2024
Queiroz, R., Sharma, D., Caldas, R., Czarnecki, K., García, S., Berger, T., Pelliccione, P., "A Driver-Vehicle Model for ADS Scenario-Based Testing" in IEEE Transactions on Intelligent Transportation Systems. 03/2024. 2024. Available in IEEEXplore
Wang, J., Pant, Y. V., Zhao, L., Antkiewicz M., Czarnecki,K., "Enhancing Safety in Mixed Traffic: Learning-Based Modeling and Efficient Control of Autonomous and Human-Driven Vehicles," in IEEE Transactions on Intelligent Transportation Systems. 04/2024. 2024. Available in IEEEXplore
Yacoub, M., Antkiewicz, M., Czarnecki, K., & McPhee, J., "Gain-scheduled model predictive controller for vehicle-following trajectory generation for autonomous vehicles," Vehicle System Dynamics, 26. Available at Taylor & Francis.
Thérien, B., Huang, C., Chow, A., & Czarnecki, K., "Object Re-identification from Point Clouds". IEEE/CVF/Winter/Conference/on/Applications/of/Computer/Vision/(WACV). 2024. Available in IEEE Computer Society.
Theses
Stewart, Connor Raymond, "Traffic Rule Checking and Validation", MMath, University of Waterloo, 2024.
Dykhne, Henry Eli, "Automated Generation of Dynamic Occlusion-Caused Collisions", MMath.
- Thesis presentation video
2023
Lee, J., Sedwards, S., Czarnecki, K., Uniformly Constrained Reinforcement Learning. Accepted for Publication in Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS): Special Issue on Multi-Objective Decision Making (MODeM), 2023.
Theses
Mannes, Christopher Gus, "Sparse2SOAP: Domain Adaptation for LiDAR-Based 3D Object Detection", MMath, University of Waterloo, 2023.
Bhattacharyya, Prarthana, "Perception and Prediction in Multi-Agent Urban Traffic Scenarios for Autonomous Driving", PhD, University of Waterloo, 2023.
Rowe, Luke, "FJMP: Factorized Joint Multi-Agent Motion Prediction". MMath, University of Waterloo, 2023.
- Thesis presentation video
Therien, Benjamin, "Towards Object Re-identification from Point Clouds for 3D MOT", MMath, University of Waterloo, 2023.
2022
Kahn, M., Sarkar, A., Czarnecki, K., I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames.. I Know You Can’t See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames. 2022. Retrieved from https://arxiv.org/abs/2109.09807
Sarkar, A., Larson, K., Czarnecki, K., Generalized dynamic cognitive hierarchy models for strategic driving behavior.. Generalized Dynamic Cognitive Hierarchy Models for Strategic Driving Behavior. 2022. Retrieved from https://arxiv.org/abs/2109.09861
Bouchard, F., Sedwards, S., Czarnecki, K., A Rule-Based Behaviour Planner for Autonomous Driving. Berlin, Springer, 2022.
Larter, S., Queiroz, R., Sedwards, S., Sarkar, A., Czarnecki, K., A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation. Aachen, Germany: IEEE, 2022. https://doi.org/10.1109/IV51971.2022.9827035
Theses
Rodrigo Queiroz, Scenario Modeling and Execution for Simulation Testing of Automated-Driving Systems. PhD, University of Waterloo, 2022.
Scott Larter, A Hierarchical Pedestrian Behaviour Model to Reproduce Realistic Human Behaviour in a Traffic Environment. MMath. 2022. Retrieved from http://hdl.handle.net/10012/18094
Sunsheng Gu, XC: Exploring Quantitative Use Cases for Explanations in 3D Object Detection. MSc. 2022. Retrieved from http://hdl.handle.net/10012/17899
Matthew Pitropov, LiDAR-MIMO: Efficient Uncertainty Estimation for LiDAR-based 3D Object Detection. MSc. 2022. Retrieved from http://hdl.handle.net/10012/18062
Atrisha Sarkar, Empirical Game Theoretic Models for Autonomous Driving: Methods and Applications. MMath. 2022. Retrieved from http://hdl.handle.net/10012/18751
Van Duong Nguyen, Out-of-Distribution Detection for LiDAR-based 3D Object Detection. MSc. 2022. Retrieved from http://hdl.handle.net/10012/17902
2021
Sarkar, A., Czarnecki, K., Solution Concepts in Hierarchical Games Under Bounded Rationality With Applications to Autonomous Driving.. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/16715, 2021.
