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Radu Grosu

Univ.Prof. Dipl.-Ing. Dr.rer.nat. Dr.h.c.

Research Focus

  • Computer Engineering: 100%

Research Areas

  • Modelling, Control, Cyber-Physical Systems, Verification, Analysis, Abstraction, Stochastic Model Checking, Compositional Reasoning, Modelling, Analysis and Control of Cardiac-Cell Networks
Radu Grosu

About

research interests include modeling, analysis and control of cyber-physical and biological systems and application focus includes green operating systems, mobile ad-hoc networks, automotive systems, the Mars rover, cardiac-cell networks and genetic regulatory networks.

Roles

Contact

2024W

2025S

 

2025

  • Real-Time Recurrent Reinforcement Learning / Lemmel, J., & Grosu, R. (2025). Real-Time Recurrent Reinforcement Learning. In Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (pp. 18189–18197). AAAI Press. https://doi.org/10.1609/aaai.v39i17.34001

2024

  • Prediction of Tourism Flow with Sparse Geolocation Data / Lemmel, J., Babaiee, Z., Kleinlehner, M., Majic, I., Neubauer, P., Scholz, J., Grosu, R., & Neubauer, S. (2024). Prediction of Tourism Flow with Sparse Geolocation Data. In P. Haber, T. J. Lampoltshammer, & M. Mayr (Eds.), Data Science—Analytics and Applications : Proceedings of the 5th International Data Science Conference—iDSC2023 (pp. 45–52). Springer Cham. https://doi.org/10.1007/978-3-031-42171-6_6
  • Learning with Chemical versus Electrical Synapses Does it Make a Difference? / Farsang, M., Lechner, M., Lung, D., Hasani, R., Rus, D., & Grosu, R. (2024). Learning with Chemical versus Electrical Synapses Does it Make a Difference? In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 15106–15112). https://doi.org/10.1109/ICRA57147.2024.10611016
  • DeepRIoT: Continuous Integration and Deployment of Robotic-IoT Applications / Qu, M., He, J., Tucakovic, Z., Bartocci, E., Nickovic, D., Isakovic, H., & Grosu, R. (2024). DeepRIoT: Continuous Integration and Deployment of Robotic-IoT Applications. In DAC ’24: Proceedings of the 61st ACM/IEEE Design Automation Conference (pp. 1–6). https://doi.org/10.1145/3649329.3658250
    Projects: EdgeAI (2022–2025) / TAIGER (2023–2027)
  • Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers / Lygizou, E. M., Reiter, M., Maurer-Granofszky, M., Dworzak, M., & Grosu, R. (2024). Automated Immunophenotyping Assessment for Diagnosing Childhood Acute Leukemia using Set-Transformers. In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, United States of America (the). IEEE. https://doi.org/10.1109/EMBC53108.2024.10781595
  • Flock-Formation Control of Multi-Agent Systems using Imperfect Relative Distance Measurements / Brandstätter, A., Smolka, S. A., Stoller, S. D., Tiwari, A., & Grosu, R. (2024). Flock-Formation Control of Multi-Agent Systems using Imperfect Relative Distance Measurements. In Proceedings 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 12193–12200). https://doi.org/10.1109/ICRA57147.2024.10610147
  • Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels / Babaiee, Z., Mohseni Kiasari, P., Rus, D., & Grosu, R. (2024). Unveiling the Unseen: Identifiable Clusters in Trained Depthwise Convolutional Kernels. In The Twelth International Conference on Learning Representations, ICLR 2024, Vienna, Austria, May 7-11, 2024. The Twelfth International Conference on Learning Representations (ICLR 2024), Austria. http://hdl.handle.net/20.500.12708/203933
  • Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields / Babaiee, Z., Mohseni Kiasari, P., Rus, D., & Grosu, R. (2024). Neural Echos: Depthwise Convolutional Filters Replicate Biological Receptive Fields. In 2024 IEEE Winter Conference on Applications of Computer Vision (pp. 8216–8225). https://doi.org/10.1109/WACV57701.2024.00803
    Project: MATTO-GBM (2024–2027)
  • Learning Adaptive Safety for Multi-Agent Systems / Berducci, L., Yang, S., Mangharam, R., & Grosu, R. (2024). Learning Adaptive Safety for Multi-Agent Systems. In 2024 IEEE International Conference on Robotics and Automation (ICRA) (pp. 2859–2865). https://doi.org/10.1109/ICRA57147.2024.10611037

2023

  • TD-Magic: From Pictures of Timing Diagrams To Formal Specifications / He, J., Nickovic, D., Bartocci, E., & Grosu, R. (2023). TD-Magic: From Pictures of Timing Diagrams To Formal Specifications. In 2023 60th ACM/IEEE Design Automation Conference (DAC) (pp. 1–6). IEEE. https://doi.org/10.1109/DAC56929.2023.10247685
    Project: ADEX (2020–2024)
  • Investigation and benchmarking of U-Nets on prostate segmentation tasks / Bhandary, S., Kuhn, D., Babaiee, Z., Fechter, T., Benndorf, M., Zamboglou, C., Grosu, A.-L., & Grosu, R. (2023). Investigation and benchmarking of U-Nets on prostate segmentation tasks. Computerized Medical Imaging and Graphics, 107, Article 102241. https://doi.org/10.1016/j.compmedimag.2023.102241
    Download: PDF (1.58 MB)
    Project: PersoRad (2020–2023)
  • Enhancing Robot Learning through Learned Human-Attention Feature Maps / Scheuchenstuhl, D., Ulmer, S., Resch, F., Berducci, L., & Grosu, R. (2023, May 29). Enhancing Robot Learning through Learned Human-Attention Feature Maps [Poster Presentation]. ICRA 2023 Workshop on effective Representations, Abstractions, and Priors for Robot Learning (Rap4Robots), London, United Kingdom of Great Britain and Northern Ireland (the). https://doi.org/10.34726/4861
    Download: Full paper version (783 KB)
    Project: NimbleAI (2022–2025)
  • Multi-Agent Spatial Predictive Control with Application to Drone Flocking / Brandstatter, A., Smolka, S. A., Stoller, S. D., Tiwari, A., & Grosu, R. (2023). Multi-Agent Spatial Predictive Control with Application to Drone Flocking. In 2023 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1221–1227). IEEE. https://doi.org/10.1109/ICRA48891.2023.10160617

