Ankush Chakrabarty

Ankush Chakrabarty
Ankush's Quick Links

  • Biography

    Ankush works at the confluence of machine learning and automatic control. At Purdue, his doctoral research focused on developing scalable, data-driven methods for simplifying computationally intensive operations encountered in controlling and observing complex, nonlinear systems. Prior to joining MERL, Ankush was a postdoctoral Fellow at Harvard where he designed embedded model predictive controllers and deep learning-assisted control strategies for treating people with type 1 diabetes.

  • 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.
    •  

    See All News & Events for Ankush

  • Internships with Ankush

    • MS0109: Internship - Time-Series Forecasting for Energy Systems

      MERL seeks graduate students passionate about deep learning and energy systems to contribute to the development of deep time-series forecasting models for real building energy data. The work will involve multi-domain research including deep learning model development, time-series analysis, and possibly integration with energy management systems. The methods will be implemented and evaluated using real-world datasets. The results of the internship are expected to be published in top-tier machine learning and energy systems conferences and/or journals.

      Exact start date is flexible (most likely Summer 2025), with an expected duration of 3-6 months, depending on agreed scope and intermediate progress.

      Required Specific Experience:

      • Current or past enrollment in a PhD program in Electrical Engineering, Computer Science, or a related field with a focus on Machine Learning or Energy Systems.
      • 2+ years of research experience in at least some of the following areas: deep learning, time-series analysis, probabilistic machine learning, energy systems modeling.
      • PyTorch fluency.
      • Familiarity with real-world data wrangling.
      • Experience with time-series data visualization and analysis tools.

      Strong Pluses:

      • Familiarity with transformer-based time-series forecasting methodologies e.g. TFT or time-series foundation models.
      • Familiarity with adaptation mechanisms e.g. fine-tuning, meta-learning.

    • MS0110: Internship - Stochastic MPC for Grid-Interactive Buildings and HVAC

      MERL is looking for a highly motivated and qualified candidate to work on stochastic control for grid-interactive net-zero energy buildings informed by deep generative models. The ideal candidate will have a strong understanding of optimization-based control with expertise demonstrated via, e.g., publications, in stochastic model predictive control.

      Additional understanding of energy systems and machine learning is a plus. Hands-on programming experience with numerical optimization solvers and Python fluency is required. The results of this 3-6 month internship are expected to be published in top-tier energy systems and/or control venues.

    See All Internships at MERL

  • MERL Publications

    •  Chakrabarty, A., Deshpande, V.M., Wichern, G., Berntorp, K., "Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications", IEEE Annual Conference on Decision and Control (CDC), December 2024.
      BibTeX TR2024-180 PDF
      • @inproceedings{Chakrabarty2024dec,
      • author = {Chakrabarty, Ankush and Deshpande, Vedang M. and Wichern, Gordon and Berntorp, Karl}},
      • title = {Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications},
      • booktitle = {IEEE Annual Conference on Decision and Control (CDC)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-180}
      • }
    •  Tang, W.-T., Chakrabarty, A., Paulson, J.A., "TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions", Advances in Neural Information Processing Systems (NeurIPS), December 2024.
      BibTeX TR2024-167 PDF
      • @inproceedings{Tang2024dec,
      • author = {Tang, Wei-Ting and Chakrabarty, Ankush and Paulson, Joel A.}},
      • title = {TR-BEACON: Shedding Light on Efficient Behavior Discovery in High-Dimensional Spaces with Bayesian Novelty Search over Trust Regions},
      • booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
      • year = 2024,
      • month = dec,
      • url = {https://www.merl.com/publications/TR2024-167}
      • }
    •  Vanfretti, L., Laughman, C.R., Chakrabarty, A., "Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation", American Modelica Conference, October 2024.
      BibTeX TR2024-140 PDF
      • @inproceedings{Vanfretti2024oct,
      • author = {Vanfretti, Luigi and Laughman, Christopher R. and Chakrabarty, Ankush}},
      • title = {Integrating Generative Machine Learning Models and Physics-Based Models for Building Energy Simulation},
      • booktitle = {American Modelica Conference},
      • year = 2024,
      • month = oct,
      • url = {https://www.merl.com/publications/TR2024-140}
      • }
    •  Yan, J., Chakrabarty, A., Rupenyan, A., Lygeros, J., "MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models", International Conference on Automation Science and Engineering (CASE), DOI: 10.1109/​CASE59546.2024.10711717, August 2024.
      BibTeX TR2024-115 PDF
      • @inproceedings{Yan2024aug,
      • author = {Yan, Jiaqi and Chakrabarty, Ankush and Rupenyan, Alisa and Lygeros, John}},
      • title = {MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models},
      • booktitle = {International Conference on Automation Science and Engineering (CASE)},
      • year = 2024,
      • month = aug,
      • doi = {10.1109/CASE59546.2024.10711717},
      • url = {https://www.merl.com/publications/TR2024-115}
      • }
    •  Chakrabarty, A., Vanfretti, L., Bortoff, S.A., Deshpande, V.M., Wang, Y., Paulson, J.A., Zhan, S., Laughman, C.R., "Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks", IEEE Conference on Control Technology and Applications (CCTA) 2024, DOI: 10.1109/​CCTA60707.2024.10666585, August 2024.
      BibTeX TR2024-113 PDF
      • @inproceedings{Chakrabarty2024aug,
      • author = {Chakrabarty, Ankush and Vanfretti, Luigi and Bortoff, Scott A. and Deshpande, Vedang M. and Wang, Ye and Paulson, Joel A. and Zhan, Sicheng and Laughman, Christopher R.}},
      • title = {Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks},
      • booktitle = {IEEE Conference on Control Technology and Applications (CCTA) 2024},
      • year = 2024,
      • month = aug,
      • doi = {10.1109/CCTA60707.2024.10666585},
      • url = {https://www.merl.com/publications/TR2024-113}
      • }
    See All MERL Publications for Ankush

