default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDAnkush Chakrabarty
Person information
SPARQL queries 
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j25]Sleiman Safaoui
, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano:
Safe Multiagent Motion Planning Under Uncertainty for Drones Using Filtered Reinforcement Learning. IEEE Trans. Robotics 40: 2529-2542 (2024)- [c43]Abraham P. Vinod, Sachiyo Yamazaki, Ankush Chakrabarty, Nobuyuki Yoshikawa, Stefano Di Cairano:
Aircraft Approach Management Using Reachability and Dynamic Programming. ACC 2024: 318-324 - [c42]Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan, John Lygeros:
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models. CASE 2024: 1910-1915 - [c41]Farshud Sorourifar, Joel A. Paulson, Ye Wang, Rien Quirynen, Christopher R. Laughman, Ankush Chakrabarty:
Bayesian Forecasting with Deep Generative Disturbance Models in Stochastic MPC for Building Energy Systems. CCTA 2024: 414-419 - [c40]Ankush Chakrabarty, Luigi Vanfretti, Wei-Ting Tang, Joel A. Paulson, Sicheng Zhan, Scott A. Bortoff, Vedang M. Deshpande, Ye Wang, Christopher R. Laughman:
Assessing Building Control Performance Using Physics-Based Simulation Models and Deep Generative Networks. CCTA 2024: 547-554 - [i17]Jiaqi Yan, Ankush Chakrabarty, Alisa Rupenyan, John Lygeros:
MPC of Uncertain Nonlinear Systems with Meta-Learning for Fast Adaptation of Neural Predictive Models. CoRR abs/2404.12097 (2024) - [i16]Wei-Ting Tang, Ankush Chakrabarty, Joel A. Paulson:
BEACON: A Bayesian Optimization Strategy for Novelty Search in Expensive Black-Box Systems. CoRR abs/2406.03616 (2024) - 2023
- [j24]Vedang M. Deshpande, Ankush Chakrabarty, Abraham P. Vinod, Christopher R. Laughman:
Physics-Constrained Deep Autoencoded Kalman Filters for Estimating Vapor Compression System States. IEEE Control. Syst. Lett. 7: 3483-3488 (2023) - [j23]Claus Danielson, Scott A. Bortoff, Ankush Chakrabarty:
Extremum Seeking Control With an Adaptive Gain Based on Gradient Estimation Error. IEEE Trans. Syst. Man Cybern. Syst. 53(1): 152-164 (2023) - [j22]Ankush Chakrabarty, Scott A. Bortoff, Christopher R. Laughman:
Simulation Failure-Robust Bayesian Optimization for Data-Driven Parameter Estimation. IEEE Trans. Syst. Man Cybern. Syst. 53(5): 2629-2640 (2023) - [c39]Joel A. Paulson, Farshud Sorourifar, Christopher R. Laughman, Ankush Chakrabarty:
LSR-BO: Local Search Region Constrained Bayesian Optimization for Performance Optimization of Vapor Compression Systems. ACC 2023: 576-582 - [c38]Truong X. Nghiem, Ján Drgona, Colin N. Jones, Zoltán Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw-Cortez, Draguna L. Vrabie:
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. ACC 2023: 3735-3750 - [c37]Raphael Chinchilla, Vedang M. Deshpande, Ankush Chakrabarty, Christopher R. Laughman:
Learning Residual Dynamics via Physics-Augmented Neural Networks: Application to Vapor Compression Cycles. ACC 2023: 4069-4076 - [c36]Alessandro Salatiello, Ye Wang, Gordon Wichern, Toshiaki Koike-Akino, Yoshihiro Ohta, Yosuke Kaneko, Christopher R. Laughman, Ankush Chakrabarty:
Synthesizing Building Operation Data with Generative Models: VAEs, GANs, or Something In Between? e-Energy (Companion) 2023: 125-133 - [i15]Wenjie Xu, Colin N. Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty:
Violation-Aware Contextual Bayesian Optimization for Controller Performance Optimization with Unmodeled Constraints. CoRR abs/2301.12099 (2023) - [i14]Truong X. Nghiem, Ján Drgona, Colin N. Jones, Zoltán Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw-Cortez, Draguna L. Vrabie:
Physics-Informed Machine Learning for Modeling and Control of Dynamical Systems. CoRR abs/2306.13867 (2023) - [i13]Sleiman Safaoui, Abraham P. Vinod, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano:
Safe multi-agent motion planning under uncertainty for drones using filtered reinforcement learning. CoRR abs/2311.00063 (2023) - 2022
- [j21]Shamma Nasrin, Ahish Shylendra, Nastaran Darabi, Theja Tulabandhula, Wilfred Gomes, Ankush Chakrabarty, Amit Ranjan Trivedi:
ENOS: Energy-Aware Network Operator Search in Deep Neural Networks. IEEE Access 10: 81447-81457 (2022) - [j20]Sanjana Vijayshankar, Ankush Chakrabarty, Piyush Grover, Saleh Nabi:
Co-design of reduced-order models and observers from thermo-fluid data. IFAC J. Syst. Control. 19: 100181 (2022) - [j19]Yehan Ma, Jianlin Guo, Yebin Wang, Ankush Chakrabarty, Heejin Ahn, Philip V. Orlik, Xinping Guan, Chenyang Lu:
Optimal Dynamic Transmission Scheduling for Wireless Networked Control Systems. IEEE Trans. Control. Syst. Technol. 30(6): 2360-2376 (2022) - [j18]Ankush Chakrabarty, Claus Danielson, Stefano Di Cairano, Arvind U. Raghunathan:
Active Learning for Estimating Reachable Sets for Systems With Unknown Dynamics. IEEE Trans. Cybern. 52(4): 2531-2542 (2022) - [c35]Wenjie Xu, Colin N. Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty:
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints. ACC 2022: 5288-5293 - [c34]Marcel Menner, Ankush Chakrabarty, Karl Berntorp, Stefano Di Cairano:
Learning Optimization-based Control Policies Directly from Digital Twin Simulations. CCTA 2022: 895-900 - [c33]Joel A. Paulson, Farshud Sorourifar, Ankush Chakrabarty:
Efficient Multi-Step Lookahead Bayesian Optimization with Local Search Constraints. CDC 2022: 123-129 - [c32]Ankush Chakrabarty:
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach. CDC 2022: 130-136 - [c31]Abraham P. Vinod, Sleiman Safaoui, Ankush Chakrabarty, Rien Quirynen, Nobuyuki Yoshikawa, Stefano Di Cairano:
Safe multi-agent motion planning via filtered reinforcement learning. ICRA 2022: 7270-7276 - [c30]Abhishek Cauligi, Ankush Chakrabarty, Stefano Di Cairano, Rien Quirynen:
PRISM: Recurrent Neural Networks and Presolve Methods for Fast Mixed-integer Optimal Control. L4DC 2022: 34-46 - [i12]Shen Wang, Ankush Chakrabarty, Ahmad F. Taha:
Data-Driven Identification of Dynamic Quality Models in Drinking Water Networks. CoRR abs/2207.05983 (2022) - [i11]Ankush Chakrabarty:
Optimizing Closed-Loop Performance with Data from Similar Systems: A Bayesian Meta-Learning Approach. CoRR abs/2211.00077 (2022) - [i10]Ankush Chakrabarty, Gordon Wichern, Christopher R. Laughman:
Meta-Learning of Neural State-Space Models Using Data From Similar Systems. CoRR abs/2211.07768 (2022) - 2021
- [j17]Ankush Chakrabarty, Mouhacine Benosman:
Safe learning-based observers for unknown nonlinear systems using Bayesian optimization. Autom. 133: 109860 (2021) - [j16]Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard, Yebin Wang, Kyriakos G. Vamvoudakis:
Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation. IEEE Trans. Neural Networks Learn. Syst. 32(1): 405-419 (2021) - [c29]Karl Berntorp, Ankush Chakrabarty, Stefano Di Cairano:
Vehicle Center-of-Gravity Height and Dynamics Estimation with Uncertainty Quantification by Marginalized Particle Filter. ACC 2021: 160-165 - [c28]Karl Berntorp, Ankush Chakrabarty, Stefano Di Cairano:
Vehicle Rollover Avoidance by Parameter-Adaptive Reference Governor. CDC 2021: 635-640 - [c27]Ankush Chakrabarty, Scott A. Bortoff, Christopher R. Laughman:
Simulation Failure Robust Bayesian Optimization for Estimating Black-Box Model Parameters. SMC 2021: 1533-1538 - [c26]Ankush Chakrabarty, Rien Quirynen, Diego Romeres, Stefano Di Cairano:
Learning Disagreement Regions with Deep Neural Networks to Reduce Practical Complexity of Mixed-Integer MPC. SMC 2021: 3238-3244 - [c25]Gordon Wichern, Ankush Chakrabarty, Zhong-Qiu Wang, Jonathan Le Roux:
Anomalous Sound Detection Using Attentive Neural Processes. WASPAA 2021: 186-190 - [i9]Shen Wang, Ahmad F. Taha, Ankush Chakrabarty, Lina Sela, Ahmed A. Abokifa:
Model Order Reduction for Water Quality Dynamics. CoRR abs/2102.10737 (2021) - [i8]Ankush Chakrabarty, Gordon Wichern, Christopher R. Laughman:
Attentive Neural Processes and Batch Bayesian Optimization for Scalable Calibration of Physics-Informed Digital Twins. CoRR abs/2106.15502 (2021) - [i7]Claus Danielson, Scott A. Bortoff, Ankush Chakrabarty:
Extremum Seeking Control with an Adaptive Gain Based On Gradient Estimation Error. CoRR abs/2107.01176 (2021) - [i6]Wenjie Xu, Colin N. Jones, Bratislav Svetozarevic, Christopher R. Laughman, Ankush Chakrabarty:
VABO: Violation-Aware Bayesian Optimization for Closed-Loop Control Performance Optimization with Unmodeled Constraints. CoRR abs/2110.07479 (2021) - 2020
- [j15]Arnab Raha, Ankush Chakrabarty, Vijay Raghunathan, Gregery T. Buzzard:
Embedding Approximate Nonlinear Model Predictive Control at Ultrahigh Speed and Extremely Low Power. IEEE Trans. Control. Syst. Technol. 28(3): 1092-1099 (2020) - [j14]Ankush Chakrabarty, Elizabeth Healey, Dawei Shi, Stamatina Zavitsanou, Francis J. Doyle, Eyal Dassau:
Embedded Model Predictive Control for a Wearable Artificial Pancreas. IEEE Trans. Control. Syst. Technol. 28(6): 2600-2607 (2020) - [c24]Ankush Chakrabarty, Karl Berntorp, Stefano Di Cairano:
Learning-based Parameter-Adaptive Reference Governors. ACC 2020: 956-961 - [c23]Sanjana Vijayshankar, Saleh Nabi, Ankush Chakrabarty, Piyush Grover, Mouhacine Benosman:
Dynamic Mode Decomposition and Robust Estimation: Case Study of a 2D Turbulent Boussinesq Flow. ACC 2020: 2351-2356 - [c22]Ankush Chakrabarty, Claus Danielson, Yebin Wang:
Data-Driven Optimal Tracking with Constrained Approximate Dynamic Programming for Servomotor Systems. CCTA 2020: 352-357 - [i5]Ankush Chakrabarty, Mouhacine Benosman:
Safe Learning-based Observers for Unknown Nonlinear Systems using Bayesian Optimization. CoRR abs/2005.05888 (2020)
2010 – 2019
- 2019
- [j13]John H. Abel, Ankush Chakrabarty, Elizabeth B. Klerman, Francis J. Doyle III:
Pharmaceutical-based entrainment of circadian phase via nonlinear model predictive control. Autom. 100: 336-348 (2019) - [j12]Olivia Choudhury, Ankush Chakrabarty, Scott J. Emrich:
Highly Accurate and Efficient Data-Driven Methods for Genotype Imputation. IEEE ACM Trans. Comput. Biol. Bioinform. 16(4): 1107-1116 (2019) - [c21]Ankush Chakrabarty, Devesh K. Jha, Yebin Wang:
Data-Driven Control Policies for Partially Known Systems via Kernelized Lipschitz Learning. ACC 2019: 4192-4197 - [c20]Ankush Chakrabarty, Ali Zemouche, Rajesh Rajamani, Mouhacine Benosman:
Robust Data-Driven Neuro-Adaptive Observers With Lipschitz Activation Functions. CDC 2019: 2862-2867 - [c19]Ankush Chakrabarty, Rien Quirynen, Claus Danielson, Weinan Gao:
Approximate Dynamic Programming For Linear Systems with State and Input Constraints. ECC 2019: 524-529 - [c18]Martin Corless, Ankush Chakrabarty:
L2Observers for a Class of Nonlinear Systems with Unknown Inputs. ECC 2019: 1142-1147 - [c17]Yehan Ma, Jianlin Guo, Yebin Wang, Ankush Chakrabarty, Heejin Ahn, Philip V. Orlik, Chenyang Lu:
Optimal dynamic scheduling of wireless networked control systems. ICCPS 2019: 77-86 - [c16]Uros Kalabic, Ankush Chakrabarty, Rien Quirynen, Stefano Di Cairano:
Learning autonomous vehicle passengers' preferred driving styles using g-g plots and haptic feedback. ITSC 2019: 4012-4017 - [c15]Yebin Wang, Ankush Chakrabarty, MengChu Zhou, Jinyun Zhang:
Near-Optimal Control of Motor Drives via Approximate Dynamic Programming. SMC 2019: 3679-3686 - [i4]Martin J. Corless, Ankush Chakrabarty:
L2 Observers for a Class of Nonlinear Systems with Unknown Inputs. CoRR abs/1902.08288 (2019) - [i3]Ankush Chakrabarty, Rien Quirynen, Claus Danielson, Weinan Gao:
Approximate Dynamic Programming For Linear Systems with State and Input Constraints. CoRR abs/1906.11369 (2019) - [i2]Ankush Chakrabarty, Devesh K. Jha, Gregery T. Buzzard, Yebin Wang, Kyriakos G. Vamvoudakis:
Safe Approximate Dynamic Programming Via Kernelized Lipschitz Estimation. CoRR abs/1907.02151 (2019) - 2018
- [j11]Ankush Chakrabarty, Emilia Fridman, Stanislaw H. Zak, Gregery T. Buzzard:
State and unknown input observers for nonlinear systems with delayed measurements. Autom. 95: 246-253 (2018) - [j10]Ankush Chakrabarty, Ann E. Rundell, Stanislaw H. Zak, Fanglai Zhu, Gregery T. Buzzard:
Unknown Input Estimation for Nonlinear Systems Using Sliding Mode Observers and Smooth Window Functions. SIAM J. Control. Optim. 56(5): 3619-3641 (2018) - [j9]Ankush Chakrabarty, Stamatina Zavitsanou, Francis J. Doyle, Eyal Dassau:
Event-Triggered Model Predictive Control for Embedded Artificial Pancreas Systems. IEEE Trans. Biomed. Eng. 65(3): 575-586 (2018) - [c14]Ankush Chakrabarty, Francis J. Doyle, Eyal Dassau:
Deep Learning Assisted Macronutrient Estimation For Feedforward-Feedback Control In Artificial Pancreas Systems. ACC 2018: 3564-3570 - [c13]Ankush Chakrabarty, Arvind U. Raghunathan, Stefano Di Cairano, Claus Danielson:
Data-Driven Estimation of Backward Reachable and Invariant Sets for Unmodeled Systems via Active Learning. CDC 2018: 372-377 - 2017
- [j8]Ankush Chakrabarty, Raid Ayoub, Stanislaw H. Zak, Shreyas Sundaram:
Delayed unknown input observers for discrete-time linear systems with guaranteed performance. Syst. Control. Lett. 103: 9-15 (2017) - [j7]Ankush Chakrabarty, Vu C. Dinh, Martin J. Corless, Ann E. Rundell, Stanislaw H. Zak, Gregery T. Buzzard:
Support Vector Machine Informed Explicit Nonlinear Model Predictive Control Using Low-Discrepancy Sequences. IEEE Trans. Autom. Control. 62(1): 135-148 (2017) - [j6]Ankush Chakrabarty, Martin J. Corless, Gregery T. Buzzard, Stanislaw H. Zak, Ann E. Rundell:
State and Unknown Input Observers for Nonlinear Systems With Bounded Exogenous Inputs. IEEE Trans. Autom. Control. 62(11): 5497-5510 (2017) - [j5]Ankush Chakrabarty, Gregery T. Buzzard, Stanislaw H. Zak:
Output-Tracking Quantized Explicit Nonlinear Model Predictive Control Using Multiclass Support Vector Machines. IEEE Trans. Ind. Electron. 64(5): 4130-4138 (2017) - [c12]Ankush Chakrabarty, Stamatina Zavitsanou, Francis J. Doyle, Eyal Dassau:
Reducing controller updates via event-triggered model predictive control in an embedded artificial pancreas. ACC 2017: 134-139 - [c11]Stanislaw H. Zak, Ankush Chakrabarty, Gregery T. Buzzard:
Robust state and unknown input estimation for nonlinear systems characterized by incremental multiplier matrices. ACC 2017: 3270-3275 - [c10]Arnab Raha, Ankush Chakrabarty, Vijay Raghunathan, Gregery T. Buzzard:
Ultrafast embedded explicit model predictive control for nonlinear systems. ACC 2017: 4398-4403 - [c9]Ankush Chakrabarty, Stamatina Zavitsanou, Francis J. Doyle, Eyal Dassau:
Model predictive control with event-triggered communication for an embedded artificial pancreas. CCTA 2017: 536-541 - 2016
- [b1]Ankush Chakrabarty:
Supervised learning-based explicit nonlinear model predictive control and unknown input estimation in biomedical systems. Purdue University, USA, 2016 - [j4]Xiaohang Li, Fanglai Zhu, Ankush Chakrabarty, Stanislaw H. Zak:
Nonfragile Fault-Tolerant Fuzzy Observer-Based Controller Design for Nonlinear Systems. IEEE Trans. Fuzzy Syst. 24(6): 1679-1689 (2016) - [c8]Ankush Chakrabarty, Shreyas Sundaram, Martin J. Corless, Gregery T. Buzzard, Stanislaw H. Zak, Ann E. Rundell:
Distributed unknown input observers for interconnected nonlinear systems. ACC 2016: 101-106 - [c7]Olivia Choudhury, Ankush Chakrabarty, Scott J. Emrich:
HAPI-Gen: Highly Accurate Phasing and Imputation of Genotype Data. BCB 2016: 78-87 - [c6]Ankush Chakrabarty, Gregery T. Buzzard, Emilia Fridman, Stanislaw H. Zak:
Unknown input estimation via observers for nonlinear systems with measurement delays. CDC 2016: 2308-2313 - [c5]Haotian Zhang, Ankush Chakrabarty, Raid Ayoub, Gregery T. Buzzard, Shreyas Sundaram:
Sampling-based explicit nonlinear model predictive control for output tracking. CDC 2016: 4722-4727 - [c4]Ankush Chakrabarty, Stanislaw H. Zak, Shreyas Sundaram:
State and unknown input observers for discrete-time nonlinear systems. CDC 2016: 7111-7116 - 2015
- [i1]Ankush Chakrabarty, Gregery T. Buzzard, Stanislaw H. Zak, Fanglai Zhu, Ann E. Rundell:
Simultaneous Unknown Input And Sensor Noise Reconstruction For Nonlinear Systems With Boundary Layer Sliding Mode Observers. CoRR abs/1507.03924 (2015) - 2014
- [c3]Ankush Chakrabarty, Vu C. Dinh, Gregery T. Buzzard, Stanislaw H. Zak, Ann E. Rundell:
Robust explicit nonlinear model predictive control with integral sliding mode. ACC 2014: 2851-2856 - [c2]Ankush Chakrabarty, Gregery T. Buzzard, Martin J. Corless, Stanislaw H. Zak, Ann E. Rundell:
Correcting hypothalamic-pituitary-adrenal axis dysfunction using observer-based explicit nonlinear model predictive control. EMBC 2014: 3426-3429 - 2013
- [j3]Ankush Chakrabarty, Harsh Jain, Amitava Chatterjee:
Volterra kernel based face recognition using artificial bee colonyoptimization. Eng. Appl. Artif. Intell. 26(3): 1107-1114 (2013) - [c1]Ankush Chakrabarty, Serena M. Pearce, Robert P. Nelson, Ann E. Rundell:
Treating acute myeloid leukemia via HSC transplantation: A preliminary study of multi-objective personalization strategies. ACC 2013: 3790-3795 - 2012
- [j2]Ankush Chakrabarty, Olivia Choudhury, Pallab Sarkar, Avishek Paul, Debarghya Sarkar:
Hyperspectral image classification incorporating bacterial foraging-optimized spectral weighting. Artif. Intell. Res. 1(1): 63-83 (2012) - 2011
- [j1]Suvadeep Banerjee, Ankush Chakrabarty, Sayan Maity, Amitava Chatterjee:
Feedback linearizing indirect adaptive fuzzy control with foraging based on-line plant model estimation. Appl. Soft Comput. 11(4): 3441-3450 (2011)
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-15 20:37 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