2022

  • Safe Policy Improvement in Constrained Markov Decision Processes / Berducci, L., & Grosu, R. (2022). Safe Policy Improvement in Constrained Markov Decision Processes. In T. Margaria & B. Steffen (Eds.), Leveraging Applications of Formal Methods, Verification and Validation. Verification Principles (ISoLA 2022), Proceedings, Part I (pp. 360–381). Springer. https://doi.org/10.1007/978-3-031-19849-6_21
    Project: ADEX (2020–2024)
  • Towards Drone Flocking Using Relative Distance Measurements / Brandstätter, A., Smolka, S. A., Stoller, S. D., Tiwari, A., & Grosu, R. (2022). Towards Drone Flocking Using Relative Distance Measurements. In Leveraging Applications of Formal Methods, Verification and Validation. Adaptation and Learning (ISoLA 2022). Proceedings, Part III (pp. 97–109). Springer. https://doi.org/10.1007/978-3-031-19759-8_7
  • Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data / Lemmel, J., Babaiee, Z., Kleinlehner, M., Majic, I., Neubauer, P., Scholz, J., Grosu, R., & Neubauer, S. (2022). Deep-Learning vs Regression: Prediction of Tourism Flow with Limited Data. In Schedule - IJCAI’22 Workshop. AI4TS: AI for Time Series Analysis. IJCAI’22 Workshop - AI4TS: AI for Time Series Analysis, Vienna, Austria. IJCAI. https://doi.org/10.34726/4262
    Download: PDF (257 KB)
  • Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing / Brunnbauer, A., Berducci, L., Brandstätter, A., Lechner, M., Hasani, R., Rus, D., & Grosu, R. (2022). Latent Imagination Facilitates Zero-Shot Transfer in Autonomous Racing. In 2022 IEEE International Conference on Robotics and Automation (ICRA) (pp. 7513–7520). https://doi.org/10.1109/ICRA46639.2022.9811650
  • DeepSTL / He, J., Bartocci, E., Ničković, D., Isakovic, H., & Grosu, R. (2022). DeepSTL. In ICSE ’22: Proceedings of the 44th International Conference on Software Engineering (pp. 610–622). Association for Computing Machinery. https://doi.org/10.1145/3510003.3510171

2021

  • QoS for Dynamic Deployment of IoT Services / Isakovic, H., Ferreira, L. L., Okic, I., Dukkon, A., Tucakovic, Z., & Grosu, R. (2021). QoS for Dynamic Deployment of IoT Services. In 2021 22nd IEEE International Conference on Industrial Technology (ICIT). IEEE. https://doi.org/10.1109/icit46573.2021.9453670
  • Collision-Free 3D Flocking Using the Distributed Simplex Architecture / Mehmood, U., Stoller, S. D., Grosu, R., & Smolka, S. A. (2021). Collision-Free 3D Flocking Using the Distributed Simplex Architecture. In Formal Methods in Outer Space : Essays Dedicated to Klaus Havelund on the Occasion of His 65th Birthday (pp. 147–156). Springer. https://doi.org/10.1007/978-3-030-87348-6_9
  • Lumpability for Uncertain Continuous-Time Markov Chains / Cardelli, L., Grosu, R., Larsen, K. G., Tribastone, M., Tschaikowski, M., & Vandin, A. (2021). Lumpability for Uncertain Continuous-Time Markov Chains. In Quantitative Evaluation of Systems (pp. 391–409). Springer, LNCS. https://doi.org/10.1007/978-3-030-85172-9_21
  • Adversarial Training is Not Ready for Robot Learning / Lechner, M., Hasani, R., Grosu, R., Rus, D., & Henzinger, T. A. (2021). Adversarial Training is Not Ready for Robot Learning. In In Proc. of ICRA’21, the International Conference on Robotics and Automation (pp. 1–8). IEEE. http://hdl.handle.net/20.500.12708/55648
  • Distributed Control for Flocking Maneuvers via Acceleration-Weighted Neighborhooding / Roy, S., Usama, M., Grosu, R., Smolka, S. A., & Stoller, S. D. (2021). Distributed Control for Flocking Maneuvers via Acceleration-Weighted Neighborhooding. In 2021 American Control Conference (ACC). American Control Conference, Online, United States of America (the). IEEE. https://doi.org/10.23919/acc50511.2021.9483155
  • On The Verification of Neural ODEs with Stochastic Guarantees / Grünbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., & Grosu, R. (2021). On The Verification of Neural ODEs with Stochastic Guarantees. In Proceedings of the AAAI Conference on Artificial Intelligence (pp. 11525–11535). Proceedings of the AAAI Conference on Artificial Intelligence. http://hdl.handle.net/20.500.12708/58537
  • Liquid Time-Constant Networks / Hasani, R., Lechner, M., Amini, A., Rus, D., & Grosu, R. (2021). Liquid Time-Constant Networks. In Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) (pp. 7657–7666). Proceedings of the AAAI Conference on Artificial Intelligence. http://hdl.handle.net/20.500.12708/55647
  • On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification / Babaiee, Z., Hasani, R., Lechner, M., Rus, D., & Grosu, R. (2021). On-Off Center-Surround Receptive Fields for Accurate and Robust Image Classification. In International Conference on Machine Learning (pp. 478–489). Proceedings of Machine Learning Research. http://hdl.handle.net/20.500.12708/55625
  • A Distributed Simplex Architecture for Multi-agent Systems / Usama, M., Stoller, S. D., Grosu, R., Roy, S., Damare, A., & Smolka, S. A. (2021). A Distributed Simplex Architecture for Multi-agent Systems. In Dependable Software Engineering. Theories, Tools, and Applications (pp. 239–257). Springer. https://doi.org/10.1007/978-3-030-91265-9_13
  • Adaptive Signal Filtering Platform for a CPS/IoT Ecosystem / Isakovic, H., Dangl, S., Tucakovic, Z., & Grosu, R. (2021). Adaptive Signal Filtering Platform for a CPS/IoT Ecosystem. In 2021 22nd IEEE International Conference on Industrial Technology (ICIT). IEEE. https://doi.org/10.1109/icit46573.2021.9453496