  • Other Publications

    •  Ankush Chakrabarty, Emilia Fridman, Stanisław H Zak and Gregery T Buzzard, "State and unknown input observers for nonlinear systems with delayed measurements", Automatica, Vol. 95, pp. 246-253, 2018.
      BibTeX
      • @Article{aachakrabarty2018state,
      • author = {Chakrabarty, Ankush and Fridman, Emilia and Zak, Stanislaw H and Buzzard, Gregery T},
      • title = {State and unknown input observers for nonlinear systems with delayed measurements},
      • journal = {Automatica},
      • year = 2018,
      • volume = 95,
      • pages = {246--253},
      • publisher = {Pergamon}
      • }
    •  Olivia Choudhury, Ankush Chakrabarty and Scott J. Emrich, "HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning", Nature Scientific Reports, Vol. 8, No. 1, pp. 9936, 2018.
      BibTeX
      • @Article{aachoudhury2018hecil,
      • author = {Choudhury, Olivia and Chakrabarty, Ankush and Emrich, Scott J.},
      • title = {HECIL: A Hybrid Error Correction Algorithm for Long Reads with Iterative Learning},
      • journal = {Nature Scientific Reports},
      • year = 2018,
      • volume = 8,
      • number = 1,
      • pages = 9936,
      • publisher = {Springer}
      • }
    •  John H Abel, Ankush Chakrabarty and Francis J Doyle, "Controlling Biological Time: Nonlinear Model Predictive Control for Populations of Circadian Oscillators" in Emerging Applications of Control and Systems Theory, pp. 123-138, Springer, Cham, 2018.
      BibTeX
      • @Incollection{abel2018controlling,
      • author = {Abel, John H and Chakrabarty, Ankush and Doyle, Francis J},
      • title = {Controlling Biological Time: Nonlinear Model Predictive Control for Populations of Circadian Oscillators},
      • booktitle = {Emerging Applications of Control and Systems Theory},
      • year = 2018,
      • pages = {123--138},
      • publisher = {Springer, Cham}
      • }
    •  Ankush Chakrabarty, Francis J. Doyle III and Eyal Dassau, "Deep Learning Assisted Macronutrient Estimation For Feedforward-Feedback Control In Artificial Pancreas Systems", American Control Conference (ACC), 2018, pp. 3564-3570.
      BibTeX
      • @Inproceedings{chakrabarty2018deep,
      • author = {Chakrabarty, Ankush and Doyle III, Francis J. and Dassau, Eyal},
      • title = {Deep Learning Assisted Macronutrient Estimation For Feedforward-Feedback Control In Artificial Pancreas Systems},
      • booktitle = {American Control Conference (ACC)},
      • year = 2018,
      • pages = {3564--3570}
      • }
    •  Ankush Chakrabarty, Stamatina Zavitsanou, Francis J Doyle and Eyal Dassau, "Event-triggered model predictive control for embedded artificial pancreas systems", IEEE Transactions on Biomedical Engineering, Vol. 65, No. 3, pp. 575-586, 2018.
      BibTeX
      • @Article{chakrabarty2018event,
      • author = {Chakrabarty, Ankush and Zavitsanou, Stamatina and Doyle, Francis J and Dassau, Eyal},
      • title = {Event-triggered model predictive control for embedded artificial pancreas systems},
      • journal = {IEEE Transactions on Biomedical Engineering},
      • year = 2018,
      • volume = 65,
      • number = 3,
      • pages = {575--586},
      • publisher = {IEEE}
      • }
    •  Scott C Johnson, Ankush Chakrabarty, Jianghai Hu, Stanisław H Zak and Raymond A DeCarlo, "Dual-mode robust fault estimation for switched linear systems with state jumps", Nonlinear Analysis: Hybrid Systems, Vol. 27, pp. 125-140, 2018.
      BibTeX
      • @Article{johnson2018dual,
      • author = {Johnson, Scott C and Chakrabarty, Ankush and Hu, Jianghai and Zak, Stanislaw H and DeCarlo, Raymond A},
      • title = {Dual-mode robust fault estimation for switched linear systems with state jumps},
      • journal = {Nonlinear Analysis: Hybrid Systems},
      • year = 2018,
      • volume = 27,
      • pages = {125--140},
