equations, accurately extrapolate beyond the original data, and accelerate model simulation, all in a time and data-efficient manner. This advance is coupled with open-source software that allows for training UDEs which incorporate physical constraints, delayed interactions, implicitly-defined events, and intrinsic stochasticity in the model. Our examples show how a diverse set of computationally-difficult modeling issues across scientific disciplines, from automatically discovering biological mechanisms to accelerating climate simulations by 15,000x, can be handled by training UDEs.
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NEWS MERL researchers presented more than 8 papers in European Control Conference, ECC 2019 Date: June 25, 2019 - June 28, 2019
Where: Naples, Italy
MERL Contacts: Karl Berntorp; Scott A. Bortoff; Ankush Chakrabarty; Stefano Di Cairano; Devesh K. Jha; Christopher R. Laughman; Daniel N. Nikovski; Rien Quirynen; Diego Romeres; William S. Yerazunis
Research Areas: Control, Machine Learning, OptimizationBrief- The European Control Conference is the premier control conference in Europe. This year MERL was well represented with papers on control for HVAC, machine learning for estimation and control, robot assembly, and optimization methods for control.
See All News & Events for Chris -
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Internships with Chris
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MS1717: Estimation and Optimization for Large-Scale Systems
MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems. Representative applications include large vapor-compression cycles and other multiphysical systems for energy conversion that couple thermodynamic, fluid, and electrical domains. The ideal candidate would have a solid background in control and estimation, numerical methods, and optimization; strong programming skills and experience with Julia/Python/Matlab are also expected. Knowledge of the fundamental physics of thermofluid flows (e.g., thermodynamics, heat transfer, and fluid mechanics), nonlinear dynamics, or equation-oriented languages (Modelica, gPROMS) is a plus. The expected duration of this internship is 3 months.
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MERL Publications
- , "Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes", Advances in Neural Information Processing Systems (NeurIPS), December 2021.BibTeX TR2021-149 PDF
- @inproceedings{Zhan2021dec,
- author = {Zhan, Sicheng and Wichern, Gordon and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes},
- booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
- year = 2021,
- month = dec,
- url = {https://www.merl.com/publications/TR2021-149}
- }
- , "Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters", IEEE International Conference on Systems, Man, and Cybernetics (SMC), DOI: 10.1109/SMC52423.2021.9658893, October 2021.BibTeX TR2021-128 PDF Video
- @inproceedings{Chakrabarty2021oct2,
- author = {Chakrabarty, Ankush and Bortoff, Scott A. and Laughman, Christopher R.},
- title = {Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters},
- booktitle = {IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
- year = 2021,
- month = oct,
- doi = {10.1109/SMC52423.2021.9658893},
- url = {https://www.merl.com/publications/TR2021-128}
- }
- , "VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints", arXiv, October 2021.BibTeX arXiv
- @article{Xu2021oct,
- author = {Xu, Wenjie and Jones, Colin and Svetozarevic, Bratislav and Laughman, Christopher R. and Chakrabarty, Ankush},
- title = {VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints},
- journal = {arXiv},
- year = 2021,
- month = oct,
- url = {https://arxiv.org/abs/2110.07479}
- }
- , "Composing Modeling and Simulation with Machine Learning in Julia", 14th International Modelica Conference, September 2021.BibTeX TR2021-114 PDF
- @inproceedings{Rackauckas2021sep,
- author = {Rackauckas, Chris and Anantharaman, Ranjan and Edelman, Alan and Gowda, Shashi and Gwozdz, Maja and Jain, Anand and Laughman, Christopher R. and Ma, Yingbo and Martinuzzi, Francesco and Pal, Avik and Rajput, Utkarsh and Saba, Elliot and Shah, Viral},
- title = {Composing Modeling and Simulation with Machine Learning in Julia},
- booktitle = {14th International Modelica Conference},
- year = 2021,
- month = sep,
- url = {https://www.merl.com/publications/TR2021-114}
- }
- , "Data-driven calibration of physics-informed models of joint building/equipment dynamics using Bayesian optimization", 2021 Building Simulation Conference, September 2021.BibTeX TR2021-105 PDF Video
- @inproceedings{Chakrabarty2021sep,
- author = {Chakrabarty, Ankush and Maddalena, Emilio and Qiao, Hongtao and Laughman, Christopher R.},
- title = {Data-driven calibration of physics-informed models of joint building/equipment dynamics using Bayesian optimization},
- booktitle = {2021 Building Simulation Conference},
- year = 2021,
- month = sep,
- url = {https://www.merl.com/publications/TR2021-105}
- }
- , "Meta-Learned Bayesian Optimization for Building Model Calibration using Attentive Neural Processes", Advances in Neural Information Processing Systems (NeurIPS), December 2021.