2020

2019

  • A generative neural network model for the quality prediction of work in progress products / Wang, G., Ledwoch, A., Hasani, R. M., Grosu, R., & Brintrup, A. (2019). A generative neural network model for the quality prediction of work in progress products. Applied Soft Computing, 85, Article 105683. https://doi.org/10.1016/j.asoc.2019.105683
  • Probabilistic reachability for multi-parameter bifurcation analysis of cardiac alternans / Islam, Md. A., Cleaveland, R., Fenton, F. H., Grosu, R., Jones, P. L., & Smolka, S. A. (2019). Probabilistic reachability for multi-parameter bifurcation analysis of cardiac alternans. Theoretical Computer Science, 765, 158–169. https://doi.org/10.1016/j.tcs.2018.02.005
  • Adaptive Fault Detection Exploiting Redundancy with Uncertainties in Space and Time / Ratasich, D., Platzer, M., Grosu, R., & Bartocci, E. (2019). Adaptive Fault Detection Exploiting Redundancy with Uncertainties in Space and Time. In 2019 IEEE 13th International Conference on Self-Adaptive and Self-Organizing Systems (SASO). 13th IEEE International Conference on Self-Adaptive and Self-Organizing Systems, Umeå, Sweden. IEEE. https://doi.org/10.1109/saso.2019.00013
  • Designing Worm-inspired Neural Networks for Interpretable Robotic Control / Lechner, M., Hasani, R., Zimmer, M., Henzinger, T. A., & Grosu, R. (2019). Designing Worm-inspired Neural Networks for Interpretable Robotic Control. In Robotics and Automation (ICRA), IEEE International Conference on (pp. 87–94). http://hdl.handle.net/20.500.12708/58142
  • A Machine Learning Suite for Machine Components' Health-Monitoring / Hasani, R., Wang, G., & Grosu, R. (2019). A Machine Learning Suite for Machine Components’ Health-Monitoring. In 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019, 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019 (pp. 9472–9477). http://hdl.handle.net/20.500.12708/58140
  • CPS/IoT Ecosystem: Indoor Vertical Farming System / Isakovic, H., Grosu, R., Fasching, A., & Punzenberger, L. (2019). CPS/IoT Ecosystem: Indoor Vertical Farming System. In 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT). 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), Ancona, Italy. IEEE Xplore. https://doi.org/10.1109/isce.2019.8900974
  • Sensyml: Simulation Environment for large-scale IoT Applications / Isakovic, H., Grosu, R., Wally, B., Rausch, T., Dustdar, S., Kappel, G., Ratasich, D., & Bisanovic, V. (2019). Sensyml: Simulation Environment for large-scale IoT Applications. In IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society. 45th Annual Conference of the IEEE Industrial Electronics Society (IECON 2019), Lisbon, Portugal. IEEE Xplore. https://doi.org/10.1109/iecon.2019.8927756
  • Capacitive Soil Moisture Sensor Node for IoT in Agriculture and Home / Hirsch, C., Bartocci, E., & Grosu, R. (2019). Capacitive Soil Moisture Sensor Node for IoT in Agriculture and Home. In 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT). 2019 IEEE 23rd International Symposium on Consumer Technologies (ISCT), Ancona, Italy. https://doi.org/10.1109/isce.2019.8901012
  • CPS/IoT Ecosystem: A Platform for Research and Education / Isakovic, H., Ratasich, D., Hirsch, C., Platzer, M., Wally, B., Rausch, T., Nickovic, D., Krenn, W., Kappel, G., Dustdar, S., & Grosu, R. (2019). CPS/IoT Ecosystem: A Platform for Research and Education. In R. Chamberlain, W. Taha, & M. Törngren (Eds.), Cyber Physical Systems. Model-Based Design (pp. 206–213). Springer International Publishing. https://doi.org/10.1007/978-3-030-23703-5_12
  • Sequential Edge Clustering in Temporal Multigraphs / Ghalebi, E., Mayhar, H., Grosu, R., Taylor, G. W., & Williamson, S. A. (2019). Sequential Edge Clustering in Temporal Multigraphs. arXiv. https://doi.org/10.48550/arXiv.1905.11724
  • Neural Simplex Architecture / Phan, D., Paoletti, N., Grosu, R., Jansen, N., Smolka, S. A., & Stoller, S. D. (2019). Neural Simplex Architecture. arXiv. https://doi.org/10.48550/arXiv.1908.00528
  • A Nonparametric Bayesian Model for Sparse Temporal Multigraphs / Ghalebi, E., Mayhar, H., Grosu, R., Taylor, G. W., & Williamson, S. A. (2019). A Nonparametric Bayesian Model for Sparse Temporal Multigraphs. arXiv. https://doi.org/10.48550/arXiv.1910.05098
  • Neural Flocking: MPC-based Supervised Learning of Flocking Controllers / Roy, S., Mehmood, U., Grosu, R., Smolka, S. A., Stoller, S. D., & Tiwari, A. (2019). Neural Flocking: MPC-based Supervised Learning of Flocking Controllers. arXiv. https://doi.org/10.48550/arXiv.1908.09813
  • Under the Hood of a Stand-Alone Lagrangian Reachability Tool / Gruenbacher, S., Cyranka, J., Islam, M. A., Tschaikowski, M., Smolka, S., & Grosu, R. (2019). Under the Hood of a Stand-Alone Lagrangian Reachability Tool. In G. Frehse & M. Althoff (Eds.), ARCH19. 6th International Workshop on Applied Verification of Continuous and Hybrid Systems (Vol. 61, pp. 211–219). EasyChair. https://doi.org/10.29007/ns8p
  • Statistical model checking / Legay, A., Lukina, A., Traonouez, L. M., Yang, J., Smolka, S. A., & Grosu, R. (2019). Statistical model checking. In Computing and Software Science (pp. 478–504). Springer LNCS. http://hdl.handle.net/20.500.12708/30240
  • Quantitative Regular Expressions for Arrhythmia Detection / Abbas, H., Rodionova, A., Mamouras, K., Bartocci, E., Smolka, S. A., & Grosu, R. (2019). Quantitative Regular Expressions for Arrhythmia Detection. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 16(5), 1586–1597. https://doi.org/10.1109/tcbb.2018.2885274
  • A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems / Ratasich, D., Khalid, F., Geissler, F., Grosu, R., Shafique, M., & Bartocci, E. (2019). A Roadmap Toward the Resilient Internet of Things for Cyber-Physical Systems. IEEE Access, 7, 13260–13283. https://doi.org/10.1109/access.2019.2891969
  • Parallel reachability analysis of hybrid systems in XSpeed / Gurung, A., Ray, R., Bartocci, E., Bogomolov, S., & Grosu, R. (2019). Parallel reachability analysis of hybrid systems in XSpeed. International Journal on Software Tools for Technology Transfer, 21(4), 401–423. https://doi.org/10.1007/s10009-018-0485-6
  • Distributed adaptive-neighborhood control for stochastic reachability in multi-agent systems / Lukina, A., Tiwari, A., Smolka, S. A., & Grosu, R. (2019). Distributed adaptive-neighborhood control for stochastic reachability in multi-agent systems. In SAC ’19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing. Association for Computing Machinery. http://hdl.handle.net/20.500.12708/58143
  • Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks / Hasani, R., Amini, A., Lechner, M., Naser, F., Grosu, R., & Rus, D. (2019). Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks. In 2019 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN), Montréal, Québec, Canada, Austria. https://doi.org/10.1109/ijcnn.2019.8851954
  • From Reactive Systems to Cyber-Physical Systems / From Reactive Systems to Cyber-Physical Systems. (2019). In E. Bartocci, R. Cleaveland, R. Grosu, & O. Sokolsky (Eds.), Lecture Notes in Computer Science. Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-030-31514-6