Ernst, G., Sedwards, S., Zhang, Z., Hasuo, I., Falsification of Hybrid Systems Using Adaptive Probabilistic Search. ACM Transactions on Modeling and Computer Simulation (TOMACS), 31, 1-22. https://doi.org/10.1145/3459605, 2021.
Sarkar, A., Larson, K., Czarnecki, K., A taxonomy of strategic human interactions in traffic conflicts. A Taxonomy of Strategic Human Interactions in Traffic Conflicts. Presented at the. Retrieved from https://arxiv.org/abs/2109.13367, 2021.
Abdelzad, V., Lee, J., Sedwards, S., Soltani, S., Czarnecki, K., Non-divergent Imitation for Verification of Complex Learned Controllers. Shenzhen, China (virtual): IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533410, 2021.
Balakrishnan, A., Lee, J., Gaurav, A., Czarnecki, K., Sedwards, S., Transfer Reinforcement Learning for Autonomous Driving: From WiseMove to WiseSim. ACM Transactions on Modeling and Computer Simulation, 31, Article No. 15, pp 1~26. https://doi.org/10.1145/3449356, 2021.
Lee, J., Sutton, R. S., Policy iterations for reinforcement learning problems in continuous time and space - Fundamental theory and methods. Automatica, 126, 109421, 15 pages. https://doi.org/10.1016/j.automatica.2020.109421, 2021.
Lee, S., Lee, J., Hasuo, I., Predictive PER: Balancing Priority and Diversity Towards Stable Deep Reinforcement Learning. Shenzhen, China (virtual): IEEE. https://doi.org/10.1109/IJCNN52387.2021.9534243, 2021.
Lee, J., Sedwards, S., Czarnecki, K., Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning. Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning. Presented at the. Online at http://modem2021.cs.nuigalway.ie/. Retrieved from https://arxiv.org/abs/2201.07958, 2021.
Theses
Maximilian Kahn, Dynamic-Occlusion-Aware Risk Identification for Autonomous Vehicles Using Hypergames. Retrieved from http://hdl.handle.net/10012/17774, 2021.
2020
Lee, S, Lee, J, Hasuo, I., Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning. Predictive PER: Balancing Priority and Diversity towards Stable Deep Reinforcement Learning. Presented at the. Retrieved from https://sites.google.com/view/deep-rl-workshop-neurips2020/home, 2020.
Budde, C, D'Argenio, P, Hartmanns, A, Sedwards, S., An Efficient Statistical Model Checker for Nondeterminism and Rare Events. International Journal on Software Tools For Technology Transfer, Special Issue TACAS 2018, 2020.
Salay, R, Czarnecki, K, Alvarez, I, Elli, M. S., Sedwards, S, Weast, J., PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety with ML. PURSS: Towards Perceptual Uncertainty Aware Responsibility Sensitive Safety With ML. Presented at the. New York: CEUR, 2020.
Jhunjhunwala, A, Lee, J, Sedwards, S, Abdelzad, V, Czarnecki, K., Improved Policy Extraction via Online Q-Value Distillation. Improved Policy Extraction via Online Q-Value Distillation. Presented at the. Glasgow: IEEE, 2020.
Chen, H, Cohen, R, Dautenhahn, K, Law, E, Czarnecki, K., Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations. Autonomous Vehicle Visual Signals for Pedestrians: Experiments and Design Recommendations. Presented at the. Retrieved from https://arxiv.org/abs/2010.05115, 2020.
Gaurav, A, Vernekar, S, Lee, J, Sedwards, S, Abdelzad, V, Czarnecki, K., Simple Continual Learning Strategies for Safer Classifers. Simple Continual Learning Strategies for Safer Classifers. Presented at the. CEUR. Retrieved from http://ceur-ws.org/Vol-2560/paper6.pdf, 2020.