      • publisher = {Elsevier}
      • }
    •  John H Abel, Ankush Chakrabarty and Francis J Doyle III, "Nonlinear model predictive control for circadian entrainment using small-molecule pharmaceuticals", IFAC-PapersOnLine, Vol. 50, No. 1, pp. 9864-9870, 2017.
      BibTeX
      • @Article{abel2017nonlinear,
      • author = {Abel, John H and Chakrabarty, Ankush and Doyle III, Francis J},
      • title = {Nonlinear model predictive control for circadian entrainment using small-molecule pharmaceuticals},
      • journal = {IFAC-PapersOnLine},
      • year = 2017,
      • volume = 50,
      • number = 1,
      • pages = {9864--9870},
      • publisher = {Elsevier}
      • }
    •  Ankush Chakrabarty, Raid Ayoub, Stanisław H Zak and Shreyas Sundaram, "Delayed unknown input observers for discrete-time linear systems with guaranteed performance", Systems & Control Letters, Vol. 103, pp. 9-15, 2017.
      BibTeX
      • @Article{chakrabarty2017delayed,
      • author = {Chakrabarty, Ankush and Ayoub, Raid and Zak, Stanislaw H and Sundaram, Shreyas},
      • title = {Delayed unknown input observers for discrete-time linear systems with guaranteed performance},
      • journal = {Systems \& Control Letters},
      • year = 2017,
      • volume = 103,
      • pages = {9--15},
      • publisher = {North-Holland}
      • }
    •  Ankush Chakrabarty, Stamatina Zavitsanou, Francis J Doyle and Eyal Dassau, "Model predictive control with event-triggered communication for an embedded artificial pancreas", Control Technology and Applications (CCTA), 2017 IEEE Conference on, 2017, pp. 536-541.
      BibTeX
      • @Inproceedings{chakrabarty2017model,
      • author = {Chakrabarty, Ankush and Zavitsanou, Stamatina and Doyle, Francis J and Dassau, Eyal},
      • title = {Model predictive control with event-triggered communication for an embedded artificial pancreas},
      • booktitle = {Control Technology and Applications (CCTA), 2017 IEEE Conference on},
      • year = 2017,
      • pages = {536--541},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Gregery T Buzzard and Stanisław H Zak, "Output-tracking quantized explicit nonlinear model predictive control using multiclass support vector machines", IEEE Transactions on Industrial Electronics, Vol. 64, No. 5, pp. 4130-4138, 2017.
      BibTeX
      • @Article{chakrabarty2017output,
      • author = {Chakrabarty, Ankush and Buzzard, Gregery T and Zak, Stanislaw H},
      • title = {Output-tracking quantized explicit nonlinear model predictive control using multiclass support vector machines},
      • journal = {IEEE Transactions on Industrial Electronics},
      • year = 2017,
      • volume = 64,
      • number = 5,
      • pages = {4130--4138},
      • publisher = {IEEE}
      • }
    •  Ankush Chakrabarty, Stamatina Zavitsanou, Francis J Doyle and Eyal Dassau, "Reducing controller updates via event-triggered model predictive control in an embedded artificial pancreas", American Control Conference (ACC), 2017, 2017, pp. 134-139.
      BibTeX
      • @Inproceedings{chakrabarty2017reducing,
      • author = {Chakrabarty, Ankush and Zavitsanou, Stamatina and Doyle, Francis J and Dassau, Eyal},
      • title = {Reducing controller updates via event-triggered model predictive control in an embedded artificial pancreas},
      • booktitle = {American Control Conference (ACC), 2017},
      • year = 2017,
      • pages = {134--139},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Martin J Corless, Gregery T Buzzard, Stanisław H Zak and Ann E Rundell, "State and unknown input observers for nonlinear systems with bounded exogenous inputs", IEEE Transactions on Automatic Control, Vol. 62, No. 11, pp. 5497-5510, 2017.
      BibTeX
      • @Article{chakrabarty2017state,
      • author = {Chakrabarty, Ankush and Corless, Martin J and Buzzard, Gregery T and Zak, Stanislaw H and Rundell, Ann E},