2018

  • Unsupervised Wafermap Patterns Clustering via Variational Autoencoders / Tulala, P., Mahyar, H., Ghalebi, E., & Grosu, R. (2018). Unsupervised Wafermap Patterns Clustering via Variational Autoencoders. In 2018 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN), Montréal, Québec, Canada, Austria. IEEE. https://doi.org/10.1109/ijcnn.2018.8489422
  • Generative Adversarial Networks for Clustering Semiconductor Wafer Maps / Mahyar, H., Tulala, P., Rabiee, H. R., & Grosu, R. (2018). Generative Adversarial Networks for Clustering Semiconductor Wafer Maps. In Proc. of ML for Systems Workshop. ML for Systems Workshop at NIPS 2018, Montreal, Canada. ML for Systems. http://hdl.handle.net/20.500.12708/57650
  • Self-healing by property-guided structural adaptation / Ratasich, D., Preindl, T., Selyunin, K., & Grosu, R. (2018). Self-healing by property-guided structural adaptation. In 2018 IEEE Industrial Cyber-Physical Systems (ICPS). 1st IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2018), St. Petersburg, Russian Federation (the). IEEE. https://doi.org/10.1109/icphys.2018.8387659
  • Tight Continuous-Time Reachtubes for Lagrangian Reachability / Cyranka, J., Islam, Md. A., Smolka, S. A., Gao, S., & Grosu, R. (2018). Tight Continuous-Time Reachtubes for Lagrangian Reachability. In 2018 IEEE Conference on Decision and Control (CDC). 57th IEEE Conference on Decision and Control, Miami Beach, United States of America (the). IEEE. https://doi.org/10.1109/cdc.2018.8619647
  • Production Tests Coverage Analysis in the Simulation Environment / Manjunath, N., Haerle, D., Manthey, C., Väänänen, M., Sabanal, S., Eichinger, H., Tauber, H., Machne, A., Grosu, R., & Nickovic, D. (2018). Production Tests Coverage Analysis in the Simulation Environment. In 2018 IEEE International Test Conference (ITC). International Test Conference, Phoenix, United States of America (the). IEEE. https://doi.org/10.1109/test.2018.8624870
  • Neural State Classification for Hybrid Systems / Phan, D., Paoletti, N., Zhang, T., Grosu, R., Smolka, S. A., & Stoller, S. D. (2018). Neural State Classification for Hybrid Systems. In Automated Technology for Verification and Analysis (pp. 422–440). Springer. https://doi.org/10.1007/978-3-030-01090-4_25
  • Dynamic Network Model from Partial Observations / Ghalebi, E., Mirzasoleiman, B., Grosu, R., & Leskovec, J. (2018). Dynamic Network Model from Partial Observations. In Advances in Neural Information Processing Systems 31 (NIPS 2018). Neural Information Processing Systems (NIPS 2018), Montreal, Canada. Advances in Neural Information Processing Systems 31. http://hdl.handle.net/20.500.12708/57653
  • A multi-bias recurrent neural network for modeling milling sensory data / Wang, G., Ben Sassi, M. A., & Grosu, R. (2018). A multi-bias recurrent neural network for modeling milling sensory data. In 2018 IEEE Industrial Cyber-Physical Systems (ICPS). 1st IEEE International Conference on Industrial Cyber-Physical Systems (ICPS 2018), St. Petersburg, Russian Federation (the). IEEE. https://doi.org/10.1109/icphys.2018.8387640
  • Resilient Control and Safety for Cyber-Physical Systems / Lukina, A., Tiwari, A., Smolka, S. A., Esterle, L., Yang, J., & Grosu, R. (2018). Resilient Control and Safety for Cyber-Physical Systems. In 2018 IEEE Workshop on Monitoring and Testing of Cyber-Physical Systems (MT-CPS). 3rd Workshop on Monitoring and Testing of Cyber-Physical Systems, Porto, Portugal. IEEE. https://doi.org/10.1109/mt-cps.2018.00015
  • Declarative vs rule-based control for flocking dynamics / Mehmood, U., Paoletti, N., Phan, D., Grosu, R., Lin, S., Stoller, S. D., Tiwari, A., Yang, J., & Smolka, S. A. (2018). Declarative vs rule-based control for flocking dynamics. In Proceedings of the 33rd Annual ACM Symposium on Applied Computing. 33rd ACM Symposium On Applied Computing, Pau, France. ACM. https://doi.org/10.1145/3167132.3167222
  • Interpretable Neuronal Circuit Policies for Reinforcement Learning Environments / Lechner, M., Hasani, R., & Grosu, R. (2018). Interpretable Neuronal Circuit Policies for Reinforcement Learning Environments. In Proceedings of the 2nd Workshop on Explainable Artificial Intelligence (pp. 79–84). IJCAI-ECAI 2018. http://hdl.handle.net/20.500.12708/57634
  • A Compressive Sensing Framework for Distributed Detection of High Closeness Centrality Nodes in Networks / Mahyar, H., Hasheminezhad, R., Ghalebi, E., Grosu, R., & Stanley, H. E. (2018). A Compressive Sensing Framework for Distributed Detection of High Closeness Centrality Nodes in Networks. In Studies in Computational Intelligence (pp. 91–103). Springer. https://doi.org/10.1007/978-3-030-05414-4_8
  • Towards an Agricultural IoT-Infrastructure for Micro-climate Measurements / Hirsch, C., Redl, M., & Grosu, R. (2018). Towards an Agricultural IoT-Infrastructure for Micro-climate Measurements. Workshop on Smart Farming, Porto, Portugal. http://hdl.handle.net/20.500.12708/86817
  • Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks / Hasani, R., Amini, A., Lechner, M., Naser, F., Grosu, R., & Rus, D. (2018). Response Characterization for Auditing Cell Dynamics in Long Short-term Memory Networks. In Proceedings of the NIPS 2018 Interpretability and Robustness for Audio, Speech and Language Workshop. Workshop on Interpretability and Robustness in Audio, Speech, and Language (IRASL) at NIPS 2018, Montreal, Canada. NIPS 2018. http://hdl.handle.net/20.500.12708/57635
  • c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans / Gleeson, P., Lung, D., Grosu, R., Hasani, R., & Larson, S. D. (2018). c302: a multiscale framework for modelling the nervous system of Caenorhabditis elegans. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1758), 20170379. https://doi.org/10.1098/rstb.2017.0379
  • A Mixed-Criticality Integration in Cyber-Physical Systems / Isakovic, H., & Grosu, R. (2018). A Mixed-Criticality Integration in Cyber-Physical Systems. In Advances in Systems Analysis, Software Engineering, and High Performance Computing (pp. 169–194). IGI Global. https://doi.org/10.4018/978-1-5225-2845-6.ch007
  • Identifying central nodes for information flow in social networks using compressive sensing / Mahyar, H., Hasheminezhad, R., Ghalebi, E., Nazemian, A., Grosu, R., Movaghar, A., & Rabiee, H. R. (2018). Identifying central nodes for information flow in social networks using compressive sensing. Social Network Analysis and Mining, 8(33). https://doi.org/10.1007/s13278-018-0506-1
  • An algebraic framework for runtime verification / Jaksic, S., Bartocci, E., Grosu, R., & Nickovic, D. (2018). An algebraic framework for runtime verification. IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 37(11), 2233–2243. https://doi.org/10.1109/tcad.2018.2858460
  • Quantitative monitoring of STL with edit distance / Jakšić, S., Bartocci, E., Grosu, R., Nguyen, T., & Ničković, D. (2018). Quantitative monitoring of STL with edit distance. Formal Methods in System Design, 53(1), 83–112. https://doi.org/10.1007/s10703-018-0319-x
  • Artificial Intelligence Solutions for Verification of Analog and Mixed-Signal Smart Power Systems / Hasani, R., Kulnik, B., Haerle, D., & Grosu, R. (2018). Artificial Intelligence Solutions for Verification of Analog and Mixed-Signal Smart Power Systems. In Proceedings of the 9th International Workshop on Frontiers in Analog CAD. 9th International Workshop on Frontiers in Analog CAD at ASYNC 2018, Vienna, Austria, Austria. http://hdl.handle.net/20.500.12708/57636
  • OpenUAV: A UAV Testbed for the CPS and Robotics Community / Schmittle, M., Lukina, A., Vacek, L., Das, J., Buskirk, C. P., Rees, S., Sztipanovits, J., Grosu, R., & Kumar, V. (2018). OpenUAV: A UAV Testbed for the CPS and Robotics Community. In 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS). IEEE Computer Society. https://doi.org/10.1109/iccps.2018.00021
  • Formation Control and Persistent Monitoring in the OpenUAV Swarm Simulator on the NSF CPS-VO / Lukina, A., Kumar, A., Schmittle, M., Singh, A., Das, J., Rees, S., Buskirk, C. P., Sztipanovits, J., Grosu, R., & Kumar, V. (2018). Formation Control and Persistent Monitoring in the OpenUAV Swarm Simulator on the NSF CPS-VO. In 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS). IEEE Computer Society. https://doi.org/10.1109/iccps.2018.00050