Dillen, N, Ilievski, M, Law, E, Nacke, L. E., Czarnecki, K, Schneider, O., Keep Calm and Ride Along: Passenger Comfort and Anxiety as Physiological Responses to Autonomous Driving Styles. Keep Calm and Ride Along: Passenger Comfort and Anxiety As Physiological Responses to Autonomous Driving Styles. Presented at the. ACM. https://doi.org/10.1145/3313831.3376247, 2020.
Antkiewicz, M, Kahn, M, Ala, M, Czarnecki, K, Wells, P, Acharya, A, Beiker, S., Modes of Automated Driving System Scenario Testing: Experience Report and Recommendations. SAE Int. J. Adv. & Curr. Prac. In Mobility, 2, 2248-2266. https://doi.org/10.4271/2020-01-1204, 2020.
Theses
Aravind Balakrishnan. Closing the Modelling Gap: Transfer Learning from a Low-Fidelity Simulator for Autonomous Driving. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15570, 2020.
Pavel Valov. Transferring Pareto Frontiers across Heterogeneous Hardware Environments. Waterloo. Retrieved from http://hdl.handle.net/10012/16295, 2020.
Chen, W. T. Accelerating the Training of Convolutional Neural Networks for Image Segmentation with Deep Active Learning. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15537, 2020.
Marko Ilievski, WiseBench: A Motion Planning Benchmarking Framework for Autonomous Vehicles. Waterloo. Retrieved from http://hdl.handle.net/10012/16422, 2020.
Frederic Bouchard, Expert System and a Rule Set Development Method for Urban Behaviour Planning. Retrieved from http://hdl.handle.net/10012/15864, 2020.
Taylor Denouden, An Application of Out-of-Distribution Detection for Two-Stage Object Detection Networks. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15646, 2020.
Ashish Gaurav, Safety-Oriented Stability Biases for Continual Learning. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15579, 2020.
Chen, H Autonomous Vehicles with Visual Signals for Pedestrians: Experiments and Design Recommendations. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15534, 2020.
Sachin Vernekar, Training Reject-Classifiers for Out-of-distribution Detection via Explicit Boundary Sample Generation. Waterloo. Retrieved from http://hdl.handle.net/10012/15582, 2020.
2019
Sarkar, A., Czarnecki, K. A behavior driven approach for sampling rare event situations for autonomous vehicles. A Behavior Driven Approach for Sampling Rare Event Situations for Autonomous Vehicles. Presented at the. Retrieved from https://ieeexplore.ieee.org/abstract/document/8967715, 2019.
Khan, S., Phan, B. T., Salay, R., Czarnecki, K., ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. Presented at the. Long Beach, California, USA: IEEE. Retrieved from https://bit.ly/2G3MA1P, 2019.
Ilievski, M., Sedwards, S., Gaurav, A., Balakrishnan, A., Sarkar, A., Lee, J., Bouchard, F. ed\ eric, De Iaco, R., Czarnecki, K., Design Space of Behaviour Planning for Autonomous Driving. Waterloo. Retrieved from https://arxiv.org/abs/1908.07931, 2019.
Li, C., Czarnecki, K., Urban Driving with Multi-Objective Deep Reinforcement Learning. Urban Driving With Multi-Objective Deep Reinforcement Learning. Presented at the. Montreal: IFAAMAS, 2019.
Li, C., Czarnecki, K., Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective. Rethinking Expected Cumulative Reward Formalism of Reinforcement Learning: A Micro-Objective Perspective. Montreal, 2019.
Vernekar, S., Gaurav, A., Denouden, T., Phan, B. T., Abdelzad, V., Salay, R., Czarnecki, K., Analysis of Confident-Classifiers for Out-of-Distribution Detection. Analysis of Confident-Classifiers for Out-of-Distribution Detection. Presented at the. Retrieved from https://drive.google.com/uc?export=download\&id=1AY0zFvQ_u1UmGcn0bHs1XCWX9QncaqvT, 2019.
Sarkar, A., Czarnecki, K., A behavior driven approach for sampling rare event situations for autonomous vehicles. A Behavior Driven Approach for Sampling Rare Event Situations for Autonomous Vehicles. Presented at the. Retrieved from https://ieeexplore.ieee.org/abstract/document/8967715, 2019.