      • title = {State and unknown input observers for nonlinear systems with bounded exogenous inputs},
      • journal = {IEEE Transactions on Automatic Control},
      • year = 2017,
      • volume = 62,
      • number = 11,
      • pages = {5497--5510},
      • publisher = {IEEE}
      • }
    •  Ankush Chakrabarty, Vu Dinh, Martin Corless, Ann E Rundell, Stanislaw H Zak and Gregery T Buzzard, "Support Vector Machine Informed Explicit Nonlinear Model Predictive Control Using Low-Discrepancy Sequences", IEEE Transactions on Automatic Control, Vol. 62, No. 1, pp. 135-148, 2017.
      BibTeX
      • @Article{chakrabarty2017support,
      • author = {Chakrabarty, Ankush and Dinh, Vu and Corless, Martin and Rundell, Ann E and Zak, Stanislaw H and Buzzard, Gregery T},
      • title = {Support Vector Machine Informed Explicit Nonlinear Model Predictive Control Using Low-Discrepancy Sequences},
      • journal = {IEEE Transactions on Automatic Control},
      • year = 2017,
      • volume = 62,
      • number = 1,
      • pages = {135--148},
      • publisher = {IEEE}
      • }
    •  Olivia Choudhury, Ankush Chakrabarty and Scott J Emrich, "Highly Accurate and Efficient Data-Driven Methods For Genotype Imputation", IEEE/ACM transactions on computational biology and bioinformatics, 2017.
      BibTeX
      • @Article{choudhury2017highly,
      • author = {Choudhury, Olivia and Chakrabarty, Ankush and Emrich, Scott J},
      • title = {Highly Accurate and Efficient Data-Driven Methods For Genotype Imputation},
      • journal = {IEEE/ACM transactions on computational biology and bioinformatics},
      • year = 2017,
      • publisher = {IEEE}
      • }
    •  Eyal Dassau, Eric Renard, Jérôme Place, Anne Farret, Marie-José Pelletier, Justin Lee, Lauren M Huyett, Ankush Chakrabarty, Francis J Doyle III and Howard C Zisser, "Intraperitoneal insulin delivery provides superior glycaemic regulation to subcutaneous insulin delivery in model predictive control-based fully-automated artificial pancreas in patients with type 1 diabetes: a pilot study", Diabetes, Obesity and Metabolism, Vol. 19, No. 12, pp. 1698-1705, 2017.
      BibTeX
      • @Article{dassau2017intraperitoneal,
      • author = {Dassau, Eyal and Renard, Eric and Place, Jerome and Farret, Anne and Pelletier, Marie-Jose and Lee, Justin and Huyett, Lauren M and Chakrabarty, Ankush and Doyle III, Francis J and Zisser, Howard C},
      • title = {Intraperitoneal insulin delivery provides superior glycaemic regulation to subcutaneous insulin delivery in model predictive control-based fully-automated artificial pancreas in patients with type 1 diabetes: a pilot study},
      • journal = {Diabetes, Obesity and Metabolism},
      • year = 2017,
      • volume = 19,
      • number = 12,
      • pages = {1698--1705},
      • publisher = {Blackwell Publishing Ltd Oxford, UK}
      • }
    •  Arnab Raha, Ankush Chakrabarty, Vijay Raghunathan and Gregery T Buzzard, "Ultrafast embedded explicit model predictive control for nonlinear systems", American Control Conference (ACC), 2017, 2017, pp. 4398-4403.
      BibTeX
      • @Inproceedings{raha2017ultrafast,
      • author = {Raha, Arnab and Chakrabarty, Ankush and Raghunathan, Vijay and Buzzard, Gregery T},
      • title = {Ultrafast embedded explicit model predictive control for nonlinear systems},
      • booktitle = {American Control Conference (ACC), 2017},
      • year = 2017,
      • pages = {4398--4403},
      • organization = {IEEE}
      • }
    •  Stanisław H Zak, Ankush Chakrabarty and Gregery T Buzzard, "Robust state and unknown input estimation for nonlinear systems characterized by incremental multiplier matrices", American Control Conference (ACC), 2017, 2017, pp. 3270-3275.
      BibTeX
      • @Inproceedings{zak2017robust,