2017

  • Computing compositional proofs of Input-to-Output Stability using SOS optimization and δ-decidability / Murthy, A., Islam, Md. A., Smolka, S. A., & Grosu, R. (2017). Computing compositional proofs of Input-to-Output Stability using SOS optimization and δ-decidability. Nonlinear Analysis: Hybrid Systems, 23, 272–286. https://doi.org/10.1016/j.nahs.2016.03.008
  • Modeling a Simple Non-Associative Learning Mechanism in the Brain of Caenorhabditis elegans / Hasani, R., Fuchs, M., Beneder, V., & Grosu, R. (2017). Modeling a Simple Non-Associative Learning Mechanism in the Brain of Caenorhabditis elegans. In Proceedings of the Workshop on Biomedical Informatics with Optimization and Machine Learning (BOOM), 2017 (p. 5). http://hdl.handle.net/20.500.12708/57238
  • SIM-CE: An Advanced Simulation Platform for Studying the brain of Caenorhabditis elegans / Hasani, R., Beneder, V., Fuchs, M., Lung, D., & Grosu, R. (2017). SIM-CE: An Advanced Simulation Platform for Studying the brain of Caenorhabditis elegans. In Proceedings of the Workshop on Computational Biology at the 34th International Conference on Machine Learning(ICML), 2017 (p. 5). http://hdl.handle.net/20.500.12708/57237
  • Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording / Fuchs, M., Zimmer, M., Grosu, R., & Hasani, R. (2017). Searching for Biophysically Realistic Parameters for Dynamic Neuron Models by Genetic Algorithms from Calcium Imaging Recording. In Proceedings of the Workshop on Worm´s Neural Information Processing at the 31st Neural Information Processing Systems (NIPS) Conference, 2017 (p. 6). http://hdl.handle.net/20.500.12708/57236
  • A Simplified Cell Network for the Simulation of C. elegans' Forward Crawling / Lung, D., Larson, S., Palyanov, A., Khayrulin, S., Gleeson, P., Zimmer, M., Grosu, R., & Hasani, R. (2017). A Simplified Cell Network for the Simulation of C. elegans’ Forward Crawling. In Proceedings of the Workshop on Worm´s Neural Information Processing at the 31st Neural Information Processing Systems (NIPS) Conference, 2017 (p. 5). http://hdl.handle.net/20.500.12708/57235
  • Worm-level Control through Search-based Reinforcement Learning / Lechner, M., Grosu, R., & Hasani, R. (2017). Worm-level Control through Search-based Reinforcement Learning. In Proceedings of the Deep Reinforcement Learning Symposium at the 31st Neural Information Processing Systems (NIPS) Conference, 2017 (p. 5). http://hdl.handle.net/20.500.12708/57234
  • A novel Bayesian network-based fault prognostic method for semiconductor manufacturing process / Wang, G., Hasani, R., Yungang, Z., & Grosu, R. (2017). A novel Bayesian network-based fault prognostic method for semiconductor manufacturing process. In 2017 IEEE International Conference on Industrial Technology (ICIT). 2017 Annual IEEE Industrial Electronics Society´s 18th International Conference on Industrial Technology (ICIT 2017), Toronto, ON, Canada. IEEE. https://doi.org/10.1109/icit.2017.7915579
  • Attacking the V: On the Resiliency of Adaptive-Horizon MPC / Tiwari, A., Smolka, S. A., Esterle, L., Lukina, A., Yang, J., & Grosu, R. (2017). Attacking the V: On the Resiliency of Adaptive-Horizon MPC. In Automated Technology for Verification and Analysis (pp. 446–462). Springer International Publishing. https://doi.org/10.1007/978-3-319-68167-2_29
  • Quantitative Regular Expressions for Arrhythmia Detection Algorithms / Abbas, H., Rodionova, A., Bartocci, E., Smolka, S. A., & Grosu, R. (2017). Quantitative Regular Expressions for Arrhythmia Detection Algorithms. In Computational Methods in Systems Biology (pp. 23–39). Springer. https://doi.org/10.1007/978-3-319-67471-1_2
  • A Self-Healing Framework for Building Resilient Cyber-Physical Systems / Ratasich, D., Höftberger, O., Isakovic, H., Shafique, M., & Grosu, R. (2017). A Self-Healing Framework for Building Resilient Cyber-Physical Systems. In 2017 IEEE 20th International Symposium on Real-Time Distributed Computing (ISORC). 20th IEEE International Symposium on Real-Time Computing (ISORC 2017), Toronto, Canada. IEEE. https://doi.org/10.1109/isorc.2017.7
  • Runtime Monitoring with Recovery of the SENT Communication Protocol / Selyunin, K., Jaksic, S., Nguyen, T., Reidl, C., Hafner, U., Bartocci, E., Nickovic, D., & Grosu, R. (2017). Runtime Monitoring with Recovery of the SENT Communication Protocol. In Computer Aided Verification (pp. 336–355). Springer. https://doi.org/10.1007/978-3-319-63387-9_17
  • ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans / Lukina, A., Esterle, L., Hirsch, C., Bartocci, E., Yang, J., Tiwari, A., Smolka, S. A., & Grosu, R. (2017). ARES: Adaptive Receding-Horizon Synthesis of Optimal Plans. In A. Legay & T. Margaria (Eds.), Tools and Algorithms for the Construction and Analysis of Systems (pp. 286–302). Springer. https://doi.org/10.1007/978-3-662-54580-5_17
  • Computing with Biophysical and Hardware-efficient Neural Models / Selyunin, K., Hasani, R., Ratasich, D., Bartocci, E., & Grosu, R. (2017). Computing with Biophysical and Hardware-efficient Neural Models. In I. Rojas, G. Joya, & A. Catala (Eds.), Advances in Computational Intelligence (pp. 535–547). Springer. https://doi.org/10.1007/978-3-319-59153-7_46
  • Towards Deterministic and Stochastic Computations with the Izhikevich Spiking-Neuron Model / Hasani, R. M., Wang, G., & Grosu, R. (2017). Towards Deterministic and Stochastic Computations with the Izhikevich Spiking-Neuron Model. In Advances in Computational Intelligence (pp. 392–402). Springer. https://doi.org/10.1007/978-3-319-59147-6_34
  • A Component-Based Simplex Architecture for High-Assurance Cyber-Physical Systems / Phan, D., Yang, J., Clark, M., Grosu, R., Schierman, J., Smolka, S., & Stoller, S. (2017). A Component-Based Simplex Architecture for High-Assurance Cyber-Physical Systems. In 2017 17th International Conference on Application of Concurrency to System Design (ACSD). Application of Concurrency to System Design (ACSD), 2017 17th International Conference on, Zaragoza, Spain. https://doi.org/10.1109/acsd.2017.23
  • Lagrangian Reachabililty / Cyranka, J., Islam, Md. A., Byrne, G., Jones, P., Smolka, S. A., & Grosu, R. (2017). Lagrangian Reachabililty. In Computer Aided Verification (pp. 379–400). Springer. https://doi.org/10.1007/978-3-319-63387-9_19
  • A Survey of Hardware Technologies for Mixed-Critical Integration Explored in the Project EMC² / Isakovic, H., Grosu, R., Ratasich, D., Kadlec, J., Pohl, Z., Kerrison, S., Georgiou, K., Druml, N., Tadros, L., Christiansen, F., Wheatley, E., Farkas, B., Meyer, R., & Berekovic, M. (2017). A Survey of Hardware Technologies for Mixed-Critical Integration Explored in the Project EMC2. In Computer Safety, Reliability, and Security SAFECOMP 2017 Workshops, ASSURE, DECSoS, SASSUR, TELERISE, and TIPS, Trento, Italy, September 12, 2017, Proceedings (pp. 127–140). Lecture Notes in Computer Science / Springer. https://doi.org/10.1007/978-3-319-66284-8_12
  • HellRank: a Hellinger-based centrality measure for bipartite social networks / Taheri, S. M., Mahyar, H., Firouzi, M., Ghalebi K., E., Grosu, R., & Movaghar, A. (2017). HellRank: a Hellinger-based centrality measure for bipartite social networks. Social Network Analysis and Mining. https://doi.org/10.1007/s13278-017-0440-7
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  • Runtime Safety Assurance for Adaptive Cyber-Physical Systems / Amorim, T., Ratasich, D., Macher, G., Ruiz, A., Schneider, D., Driussi, M., & Grosu, R. (2017). Runtime Safety Assurance for Adaptive Cyber-Physical Systems. In N. Druml, A. Genser, A. Krieg, M. Menghin, & A. Höller (Eds.), Advances in Systems Analysis, Software Engineering, and High Performance Computing (pp. 137–168). IGI Global. https://doi.org/10.4018/978-1-5225-2845-6.ch006
  • A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres / Zhu, Y., Liu, D., Grosu, R., Wang, X., Duan, H., & Wang, G. (2017). A Multi-Sensor Data Fusion Approach for Atrial Hypertrophy Disease Diagnosis Based on Characterized Support Vector Hyperspheres. Sensors, 17(9), 2049. https://doi.org/10.3390/s17092049
  • Gaussian convex evidence theory for ordered and fuzzy evidence fusion / Zhu, Y., Duan, H., Wang, X., Zhou, B., Wang, G., & Grosu, R. (2017). Gaussian convex evidence theory for ordered and fuzzy evidence fusion. Journal of Intelligent & Fuzzy Systems, 33(5), 2843–2849. http://hdl.handle.net/20.500.12708/148019
  • ZIZO: A Novel Zoom-In-Zoom-Out Search Algorithm for the Global Parameters of Echo-State Networks / Wang, G., Ben Sassi, M. A., & Grosu, R. (2017). ZIZO: A Novel Zoom-In-Zoom-Out Search Algorithm for the Global Parameters of Echo-State Networks. Canadian Journal of Electrical and Computer Engineering, 40(3), 210–216. https://doi.org/10.1109/cjece.2017.2703093
  • Collision Avoidance for Mobile Robots with Limited Sensing and Limited Information about Moving Obstacles / Phan, D., Yang, J., Grosu, R., Smolka, S. A., & Stoller, S. D. (2017). Collision Avoidance for Mobile Robots with Limited Sensing and Limited Information about Moving Obstacles. Formal Methods in System Design, 51(1), 62–86. https://doi.org/10.1007/s10703-016-0265-4
  • Compositional neural-network modeling of complex analog circuits / Hasani, R. M., Haerle, D., Baumgartner, C. F., Lomuscio, A. R., & Grosu, R. (2017). Compositional neural-network modeling of complex analog circuits. In 2017 International Joint Conference on Neural Networks (IJCNN). IEEE International Joint Conference on Neural Networks (IJCNN), Montréal, Québec, Canada, Austria. https://doi.org/10.1109/ijcnn.2017.7966126