Khan, S., Phan, B. T., Salay, R., Czarnecki, K., ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. ProcSy: Procedural Synthetic Dataset Generation Towards Influence Factor Studies Of Semantic Segmentation Networks. Presented at the. Long Beach, California, USA: IEEE. Retrieved from https://bit.ly/2G3MA1P, 2019.
Ilievski, M., Sedwards, S., Gaurav, A., Balakrishnan, A., Sarkar, A., Lee, J., Bouchard, F., De Iaco, R., Czarnecki, K., Design Space of Behaviour Planning for Autonomous Driving. Waterloo. Retrieved from https://arxiv.org/abs/1908.07931, 2019.
Li, C., Czarnecki, K., Urban Driving with Multi-Objective Deep Reinforcement Learning. Urban Driving With Multi-Objective Deep Reinforcement Learning. Presented at the. Montreal: IFAAMAS, 2019.
Babaee, R., Ganesh, V., Sedwards, S., Accelerated Learning of Predictive Runtime Monitors for Rare Failure. Porto, Portugal: Springer, 2019.
De Iaco, R., Smith, S. L., Czarnecki, K., Learning a Lattice Planner Control Set for Autonomous Vehicles. Learning a Lattice Planner Control Set for Autonomous Vehicles. Presented at the. Paris, France. https://doi.org/10.1109/IVS.2019.8813797, 2019.
Phan, B. T., Khan, S., Salay, R., Czarnecki, K., Bayesian Uncertainty Quantification with Synthetic Data. Bayesian Uncertainty Quantification With Synthetic Data. Presented at the. Turku, Finland: SAFECOMP. Retrieved from https://www.waise.org/, 2019.
Vernekar, S., Gaurav, A., Denouden, T., Phan, B. T., Abdelzad, V., Salay, R., Czarnecki, K., Analysis of Confident-Classifiers for Out-of-Distribution Detection. Analysis of Confident-Classifiers for Out-of-Distribution Detection. Presented at the. Retrieved from https://drive.google.com/uc?export=download\&id=1AY0zFvQ_u1UmGcn0bHs1XCWX9QncaqvT, 2019.
Theses
Angus, M., Towards Pixel-Level OOD Detection for Semantic Segmentation. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15004, 2019.
Edward Chao, Autonomous Driving: Mapping and Behavior Planning for Crosswalks. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15121, 2019.
Jiang Deng, MLOD: A multi-view 3D object detection based on robust feature fusion method. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15086, 2019.
Ryan De Iaco, Motion Planning and Safety for Autonomous Driving. Waterloo. Retrieved from http://hdl.handle.net/10012/15303, 2019.
Aman Jhunjhunwala, Policy Extraction via Online Q-Value Distillation. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/14963, 2019.
Braden Hurl, Local and Cooperative Autonomous Vehicle Perception from Synthetic Datasets. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15118, 2019.
Samin Khan, Towards Synthetic Dataset Generation for Semantic Segmentation Networks. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/15128, 2019.
Li, C., Autonomous Driving: A Multi-Objective Deep Reinforcement Learning Approach. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/14697, 2019.
2018
Zhang, Z., Ernst, G., Hasuo, I., Sedwards, S., Time-Staging Enhancement of Hybrid System Falsification. Porto, Portugal: IEEE. Retrieved from https://ieeexplore.ieee.org/abstract/document/8429475, 2018.
D\textquoterightArgenio, P., Hartmanns, A., Sedwards, S., Lightweight Statistical Model Checking in Nondeterministic Continuous Time. Limassol, Cyprus: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-03421-4_22, 2018.
Phan, B. T., Salay, R., Czarnecki, K., Abdelzad, V., Denouden, T., Vernekar, S., Calibrating Uncertainties in Object Localization Task. Calibrating Uncertainties in Object Localization Task, 2018.
Czarnecki, K., Salay, R., Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving. Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving. Presented at the. Västerr as, Sweden: Springer, 2018.
