Christopher R. Laughman

Christopher R. Laughman
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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.

  • Recent News & Events

    •  NEWS    MERL Researchers to Present 2 Conference and 11 Workshop Papers at NeurIPS 2024
      Date: December 10, 2024 - December 15, 2024
      Where: Advances in Neural Processing Systems (NeurIPS)
      MERL Contacts: Petros T. Boufounos; Matthew Brand; Ankush Chakrabarty; Anoop Cherian; François Germain; Toshiaki Koike-Akino; Christopher R. Laughman; Jonathan Le Roux; Jing Liu; Suhas Lohit; Tim K. Marks; Yoshiki Masuyama; Kieran Parsons; Kuan-Chuan Peng; Diego Romeres; Pu (Perry) Wang; Ye Wang; Gordon Wichern
      Research Areas: Artificial Intelligence, Communications, Computational Sensing, Computer Vision, Control, Data Analytics, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics, Signal Processing, Speech & Audio, Human-Computer Interaction, Information Security
      Brief
      • MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.

        1. "RETR: Multi-View Radar Detection Transformer for Indoor Perception" by Ryoma Yataka (Mitsubishi Electric), Adriano Cardace (Bologna University), Perry Wang (Mitsubishi Electric Research Laboratories), Petros Boufounos (Mitsubishi Electric Research Laboratories), Ryuhei Takahashi (Mitsubishi Electric). Main Conference. https://neurips.cc/virtual/2024/poster/95530

        2. "Evaluating Large Vision-and-Language Models on Children's Mathematical Olympiads" by Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Joanna Matthiesen (Math Kangaroo USA), Kevin Smith (Massachusetts Institute of Technology), Josh Tenenbaum (Massachusetts Institute of Technology). Main Conference, Datasets and Benchmarks track. https://neurips.cc/virtual/2024/poster/97639

        3. "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?" by Young-Jin Park (Massachusetts Institute of Technology), Jing Liu (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Gordon Wichern (Mitsubishi Electric Research Laboratories), Navid Azizan (Massachusetts Institute of Technology), Christopher R. Laughman (Mitsubishi Electric Research Laboratories), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories). Time Series in the Age of Large Models Workshop.

        4. "Forget to Flourish: Leveraging Model-Unlearning on Pretrained Language Models for Privacy Leakage" by Md Rafi Ur Rashid (Penn State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Shagufta Mehnaz (Penn State University), Ye Wang (Mitsubishi Electric Research Laboratories). Workshop on Red Teaming GenAI: What Can We Learn from Adversaries?

        5. "Spatially-Aware Losses for Enhanced Neural Acoustic Fields" by Christopher Ick (New York University), Gordon Wichern (Mitsubishi Electric Research Laboratories), Yoshiki Masuyama (Mitsubishi Electric Research Laboratories), François G Germain (Mitsubishi Electric Research Laboratories), Jonathan Le Roux (Mitsubishi Electric Research Laboratories). Audio Imagination Workshop.

        6. "FV-NeRV: Neural Compression for Free Viewpoint Videos" by Sorachi Kato (Osaka University), Takuya Fujihashi (Osaka University), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Takashi Watanabe (Osaka University). Machine Learning and Compression Workshop.

        7. "GPT Sonography: Hand Gesture Decoding from Forearm Ultrasound Images via VLM" by Keshav Bimbraw (Worcester Polytechnic Institute), Ye Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). AIM-FM: Advancements In Medical Foundation Models: Explainability, Robustness, Security, and Beyond Workshop.

        8. "Smoothed Embeddings for Robust Language Models" by Hase Ryo (Mitsubishi Electric), Md Rafi Ur Rashid (Penn State University), Ashley Lewis (Ohio State University), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kieran Parsons (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories). Safe Generative AI Workshop.

        9. "Slaying the HyDRA: Parameter-Efficient Hyper Networks with Low-Displacement Rank Adaptation" by Xiangyu Chen (University of Kansas), Ye Wang (Mitsubishi Electric Research Laboratories), Matthew Brand (Mitsubishi Electric Research Laboratories), Pu Wang (Mitsubishi Electric Research Laboratories), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories). Workshop on Adaptive Foundation Models.

        10. "Preference-based Multi-Objective Bayesian Optimization with Gradients" by Joshua Hang Sai Ip (University of California Berkeley), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Ali Mesbah (University of California Berkeley), Diego Romeres (Mitsubishi Electric Research Laboratories). Workshop on Bayesian Decision-Making and Uncertainty. Lightning talk spotlight.