2016

  • Cyber-physical systems : challenge of the 21st century / Esterle, L., & Grosu, R. (2016). Cyber-physical systems : challenge of the 21st century. Elektrotechnik Und Informationstechnik : E & i, 133(7), 299–303. https://doi.org/10.1007/s00502-016-0426-6
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  • The HARMONIA Project: Hardware Monitoring for Automotive Systems-of-Systems / Nguyen, T., Bartocci, E., Ničković, D., Grosu, R., Jaksic, S., & Selyunin, K. (2016). The HARMONIA Project: Hardware Monitoring for Automotive Systems-of-Systems. In T. Margaria & B. Steffen (Eds.), Leveraging Applications of Formal Methods, Verification and Validation: Discussion, Dissemination, Applications. ISoLA 2016, Proceedings, Part II (pp. 371–379). Springer. https://doi.org/10.1007/978-3-319-47169-3_28
  • Probabilistic reachability analysis of the tap withdrawal circuit in caenorhabditis elegans / Islam, Md. A., Wang, Q., Hasani, R. M., Balun, O., Clarke, E. M., Grosu, R., & Smolka, S. A. (2016). Probabilistic reachability analysis of the tap withdrawal circuit in caenorhabditis elegans. In 2016 IEEE International High Level Design Validation and Test Workshop (HLDVT). 18th IEEE International High-Level Design Validation and Test Workshop (HLDVT) 2016, Santa Cruz, United States of America (the). IEEE. https://doi.org/10.1109/hldvt.2016.7748272
  • A heterogeneous time-triggered architecture on a hybrid system-on-a-chip platform / Isakovic, H., & Grosu, R. (2016). A heterogeneous time-triggered architecture on a hybrid system-on-a-chip platform. In 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE). 2016 IEEE 25th International Symposium on Industrial Electronics (ISIE), Santa Clara, United States of America (the). IEEE. https://doi.org/10.1109/isie.2016.7744897
  • Applying High-Level Synthesis for Synthesizing Hardware Runtime STL Monitors of Mission-Critical Properties / Selyunin, K., Nguyen, T., Basa, A.-D., Bartocci, E., Nickovic, D., & Grosu, R. (2016). Applying High-Level Synthesis for Synthesizing Hardware Runtime STL Monitors of Mission-Critical Properties. In Design and Verification Conference and Exhibition (p. 8). Online. http://hdl.handle.net/20.500.12708/56824
  • Parallel reachability analysis for hybrid systems / Gurung, A., Kumar, D. A., Bartocci, E., Bogomolov, S., Grosu, R., & Ray, R. (2016). Parallel reachability analysis for hybrid systems. In 2016 ACM/IEEE International Conference on Formal Methods and Models for System Design (MEMOCODE). Proc. of MEMOCODE 2016: the 14th ACM-IEEE International Conference on Formal Methods and Models for System Design, ACM, 2016, Kanpur, India. https://doi.org/10.1109/memcod.2016.7797741
  • Efficient modeling of complex Analog integrated circuits using neural networks / Hasani, R. M., Haerle, D., & Grosu, R. (2016). Efficient modeling of complex Analog integrated circuits using neural networks. In 2016 12th Conference on Ph.D. Research in Microelectronics and Electronics (PRIME). 12th Conference on PhD Research in Microelectronics and Electronics (PRIME) 2016, Lissabon, Portugal. IEEE. https://doi.org/10.1109/prime.2016.7519486
  • Discrete Abstraction of Multiaffine Systems / Kong, H., Bartocci, E., Bogomolov, S., Grosu, R., Henzinger, T. A., Jiang, Y., & Schilling, C. (2016). Discrete Abstraction of Multiaffine Systems. In Hybrid Systems Biology (pp. 128–144). Springer International Publishing. https://doi.org/10.1007/978-3-319-47151-8_9
  • Monitoring of MTL Specifications With IBM's Spiking-Neuron Model / Selyunin, K., Nguyen, T., Bartocci, E., Nickovic, D., & Grosu, R. (2016). Monitoring of MTL Specifications With IBM’s Spiking-Neuron Model. In Proc. of the 2016 Design, Automation & Test in Europe Conference & Exhibition (pp. 924–929). IEEE Computer Society. http://hdl.handle.net/20.500.12708/56706
  • Quantitative Monitoring of STL with Edit Distance / Jakšić, S., Bartocci, E., Grosu, R., & Ničković, D. (2016). Quantitative Monitoring of STL with Edit Distance. In Runtime Verification (pp. 201–218). Springer International Publishing. https://doi.org/10.1007/978-3-319-46982-9_13
  • Applying Runtime Monitoring for Automotive Electronic Development / Selyunin, K., Nguyen, T., Bartocci, E., & Grosu, R. (2016). Applying Runtime Monitoring for Automotive Electronic Development. In Runtime Verification (pp. 462–469). Springer International Publishing. https://doi.org/10.1007/978-3-319-46982-9_30
  • Milling-Tool Wear-Condition Prediction with Statistic Analysis and Echo-State Networks / Wang, G., & Grosu, R. (2016). Milling-Tool Wear-Condition Prediction with Statistic Analysis and Echo-State Networks. In Proceedings of S2M’16, the International Conference on Sustaniable Smart Manufacturing. S2M’16: the International Conference on Sustaniable Smart Manufacturing, Lisbon, Portugal. Taylor & Francis. http://hdl.handle.net/20.500.12708/56839
  • Bifurcation Analysis of Cardiac Alternans Using $$\delta $$ -Decidability / Islam, Md. A., Byrne, G., Kong, S., Clarke, E. M., Cleaveland, R., Fenton, F. H., Grosu, R., Jones, P. L., & Smolka, S. A. (2016). Bifurcation Analysis of Cardiac Alternans Using $$\delta $$ -Decidability. In Computational Methods in Systems Biology (pp. 132–146). LNCS, Springer. https://doi.org/10.1007/978-3-319-45177-0_9
  • A simulation framework for IEEE 1588 / Wallner, W., Wasicek, A., & Grosu, R. (2016). A simulation framework for IEEE 1588. In 2016 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication (ISPCS). 2016 IEEE International Symposium on Precision Clock Synchronization for Measurement, Control, and Communication, Stockholm, Sweden. IEEE. https://doi.org/10.1109/ispcs.2016.7579516
  • Feedback Control for Statistical Model Checking of Cyber-Physical Systems / Kalajdzic, K., Jegourel, C., Lukina, A., Bartocci, E., Legay, A., Smolka, S. A., & Grosu, R. (2016). Feedback Control for Statistical Model Checking of Cyber-Physical Systems. In T. Margaria & B. Steffen (Eds.), Leveraging Applications of Formal Methods, Verification and Validation: Foundational Techniques (ISoLA 2016), Proceedings, Part I (pp. 46–61). Springer. https://doi.org/10.1007/978-3-319-47166-2_4
  • Temporal Logic as Filtering / Rodionova, A., Bartocci, E., Nickovic, D., & Grosu, R. (2016). Temporal Logic as Filtering. In Proceedings of the 19th International Conference on Hybrid Systems: Computation and Control. Proceeding HSCC ’16 - the 19th International Conference on Hybrid Systems: Computation and Control, Vienna, Austria. ACM. https://doi.org/10.1145/2883817.2883839
  • Investigations on the Nervous System of Caenorhabditis elegans / M. Hasani, R., Esterle, L., & Grosu, R. (2016). Investigations on the Nervous System of Caenorhabditis elegans. Current AI Research in Austria (CAIRA) Workshop at the 39th German conference on Artificial Intelligence, Klagenfurt, Austria. http://hdl.handle.net/20.500.12708/86397