Juodisius, P., Sarkar, A., Mukkamala, R. R., Antkiewicz, M., Czarnecki, K., Wąsowski, A. W., Clafer: Lightweight Modeling of Structure and Behaviour. The Art, Science, and Engineering of Programming Journal, 3. https://doi.org/10.22152/programming-journal.org/2019/3/2, 2018.
Zhang, Z., Ernst, G., Sedwards, S., Arcani, P., Hasuo, I., Two-Layered Falsification of Hybrid Systems Guided by Monte Carlo Tree Search. IEEE TCAD. Torino, Italy: IEEE. Retrieved from https://ieeexplore.ieee.org/document/8418450, 2018.
Zayan, D., Sarkar, A., Antkiewicz, M., Maciel, R. S. P., Czarnecki, K., Example-driven modeling: on effects of using examples on structural model comprehension, what makes them useful, and how to create them. https://doi.org/10.1007/s10270-017-0652-3, 2018.
Budde, C., D\textquoterightArgenio, P., Hartmanns, A., Sedwards, S., A Statistical Model Checker for Nondeterminism and Rare Events. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-89963-3_20, 2018.
Given-Wilson, T., Legay, A., Sedwards, S., Zendra, O., Group abstraction for assisted navigation of social activities in intelligent environments. Springer Journal of Reliable Intelligent Environments, 4, 107-120. Retrieved from https://link.springer.com/article/10.1007/s40860-018-0058-1, 2018.
Colwell, I., Phan, B. T., Saleem, S., Salay, R., Czarnecki, K. An Automated Vehicle Safety Concept Based on Runtime Restriction of the Operational Design Domain. An Automated Vehicle Safety Concept Based on Runtime Restriction of the Operational Design Domain, 2018.
Angus, M., ElBalkini, M., Khan, S., Harakeh, A., Andrienko, O., Reading, C., Czarnecki, K., Waslander, S. Unlimited Road-scene Synthetic Annotation (URSA) Dataset. Unlimited Road-Scene Synthetic Annotation (URSA) Dataset. Presented at the. Maui, Hawaii, USA: IEEE. Retrieved from https://arxiv.org/abs/1807.06056, 2018.
Passos, L., Queiroz, R., Mukelabai, M., Berger, T., Apel, S., Czarnecki, K., Padilla, J. A., A Study of Feature Scattering in the Linux Kernel. IEEE Transactions on Software Engineering, 2018.
D\textquoterightArgenio, P., Gerhold, M., Hartmanns, A., Sedwards, S. A Hierarchy of Scheduler Classes for Stochastic Automata. Thessaloniki, Greece: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-89366-2_21, 2018.
Kido, K., Sedwards, S., Hasuo, I. Bounding Errors Due to Switching Delays in Incrementally Stable Switched Systems. Oxford, United Kingdom: Elsevier. Retrieved from https://www.sciencedirect.com/science/article/pii/S2405896318311583, 2018.
Theses
Colwell, I. (2018). Runtime Restriction of the Operational Design Domain: A Safety Concept for Automated Vehicles. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13398, 2018.
Jimmy Hui Liang, Machine Learning for SAT Solvers. Waterloo, ON, Canada. Retrieved from http://hdl.handle.net/10012/14207, 2018.
Edward Zulkoski, Understanding and Enhancing CDCL-based SAT Solvers. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13525, 2018.
Rachul Chandail, Vision Augmented State Estimation with Fault Tolerance. Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13291, 2018.
2017
Sarkar, A., Czarnecki, K., Angus, M., Li, C., Waslander, S. Trajectory prediction of traffic agents at urban intersections through learned interactions.. Trajectory Prediction of Traffic Agents at Urban Intersections through Learned Interactions. Presented at the. Retrieved from https://ieeexplore.ieee.org/document/8317731, 2017.
Larson, K., Peled, D., Sedwards, S. Memory-Efficient Tactics for Randomized LTL Model Checking. Heidelberg, Germany: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-72308-2_10, 2017.
Valov, P., Petkovich, J.-C., Guo, J., Fischmeister, S., Czarnecki, K. Transferring Performance Prediction Models Across Different Hardware Platforms. Transferring Performance Prediction Models Across Different Hardware Platforms. Presented at the. https://doi.org/10.1145/3030207.3030216, 2017.