        11. "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensions with Trust-Region-based Bayesian Novelty Search" by Wei-Ting Tang (Ohio State University), Ankush Chakrabarty (Mitsubishi Electric Research Laboratories), Joel A. Paulson (Ohio State University). Workshop on Bayesian Decision-Making and Uncertainty.

        12. "MEL-PETs Joint-Context Attack for the NeurIPS 2024 LLM Privacy Challenge Red Team Track" by Ye Wang (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Jing Liu (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Special Award for Practical Attack.

        13. "MEL-PETs Defense for the NeurIPS 2024 LLM Privacy Challenge Blue Team Track" by Jing Liu (Mitsubishi Electric Research Laboratories), Ye Wang (Mitsubishi Electric Research Laboratories), Toshiaki Koike-Akino (Mitsubishi Electric Research Laboratories), Tsunato Nakai (Mitsubishi Electric), Kento Oonishi (Mitsubishi Electric), Takuya Higashi (Mitsubishi Electric). LLM Privacy Challenge. Won 3rd Place Award.

        MERL members also contributed to the organization of the Multimodal Algorithmic Reasoning (MAR) Workshop (https://marworkshop.github.io/neurips24/). Organizers: Anoop Cherian (Mitsubishi Electric Research Laboratories), Kuan-Chuan Peng (Mitsubishi Electric Research Laboratories), Suhas Lohit (Mitsubishi Electric Research Laboratories), Honglu Zhou (Salesforce Research), Kevin Smith (Massachusetts Institute of Technology), Tim K. Marks (Mitsubishi Electric Research Laboratories), Juan Carlos Niebles (Salesforce AI Research), Petar Veličković (Google DeepMind).
    •  
    •  NEWS    MERL researchers present 9 papers at ACC 2024
      Date: July 10, 2024 - July 12, 2024
      Where: Toronto, Canada
      MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; Christopher R. Laughman; Arvind Raghunathan; Abraham P. Vinod; Yebin Wang; Avishai Weiss
      Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, Robotics
      Brief
      • MERL researchers presented 9 papers at the recently concluded American Control Conference (ACC) 2024 in Toronto, Canada. The papers covered a wide range of topics including data-driven spatial monitoring using heterogenous robots, aircraft approach management near airports, computation fluid dynamics-based motion planning for drones facing winds, trajectory planning for coordinated monitoring using a team of drones and a ground carrier vehicle, ensemble Kalman smoothing-based model predictive control for motion planning for autonomous vehicles, system identification for Lithium-ion batteries, physics-constrained deep Kalman filters for vapor compression systems, switched reference governors for constrained systems, and distributed road-map monitoring using onboard sensors.

        As a sponsor of the conference, MERL maintained a booth for open discussions with researchers and students, and hosted a special session to discuss highlights of MERL research and work philosophy.

        In addition, Abraham Vinod served as a panelist at the Student Networking Event at the conference. The student networking event provides an opportunity for all interested students to network with professionals working in industry, academia, and national laboratories during a structured event, and encourages their continued participation as the future leaders in the field.
    •  

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  • Internships with Chris

    • MS0098: Internship - Control and Estimation for Large-Scale Thermofluid Systems

      MERL is seeking a motivated graduate student to research methods for state and parameter estimation and optimization of large-scale systems for process applications. 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.