2015

  • XSpeed: Accelerating Reachability Analysis on Multi-core Processors / Rajarshi, R., Amit, G., Binayak, D., Bartocci, E., Bogomolov, S., & Grosu, R. (2015). XSpeed: Accelerating Reachability Analysis on Multi-core Processors. In N. Piterman (Ed.), Hardware and Software: Verification and Testing - 11th International Haifa Verification Conference, HVC 2015, Haifa, Israel, November 17-19, 2015, Proceedings (pp. 3–18). LNCS / Springer. https://doi.org/10.1007/978-3-319-26287-1_1
  • Neural Programming: Towards adaptive control in Cyber-Physical Systems / Selyunin, K., Ratasich, D., Bartocci, E., Islam, M. A., Smolka, S. A., & Grosu, R. (2015). Neural Programming: Towards adaptive control in Cyber-Physical Systems. In 2015 54th IEEE Conference on Decision and Control (CDC). 54th IEEE Conference on Decision and Control, Osaka, Japan. IEEE Computer Society. https://doi.org/10.1109/cdc.2015.7403319
  • From signal temporal logic to FPGA monitors / Jaksic, S., Bartocci, E., Grosu, R., Kloibhofer, R., Nguyen, T., & Nickovic, D. (2015). From signal temporal logic to FPGA monitors. In 2015 ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMOCODE). 13th ACM-IEEE International Conference on Formal Methods and Models for System Design, Austin, United States of America (the). IEEE. https://doi.org/10.1109/memcod.2015.7340489
  • Abstraction-Based Parameter Synthesis for Multiaffine Systems / Bogomolov, S., Schilling, C., Bartocci, E., Batt, G., Kong, H., & Grosu, R. (2015). Abstraction-Based Parameter Synthesis for Multiaffine Systems. In Hardware and Software: Verification and Testing (pp. 19–35). LNCS / Springer. https://doi.org/10.1007/978-3-319-26287-1_2
  • SpaTeL / Haghighi, I., Jones, A., Kong, Z., Bartocci, E., Gros, R., & Belta, C. (2015). SpaTeL. In Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control. 18th International Conference on Hybrid Systems: Computation and Control (HSCC), Seattle, United States of America (the). ACM. https://doi.org/10.1145/2728606.2728633
  • Generic sensor fusion package for ROS / Ratasich, D., Frömel, B., Höftberger, O., & Grosu, R. (2015). Generic sensor fusion package for ROS. In 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Hamburg, Germany. IEEE. https://doi.org/10.1109/iros.2015.7353387
  • Model-Order Reduction of Ion Channel Dynamics Using Approximate Bisimulation / Ariful Islam, Md., Murthy, A., Bartocci, E., Cherry, E. M., Fenton, F. H., Glimm, J., Smolka, S. A., & Grosu, R. (2015). Model-Order Reduction of Ion Channel Dynamics Using Approximate Bisimulation. Theoretical Computer Science, 599, 34–46. https://doi.org/10.1016/j.tcs.2014.03.018
  • Collision Avoidance for Mobile Robots with Limited Sensing and Limited Information About the Environment / Phan, D., Yang, J., Ratasich, D., Grosu, R., Smolka, S. A., & Stoller, S. D. (2015). Collision Avoidance for Mobile Robots with Limited Sensing and Limited Information About the Environment. In Runtime Verification (pp. 201–215). Springer. https://doi.org/10.1007/978-3-319-23820-3_13
  • Computing bisimulation functions using SOS optimization and <i>δ</i> -decidability over the reals / Murthy, A., Islam, Md. A., Smolka, S. A., & Grosu, R. (2015). Computing bisimulation functions using SOS optimization and            δ            -decidability over the reals. In Proceedings of the 18th International Conference on Hybrid Systems: Computation and Control. 18th International Conference on Hybrid Systems: Computation and Control (HSCC), Seattle, USA, Non-EU. ACM. https://doi.org/10.1145/2728606.2728609