      • author = {Zak, Stanislaw H and Chakrabarty, Ankush and Buzzard, Gregery T},
      • title = {Robust state and unknown input estimation for nonlinear systems characterized by incremental multiplier matrices},
      • booktitle = {American Control Conference (ACC), 2017},
      • year = 2017,
      • pages = {3270--3275},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Shreyas Sundaram, Martin J. Corless, Gregery T. Buzzard, Stanislaw H. Zak and Ann E. Rundell, "Distributed unknown input observers for interconnected nonlinear systems", 2016 American Control Conference (ACC), 2016, pp. 2478-2483.
      BibTeX
      • @Inproceedings{chakrabarty2016distributed,
      • author = {Chakrabarty, Ankush and Sundaram, Shreyas and Corless, Martin J. and Buzzard, Gregery T. and Zak, Stanislaw H. and Rundell, Ann E.},
      • title = {Distributed unknown input observers for interconnected nonlinear systems},
      • booktitle = {2016 American Control Conference (ACC)},
      • year = 2016,
      • pages = {2478--2483},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Stanisław H Zak and Shreyas Sundaram, "State and unknown input observers for discrete-time nonlinear systems", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 7111-7116.
      BibTeX
      • @Inproceedings{chakrabarty2016state,
      • author = {Chakrabarty, Ankush and Zak, Stanislaw H and Sundaram, Shreyas},
      • title = {State and unknown input observers for discrete-time nonlinear systems},
      • booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
      • year = 2016,
      • pages = {7111--7116},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Martin J Corless, Gregery T Buzzard, Stanisław H Zak and Ann E Rundell, "Sufficient Conditions For Exogenous Input Estimation In Nonlinear Systems", 2016 American Control Conference (ACC), 2016, pp. 103-108.
      BibTeX
      • @Inproceedings{chakrabarty2016sufficient,
      • author = {Chakrabarty, Ankush and Corless, Martin J and Buzzard, Gregery T and Zak, Stanislaw H and Rundell, Ann E},
      • title = {Sufficient Conditions For Exogenous Input Estimation In Nonlinear Systems},
      • booktitle = {2016 American Control Conference (ACC)},
      • year = 2016,
      • pages = {103--108}
      • }
    •  Ankush Chakrabarty, Gregery T Buzzard, Emilia Fridman and Stanisław H Zak, "Unknown input estimation via observers for nonlinear systems with measurement delays", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 2308-2313.
      BibTeX
      • @Inproceedings{chakrabarty2016unknown,
      • author = {Chakrabarty, Ankush and Buzzard, Gregery T and Fridman, Emilia and Zak, Stanislaw H},
      • title = {Unknown input estimation via observers for nonlinear systems with measurement delays},
      • booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
      • year = 2016,
      • pages = {2308--2313},
      • organization = {IEEE}
      • }
    •  Olivia Choudhury, Ankush Chakrabarty and Scott J Emrich, "HAPI-Gen: Highly Accurate Phasing and Imputation of Genotype Data", Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics, 2016, pp. 78-87.
      BibTeX
      • @Inproceedings{choudhury2016hapi,
      • author = {Choudhury, Olivia and Chakrabarty, Ankush and Emrich, Scott J},
      • title = {HAPI-Gen: Highly Accurate Phasing and Imputation of Genotype Data},
      • booktitle = {Proceedings of the 7th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics},
      • year = 2016,
      • pages = {78--87},
      • organization = {ACM}
      • }
    •  Xiaohang Li, Fanglai Zhu, Ankush Chakrabarty and Stanislaw H Zak, "Non-Fragile Fault-Tolerant Fuzzy Observer-Based Controller Design for Nonlinear Systems", IEEE Transactions on Fuzzy Systems, Vol. 24, No. 6, pp. 1679-1689, 2016.
      BibTeX
      • @Article{li2016non,
