Ankush Chakrabarty
- Phone: 617-621-7597
- Email:
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Position:
Research / Technical Staff
Principal Research Scientist -
Education:
Ph.D., Purdue University, 2016 -
Research Areas:
External Links:
Ankush's Quick Links
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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.
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Recent News & Events
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NEWS MERL researchers present 7 papers at CDC 2024 Date: December 16, 2024 - December 19, 2024
Where: Milan, Italy
MERL Contacts: Ankush Chakrabarty; Vedang M. Deshpande; Stefano Di Cairano; James Queeney; Abraham P. Vinod; Avishai Weiss; Gordon Wichern
Research Areas: Artificial Intelligence, Control, Dynamical Systems, Machine Learning, Multi-Physical Modeling, Optimization, RoboticsBrief- MERL researchers presented 7 papers at the recently concluded Conference on Decision and Control (CDC) 2024 in Milan, Italy. The papers covered a wide range of topics including safety shielding for stochastic model predictive control, reinforcement learning using expert observations, physics-constrained meta learning for positioning, variational-Bayes Kalman filtering, Bayesian measurement masks for GNSS positioning, divert-feasible lunar landing, and centering and stochastic control using constrained zonotopes.
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, Ankush Chakrabarty (Principal Research Scientist, Multiphysical Systems Team) was an invited speaker in the pre-conference Workshop on "Learning Dynamics From Data" where he gave a talk on few-shot meta-learning for black-box identification using data from similar systems.
- MERL researchers presented 7 papers at the recently concluded Conference on Decision and Control (CDC) 2024 in Milan, Italy. The papers covered a wide range of topics including safety shielding for stochastic model predictive control, reinforcement learning using expert observations, physics-constrained meta learning for positioning, variational-Bayes Kalman filtering, Bayesian measurement masks for GNSS positioning, divert-feasible lunar landing, and centering and stochastic control using constrained zonotopes.
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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 SecurityBrief- 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).
- MERL researchers will attend and present the following papers at the 2024 Advances in Neural Processing Systems (NeurIPS) Conference and Workshops.
See All News & Events for Ankush -
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Internships with Ankush
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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.
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MERL Publications
- , "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}
- }
- , "Physics-Constrained Meta-Learning for Online Adaptation and Estimation in Positioning Applications", IEEE 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 Conference on Decision and Control (CDC)},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2024-180}
- }
- , "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 Christopher R. and Azizan, Navid and Laughman, 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}
- }
- , "Preference-based Multi-Objective Bayesian Optimization with Gradients", NeurIPS Workshop on Bayesian Decision-making and Uncertainty, December 2024.BibTeX TR2025-011 PDF
- @inproceedings{Ip2024dec,
- author = {Ip, Joshua Hang Sai and Chakrabarty, Ankush and Masui Hideyuki and Mesbah, Ali and Romeres, Diego}},
- title = {Preference-based Multi-Objective Bayesian Optimization with Gradients},
- booktitle = {NeurIPS Workshop on Bayesian Decision-making and Uncertainty},
- year = 2024,
- month = dec,
- url = {https://www.merl.com/publications/TR2025-011}
- }
- , "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}
- }
- , "Meta-Learning for Physically-Constrained Neural System Identification", arXiv, January 2025.
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Other Publications
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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}
- }
- , "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
- }
- , "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}
- }
- , "State and unknown input observers for nonlinear systems with delayed measurements", Automatica, Vol. 95, pp. 246-253, 2018.
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Software & Data Downloads
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Videos
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MERL Issued Patents
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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
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Title: "System and Method for Polytopic Policy Optimization for Robust Feedback Control During Learning"