Chauchan, M., Pellizzoni, R., Czarnecki, K. Modeling the Effects of AUTOSAR Overheads on Application Timing and Schedulability. Modeling the Effects of AUTOSAR Overheads on Application Timing and Schedulability. Presented at the. Retrieved from https://dac.com/2017/accepted-papers, 2017.
Guo, J., Blais, E., Czarnecki, K., van Beek, P. A Worst-Case Analysis of Constraint-Based Algorithms for Exact Multi-objective Combinatorial Optimization. A Worst-Case Analysis of Constraint-Based Algorithms for Exact Multi-Objective Combinatorial Optimization. Presented at the. https://doi.org/10.1007/978-3-319-57351-9_16, 2017.
Queiroz, R., Passos, L., Valente, M. T., Hunsen, C., Apel, S., Czarnecki, K. The shape of feature code: an analysis of twenty C-preprocessor-based systems. Software & Systems Modeling, 16, 96. https://doi.org/10.1007/s10270-015-0483-z, 2017.
Zulkoski, E., Bright, C., Heinle, A., Kotsireas, I., Czarnecki, K., Ganesh, V. Combining SAT Solvers with Computer Algebra Systems to Verify Combinatorial Conjectures. Journal of Automated Reasoning, 58, 339. https://doi.org/10.1007/s10817-016-9396-y, 2017.
Kido, K., Sedwards, S., Hasuo, I. Switching Delays and the Skorokhod Distance in Incrementally Stable Switched Systems. Seoul, South Korea: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-17910-6_9, 2017.
Hartmanns, A., Sedwards, S., D'Argenio, P. Efficient Simulation-based Verification of Probabilistic Timed Automata. Efficient Simulation-Based Verification of Probabilistic Timed Automata, 1419\textendash1430. Las Vegas, USA: IEEE. https://doi.org/10.1109/WSC.2017.8247885, 2017.
Ross, J., Murashkin, A., Liang, J. H., Antkiewicz, M., Czarnecki, K. Synthesis and Exploration of Multi-Level, Multi-Perspective Architectures of Automotive Embedded Systems. Software and Systems Modeling. https://doi.org/10.1007/s10270-017-0592-y, 2017.
Queiroz, R., Passos, L., Valente, M. T., Hunsen, C., Apel, S., Czarnecki, K. The shape of feature code: an analysis of twenty C-preprocessor-based systems. Software & Systems Modeling, 16, 96. https://doi.org/10.1007/s10270-015-0483-z, 2017.
2016
Khalilov, E., Ross, J., Antkiewicz, M., Völter, M., Czarnecki, K. Modeling and Optimizing Automotive Electric/Electronic (E/E) Architectures: Towards Making Clafer Accessible to Practitioners. Modeling and Optimizing Automotive Electric Electronic (E E) Architectures: Towards Making Clafer Accessible to Practitioners. Presented at the. https://doi.org/10.1007/978-3-319-47169-3_37, 2016.
Liang, J. H., Ganesh, V., Poupart, P., Czarnecki, K. Exponential Recency Weighted Average Branching Heuristic for SAT Solvers. Exponential Recency Weighted Average Branching Heuristic for SAT Solvers. Presented at the. Retrieved from http://dl.acm.org/citation.cfm?id=3016100.3016385, 2016.
Zulkoski, E., Ganesh, V., Czarnecki, K. MathCheck: A Math Assistant via a Combination of Computer Algebra Systems and SAT Solvers. MathCheck: A Math Assistant via a Combination of Computer Algebra Systems and SAT Solvers. AAAI Press, 2016.
Theses
Atrisha Sarkar, Meta-learning Performance Prediction of Highly Configurable Systems: A Cost-oriented Approach. Waterloo. Retrieved from http://hdl.handle.net/10012/10406, 2016.
2015
Sarkar, A., Guo, J., Siegmund, N., Apel, S., Czarnecki, K. Cost-efficient sampling for performance prediction of configurable systems. Cost-Efficient Sampling for Performance Prediction of Configurable Systems. Retrieved from https://ieeexplore.ieee.org/document/7372023, 2015.