      • author = {Li, Xiaohang and Zhu, Fanglai and Chakrabarty, Ankush and Zak, Stanislaw H},
      • title = {Non-Fragile Fault-Tolerant Fuzzy Observer-Based Controller Design for Nonlinear Systems},
      • journal = {IEEE Transactions on Fuzzy Systems},
      • year = 2016,
      • volume = 24,
      • number = 6,
      • pages = {1679--1689},
      • publisher = {IEEE}
      • }
    •  Stamatina Zavitsanou, Ankush Chakrabarty, Eyal Dassau and Francis J. Doyle III, "Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas", Processes, Vol. 4, No. 4, pp. 35, 2016.
      BibTeX
      • @Article{zavitsanou2016embedded,
      • author = {Zavitsanou, Stamatina and Chakrabarty, Ankush and Dassau, Eyal and Doyle III, Francis J.},
      • title = {Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas},
      • journal = {Processes},
      • year = 2016,
      • volume = 4,
      • number = 4,
      • pages = 35,
      • publisher = {MDPI}
      • }
    •  Haotian Zhang, Ankush Chakrabarty, Raid Ayoub, Gregery T Buzzard and Shreyas Sundaram, "Sampling-based explicit nonlinear model predictive control for output tracking", Decision and Control (CDC), 2016 IEEE 55th Conference on, 2016, pp. 4722-4727.
      BibTeX
      • @Inproceedings{zhang2016sampling,
      • author = {Zhang, Haotian and Chakrabarty, Ankush and Ayoub, Raid and Buzzard, Gregery T and Sundaram, Shreyas},
      • title = {Sampling-based explicit nonlinear model predictive control for output tracking},
      • booktitle = {Decision and Control (CDC), 2016 IEEE 55th Conference on},
      • year = 2016,
      • pages = {4722--4727},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Gregery T Buzzard, Martin J Corless, Stanisław H Zak and Ann E Rundell, "Correcting hypothalamic-pituitary-adrenal axis dysfunction using observer-based explicit nonlinear model predictive control", Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE, 2014, pp. 3426-3429.
      BibTeX
      • @Inproceedings{chakrabarty2014correcting,
      • author = {Chakrabarty, Ankush and Buzzard, Gregery T and Corless, Martin J and Zak, Stanislaw H and Rundell, Ann E},
      • title = {Correcting hypothalamic-pituitary-adrenal axis dysfunction using observer-based explicit nonlinear model predictive control},
      • booktitle = {Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE},
      • year = 2014,
      • pages = {3426--3429},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Vu C Dinh, Gregery T Buzzard, Stanislaw H Zak and Ann E Rundell, "Robust explicit nonlinear model predictive control with integral sliding mode.", ACC, 2014, pp. 2851-2856.
      BibTeX
      • @Inproceedings{chakrabarty2014robust,
      • author = {Chakrabarty, Ankush and Dinh, Vu C and Buzzard, Gregery T and Zak, Stanislaw H and Rundell, Ann E},
      • title = {Robust explicit nonlinear model predictive control with integral sliding mode.},
      • booktitle = {ACC},
      • year = 2014,
      • pages = {2851--2856}
      • }
    •  Ankush Chakrabarty, Suvadeep Banerjee, Sayan Maity and Amitava Chatterjee, "Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms", Measurement, Vol. 46, No. 4, pp. 1616-1629, 2013.
      BibTeX
      • @Article{chakrabarty2013fuzzy,
      • author = {Chakrabarty, Ankush and Banerjee, Suvadeep and Maity, Sayan and Chatterjee, Amitava},
      • title = {Fuzzy model predictive control of non-linear processes using convolution models and foraging algorithms},
      • journal = {Measurement},
      • year = 2013,
      • volume = 46,
      • number = 4,
      • pages = {1616--1629},
      • publisher = {Elsevier}
      • }
    •  Ankush Chakrabarty, Gregery T Buzzard and Ann E Rundell, "Model-based design of experiments for cellular processes", Wiley Interdisciplinary Reviews: Systems Biology and Medicine, Vol. 5, No. 2, pp. 181-203, 2013.
      BibTeX
      • @Article{chakrabarty2013model,