    See All Internships at MERL

  • MERL Publications

    •  Park, Y.-J., Germain, F.G., Liu, J., Wang, Y., Koike-Akino, T., Wichern, G., Azizan, N., Laughman, C.R., Chakrabarty, A., "Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models", arXiv, May 2025.
      BibTeX arXiv
      • @article{Park2025may,
      • author = {Park, Young-Jin and Germain, François G and Liu, Jing and Wang, Ye and Koike-Akino, Toshiaki and Wichern, Gordon and Azizan, Navid and Laughman, Christopher R. and Chakrabarty, Ankush},
      • title = {{Probabilistic Forecasting for Building Energy Systems using Time-Series Foundation Models}},
      • journal = {arXiv},
      • year = 2025,
      • month = may,
      • url = {https://arxiv.org/abs/2506.00630}
      • }
    •  Chakrabarty, A., Vanfretti, L., Wang, Y., Mineyuki, T., Zhan, S., Tang, W.-T., Paulson, J.A., Deshpande, V.M., Bortoff, S.A., Laughman, C.R., "Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation", Building Simulation, April 2025.
      BibTeX TR2025-043 PDF
      • @article{Chakrabarty2025apr,
      • author = {Chakrabarty, Ankush and Vanfretti, Luigi and Wang, Ye and Mineyuki, Takuma and Zhan, Sicheng and Tang, Wei-Ting and Paulson, Joel A. and Deshpande, Vedang M. and Bortoff, Scott A. and Laughman, Christopher R.},
      • title = {{Time-Series Generative Networks for Synthesizing Realistic Scenarios in Occupant-Centric Building Simulation}},
      • journal = {Building Simulation},
      • year = 2025,
      • month = apr,
      • url = {https://www.merl.com/publications/TR2025-043}
      • }
    •  Chakrabarty, A., Wichern, G., Deshpande, V.M., Vinod, A.P., Berntorp, K., Laughman, C.R., "Meta-Learning for Physically-Constrained Neural System Identification", arXiv, January 2025.
      BibTeX arXiv
      • @article{Chakrabarty2025jan,
      • author = {Chakrabarty, Ankush and Wichern, Gordon and Deshpande, Vedang M. and Vinod, Abraham P. and Berntorp, Karl and Laughman, Christopher R.},
      • title = {{Meta-Learning for Physically-Constrained Neural System Identification}},
      • journal = {arXiv},
      • year = 2025,
      • month = jan,
      • url = {https://arxiv.org/abs/2501.06167v1}
      • }
    •  Park, Y.-J., Germain, F.G., Liu, J., Wang, Y., Koike-Akino, T., Wichern, G., Laughman, C.R., Azizan, N., Chakrabarty, A., "Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
      BibTeX TR2025-001 PDF
      • @inproceedings{Park2024dec,
      • author = {{{Park, Young-Jin and Germain, François G and Liu, Jing and Wang, Ye and Koike-Akino, Toshiaki and Wichern, Gordon and Laughman, Christopher R. and Azizan, Navid and Chakrabarty, Ankush}}},
      • title = {{{Probabilistic Forecasting for Building Energy Systems: Are Time-Series Foundation Models The Answer?}}},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2025-001}
      • }
    •  Bortoff, S.A., Laughman, C.R., Deshpande, V.M., Qiao, H., "Fluid Property Functions in Polar and Parabolic Coordinates", American Modelica Conference, October 2024.
      BibTeX TR2024-144 PDF
      • @inproceedings{Bortoff2024oct,
      • author = {Bortoff, Scott A. and Laughman, Christopher R. and Deshpande, Vedang M. and Qiao, Hongtao},
      • title = {{Fluid Property Functions in Polar and Parabolic Coordinates}},
      • booktitle = {American Modelica Conference},
      • year = 2024,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2024-144}
      • }
    See All MERL Publications for Chris

  • Videos

  • MERL Issued Patents

    • Title: "System and Method for Calculation of Thermofluid Properties using Saturation Curve-Aligned Coordinates"
      Inventors: Laughman, Christopher; Bortoff, Scott A.; Qiao, Hongtao
      Patent No.: 11,739,996
      Issue Date: Aug 29, 2023
    • Title: "Method and System for Circuiting in Heat Exchangers"
      Inventors: Raghunathan, Arvind U; Laughman, Christopher
      Patent No.: 11,704,447
      Issue Date: Jul 18, 2023
    • Title: "Controlling Vapor Compression System Using Probabilistic Surrogate Model"
      Inventors: Chakrabarty, Ankush; Laughman, Christopher; Bortoff, Scott A.
      Patent No.: 11,573,023
      Issue Date: Feb 7, 2023
    • Title: "Extremum Seeking Control with Stochastic Gradient Estimation"
      Inventors: Chakrabarty, Ankush; Danielson, Claus; Bortoff, Scott A.; Laughman, Christopher
      Patent No.: 11,467,544
      Issue Date: Oct 11, 2022
    • Title: "System and Method for Power Optimizing Control of Multi-Zone Heat Pumps"
      Inventors: Bortoff, Scott A.; Burns, Daniel 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, Daniel J; Bortoff, Scott A.
      Patent No.: 10,830,515
      Issue Date: Nov 10, 2020
    • Title: "System and Method for Thermal Comfort Control"
      Inventors: Bortoff, Scott A.; Laughman, Christopher
      Patent No.: 10,767,887
      Issue Date: Sep 8, 2020
    • Title: "System and Method for Controlling Vapor Compression Systems"
      Inventors: Burns, Daniel 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, Daniel 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, Daniel 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; 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; Laughman, Christopher
      Patent No.: 9,595,134
      Issue Date: Mar 14, 2017
    • Title: "System and Method for Controlling Vapor Compression Systems"
      Inventors: Burns, Dan; 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, Daniel 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
    See All Patents for MERL