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Software Downloads
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Videos
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MERL Issued Patents
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Title: "System and Method for Power Optimizing Control of Multi-Zone Heat Pumps"
Inventors: Bortoff, Scott A.; Burns, Dan J; Laughman, Christopher; Qiao, Hongtao
Patent No.: 10,895,412
Issue Date: Jan 19, 2021 -
Title: "System and Method for Controlling Refrigerant in Vapor Compression System"
Inventors: Laughman, Christopher; Qiao, Hongtao; Burns, Dan J; Bortoff, Scott A.
Patent No.: 10,830,515
Issue Date: Nov 10, 2020 -
Title: "System and Method for Thermal Comfort Control"
Inventors: Laughman, Christopher; Bortoff, Scott A.
Patent No.: 10,767,887
Issue Date: Sep 8, 2020 -
Title: "System and Method for Controlling Vapor Compression Systems"
Inventors: Burns, Dan J; Laughman, Christopher; Bortoff, Scott A.
Patent No.: 10,495,364
Issue Date: Dec 3, 2019 -
Title: "Coordinated Operation of Multiple Space-Conditioning Systems"
Inventors: Laughman, Christopher; Qiao, Hongtao; Burns, Dan J; Bortoff, Scott A.
Patent No.: 10,234,158
Issue Date: Mar 19, 2019 -
Title: "System and Method for Controlling Multi-Zone Vapor Compression Systems"
Inventors: Burns, Dan J; Di Cairano, Stefano; Bortoff, Scott A.; Laughman, Christopher
Patent No.: 10,174,957
Issue Date: Jan 8, 2019 -
Title: "System and Method for Controlling of Vapor Compression System"
Inventors: Burns, Dan J; Jain, Neera; Laughman, Christopher; Di Cairano, Stefano; Bortoff, Scott A.
Patent No.: 9,625,196
Issue Date: Apr 18, 2017 -
Title: "Method For Reconstructing 3D Scenes From 2D Images"
Inventors: Ramalingam, Srikumar; Taguchi, Yuichi; Pillai, Jaishanker K; Burns, Dan J; Laughman, Christopher
Patent No.: 9,595,134
Issue Date: Mar 14, 2017 -
Title: "System and Method for Controlling Vapor Compression Systems"
Inventors: Burns, Dan J; Laughman, Christopher; Bortoff, Scott A.
Patent No.: 9,534,820
Issue Date: Jan 3, 2017 -
Title: "System and Method for Controlling Temperature and Humidity in Multiple Spaces using Liquid Desiccant"
Inventors: Laughman, Christopher; Burns, Dan J; Bortoff, Scott A.; Waters, Richard C.
Patent No.: 9,518,765
Issue Date: Dec 13, 2016 -
Title: "Adaptive Control of Vapor Compression System"
Inventors: Burns, Dan J; Laughman, Christopher
Patent No.: 9,182,154
Issue Date: Nov 10, 2015 -
Title: "Controlling Operation of Vapor Compression System"
Inventors: Nikovski, Daniel N.; Laughman, Christopher; Burns, Dan J
Patent No.: 8,793,003
Issue Date: Jul 29, 2014 -
Title: "System and Method for Controlling Operations of Vapor Compression"
Inventors: Bortoff, Scott A.; Burns, Dan J; Laughman, Christopher
Patent No.: 8,694,131
Issue Date: Apr 8, 2014
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Title: "System and Method for Power Optimizing Control of Multi-Zone Heat Pumps"