      • author = {Chakrabarty, Ankush and Buzzard, Gregery T and Rundell, Ann E},
      • title = {Model-based design of experiments for cellular processes},
      • journal = {Wiley Interdisciplinary Reviews: Systems Biology and Medicine},
      • year = 2013,
      • volume = 5,
      • number = 2,
      • pages = {181--203},
      • publisher = {John Wiley \& Sons, Inc. Hoboken, USA}
      • }
    •  Ankush Chakrabarty, Serena M Pearce, Robert P Nelson and Ann E Rundell, "Treating acute myeloid leukemia via HSC transplantation: A preliminary study of multi-objective personalization strategies", American Control Conference (ACC), 2013, 2013, pp. 3790-3795.
      BibTeX
      • @Inproceedings{chakrabarty2013treating,
      • author = {Chakrabarty, Ankush and Pearce, Serena M and Nelson, Robert P and Rundell, Ann E},
      • title = {Treating acute myeloid leukemia via HSC transplantation: A preliminary study of multi-objective personalization strategies},
      • booktitle = {American Control Conference (ACC), 2013},
      • year = 2013,
      • pages = {3790--3795},
      • organization = {IEEE}
      • }
    •  Ankush Chakrabarty, Harsh Jain and Amitava Chatterjee, "Volterra kernel based face recognition using artificial bee colonyoptimization", Engineering Applications of Artificial Intelligence, Vol. 26, No. 3, pp. 1107-1114, 2013.
      BibTeX
      • @Article{chakrabarty2013volterra,
      • author = {Chakrabarty, Ankush and Jain, Harsh and Chatterjee, Amitava},
      • title = {Volterra kernel based face recognition using artificial bee colonyoptimization},
      • journal = {Engineering Applications of Artificial Intelligence},
      • year = 2013,
      • volume = 26,
      • number = 3,
      • pages = {1107--1114},
      • publisher = {Pergamon}
      • }
    •  Namrata Tomar, Olivia Choudhury, Ankush Chakrabarty and Rajat K De, "An integrated pathway system modeling of Saccharomyces cerevisiae HOG pathway: a Petri net based approach", Molecular biology reports, Vol. 40, No. 2, pp. 1103-1125, 2013.
      BibTeX
      • @Article{tomar2013integrated,
      • author = {Tomar, Namrata and Choudhury, Olivia and Chakrabarty, Ankush and De, Rajat K},
      • title = {An integrated pathway system modeling of Saccharomyces cerevisiae HOG pathway: a Petri net based approach},
      • journal = {Molecular biology reports},
      • year = 2013,
      • volume = 40,
      • number = 2,
      • pages = {1103--1125},
      • publisher = {Springer Netherlands}
      • }
    •  Ankush Chakrabarty, Olivia Choudhury, Pallab Sarkar, Avishek Paul and Debarghya Sarkar, "Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting", Artificial Intelligence Research, Vol. 1, No. 1, pp. 63, 2012.
      BibTeX
      • @Article{chakrabarty2012hyperspectral,
      • author = {Chakrabarty, Ankush and Choudhury, Olivia and Sarkar, Pallab and Paul, Avishek and Sarkar, Debarghya},
      • title = {Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting},
      • journal = {Artificial Intelligence Research},
      • year = 2012,
      • volume = 1,
      • number = 1,
      • pages = 63
      • }
    •  Suvadeep Banerjee, Ankush Chakrabarty, Sayan Maity and Amitava Chatterjee, "Feedback linearizing indirect adaptive fuzzy control with foraging based on-line plant model estimation", Applied Soft Computing, Vol. 11, No. 4, pp. 3441-3450, 2011.
      BibTeX
      • @Article{banerjee2011feedback,
      • author = {Banerjee, Suvadeep and Chakrabarty, Ankush and Maity, Sayan and Chatterjee, Amitava},
      • title = {Feedback linearizing indirect adaptive fuzzy control with foraging based on-line plant model estimation},
      • journal = {Applied Soft Computing},
      • year = 2011,
      • volume = 11,
      • number = 4,
      • pages = {3441--3450},
      • publisher = {Elsevier}
      • }

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  • MERL Issued Patents

    • Title: "System and Method for Polytopic Policy Optimization for Robust Feedback Control During Learning"
      Inventors: Jha, Devesh; Chakrabarty, Ankush
      Patent No.: 12,124,230
      Issue Date: Oct 22, 2024
    • Title: "Controller for Optimizing Motion Trajectory to Control Motion of One or More Devices"
      Inventors: Di Cairano, Stefano; Chakrabarty, Ankush; Quirynen, Rien; Srinivasan, Mohit; Yoshikawa, Nobuyuki; Mariyama, Toshisada
      Patent No.: 12,061,474
      Issue Date: Aug 13, 2024
    • Title: "System and Method for Detecting and Correcting Laser-Cutting Distortion"
      Inventors: Vetterling, William; Thornton, Jay E.; Chakrabarty, Ankush; Goldsmith, Abraham M.
      Patent No.: 12,059,751
      Issue Date: Aug 13, 2024
    • Title: "Method and System for Detecting Anomalous Sound"
      Inventors: Wichern, Gordon P; Chakrabarty, Ankush; Wang, Zhongqiu; Le Roux, Jonathan
      Patent No.: 11,978,476
      Issue Date: May 7, 2024
    • Title: "Extremum Seeking Control System and a Method for Controlling a System"
      Inventors: Danielson, Claus; Chakrabarty, Ankush
      Patent No.: 11,899,409
      Issue Date: Feb 13, 2024
    • Title: "Apparatus and Method for Control with Data-Driven Model Adaptation"
      Inventors: Benosman, Mouhacine; Chakrabarty, Ankush; Nabi, Saleh
      Patent No.: 11,840,224
      Issue Date: Dec 12, 2023
    • Title: "Controlling Thermal State of Conditioned Environment Based on Multivariable Optimization"
      Inventors: Nabi, Saleh; Chakrabarty, Ankush; Benosman, Mouhacine; VijayShankar, Sanjana
      Patent No.: 11,808,472
      Issue Date: Nov 7, 2023
    • Title: "System and Method for Adaptive Control of Vehicle Dynamics"
      Inventors: Berntorp, Karl; Chakrabarty, Ankush; Di Cairano, Stefano
      Patent No.: 11,679,759
      Issue Date: Jun 20, 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: "Control of Autonomous Vehicles Adaptive to User Driving Preferences"
      Inventors: Kalabic, Uros; Chakrabarty, Ankush; Di Cairano, Stefano
      Patent No.: 11,548,520
      Issue Date: Jan 10, 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 Feasibly Positioning Servomotors with Unmodeled Dynamics"
      Inventors: Chakrabarty, Ankush; Danielson, Claus; Wang, Yebin
      Patent No.: 11,392,104
      Issue Date: Jul 19, 2022
    • Title: "System and Method for Data-Driven Control of Constrained System"
      Inventors: Chakrabarty, Ankush; Quirynen, Rien; Danielson, Claus; Gao, Weinan
      Patent No.: 11,106,189
      Issue Date: Aug 31, 2021
    • Title: "Network Adapted Control System"
      Inventors: Guo, Jianlin; Ma, Yehan; Wang, Yebin; Chakrabarty, Ankush; Ahn, Heejin; Orlik, Philip V.
      Patent No.: 10,969,767
      Issue Date: Apr 6, 2021
    • Title: "System and Method for Control Constrained Operation of Machine with Partially Unmodeled Dynamics Using Lipschitz Constant"
      Inventors: Chakrabarty, Ankush; Jha, Devesh; Wang, Yebin
      Patent No.: 10,895,854
      Issue Date: Jan 19, 2021
    See All Patents for MERL