2014

  • Using Statistical Model Checking for Measuring Systems / Grosu, R., Peled, D., Ramakrishnan, C. R., Smolka, S. A., Stoller, S. D., & Yang, J. (2014). Using Statistical Model Checking for Measuring Systems. In Leveraging Applications of Formal Methods, Verification and Validation. Specialized Techniques and Applications. ISoLA 2014, Proceedings, Part II (pp. 223–238). Springer. https://doi.org/10.1007/978-3-662-45231-8_16
  • Cyber-Physical Systems: Theoretical and Practical Challenges / Bartocci, E., Höftberger, O., & Grosu, R. (2014). Cyber-Physical Systems: Theoretical and Practical Challenges. ERCIM NEWS, 2014(97), 8–9. http://hdl.handle.net/20.500.12708/157429
  • Tracking Action Potentials of Nonlinear Excitable Cells using Model Predictive Control / Ariful, I., Deshpande, T., Murthy, A., Bartocci, E., Smolka, S. A., Stoller, S. D., & Grosu, R. (2014). Tracking Action Potentials of Nonlinear Excitable Cells using Model Predictive Control. In Proc. of BIOTECHNO 2014: The Sixth International Conference on Bioinformatics, Biocomputational Systems and Biotechnologies (pp. 52–58). IARIA. http://hdl.handle.net/20.500.12708/55802
  • Assume-Guarantee Abstraction-Refinement Meets Hybrid Systems / Grosu, R., Bogomolov, S., Frehse, G., Greitschus, M., Pasareanu, C., Podelski, A., & Strump, T. (2014). Assume-Guarantee Abstraction-Refinement Meets Hybrid Systems. In Proc. of HVC’14, the Haifa Verification Conference. Haifa Verification Conference HVC 2014, Haifa, Isral, Non-EU. http://hdl.handle.net/20.500.12708/55828
  • Compositionality Results for Cardiac Cell Dynamics / Grosu, R., Islam, A., Murthy, A., Girard, A., & Smolka, S. A. (2014). Compositionality Results for Cardiac Cell Dynamics. In Proc. of HSCC’14, the 17th International Conference on Hybrid Systems: Computation and Control (pp. 243–252). http://hdl.handle.net/20.500.12708/55826

2013

  • Curvature Analysis of Cardiac Excitation Wavefronts / Murthy, A., Bartocci, E., Fenton, F. H., Glimm, J., Gray, R. A., Cherry, E. M., Smolka, S. A., & Grosu, R. (2013). Curvature Analysis of Cardiac Excitation Wavefronts. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, 10(2), 323–336. https://doi.org/10.1109/tcbb.2012.125
  • Monitoring with uncertainty / Bartocci, E., & Grosu, R. (2013). Monitoring with uncertainty. In Electronic Proceedings in Theoretical Computer Science (pp. 1–4). Electronic Proceedings in Theoretical Computer Science. https://doi.org/10.4204/eptcs.124.1
  • Abstraction-Based Guided Search for Hybrid Systems / Bogomolov, S., Donzé, A., Frehse, G., Grosu, R., Johnson, T. T., Ladan, H., Podelski, A., & Wehrle, M. (2013). Abstraction-Based Guided Search for Hybrid Systems. In Model Checking Software (pp. 117–134). LNCS, Springer. https://doi.org/10.1007/978-3-642-39176-7_8
  • Runtime Verification with Particle Filtering / Kalajdzic, K., Bartocci, E., Stoller, S. D., Smolka, S. A., & Grosu, R. (2013). Runtime Verification with Particle Filtering. In Runtime Verification (pp. 149–166). LNCS/Springer. https://doi.org/10.1007/978-3-642-40787-1_9

2012

  • InterAspect: aspect-oriented instrumentation with GCC / Seyster, J., Dixit, K., Huang, X., Grosu, R., Havelund, K., Smolka, S. A., Stoller, S. D., & Zadok, E. (2012). InterAspect: aspect-oriented instrumentation with GCC. Formal Methods in System Design, 41(3), 295–320. https://doi.org/10.1007/s10703-012-0171-3
  • On Temporal Logic and Signal Processing / Donzé, A., Maler, O., Bartocci, E., Nickovic, D., Grosu, R., & Smolka, S. (2012). On Temporal Logic and Signal Processing. In Automated Technology for Verification and Analysis (pp. 92–106). LNCS/Springer. https://doi.org/10.1007/978-3-642-33386-6_9
  • Software monitoring with controllable overhead / Huang, X., Seyster, J., Callanan, S., Dixit, K., Grosu, R., Smolka, S. A., Stoller, S. D., & Zadok, E. (2012). Software monitoring with controllable overhead. International Journal on Software Tools for Technology Transfer, 14(3), 327–347. https://doi.org/10.1007/s10009-010-0184-4
  • A Box-Based Distance between Regions for Guiding the Reachability Analysis of SpaceEx / Bogomolov, S., Frehse, G., Grosu, R., Ladan, H., & Podelski, A. (2012). A Box-Based Distance between Regions for Guiding the Reachability Analysis of SpaceEx. In Computer Aided Verification (pp. 479–494). LNCS / Springer. https://doi.org/10.1007/978-3-642-31424-7_35
  • Approximate Bisimulations for Sodium Channel Dynamics / Murthy, A., Ariful, I., Bartocci, E., Cherry, E., Fenton, F. H., Glimm, J., Smolka, S. A., & Grosu, R. (2012). Approximate Bisimulations for Sodium Channel Dynamics. In Computational Methods in Systems Biology (pp. 267–287). LNCS / Springer. https://doi.org/10.1007/978-3-642-33636-2_16
  • Adaptive Runtime Verification / Bartocci, E., Grosu, R., Karmarkar, A., Smolka, S. A., Stoller, S. D., & Seyster, J. (2012). Adaptive Runtime Verification. In Runtime Verification (pp. 168–182). LNCS / Springer. https://doi.org/10.1007/978-3-642-35632-2_18
  • Runtime Verification with State Estimation / Stoller, S. D., Bartocci, E., Seyster, J., Grosu, R., Havelund, K., Smolka, S. A., & Zadok, E. (2012). Runtime Verification with State Estimation. In Runtime Verification (pp. 193–207). LNCS / Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-29860-8_15

2011

  • Model Repair for Probabilistic Systems / Bartocci, E., Grosu, R., Katsaros, P., Ramakrishnan, C. R., & Smolka, S. A. (2011). Model Repair for Probabilistic Systems. In Tools and Algorithms for the Construction and Analysis of Systems (pp. 326–340). LNCS / Springer. https://doi.org/10.1007/978-3-642-19835-9_30
  • Curvature analysis of cardiac excitation wavefronts / Murthy, A., Bartocci, E., Fenton, F. H., Glimm, J., Gray, R., Smolka, S. A., & Grosu, R. (2011). Curvature analysis of cardiac excitation wavefronts. In Proceedings of the 9th International Conference on Computational Methods in Systems Biology - CMSB ’11. CMSB 2011: the 9th ACM International Conference on Computational Methods in Systems Biology, Paris, France, EU. ACM. https://doi.org/10.1145/2037509.2037532
  • Toward real-time simulation of cardiac dynamics / Bartocci, E., Cherry, E., Glimm, J., Grosu, R., & Smolka, S. A. (2011). Toward real-time simulation of cardiac dynamics. In Proceedings of the 9th International Conference on Computational Methods in Systems Biology - CMSB ’11. CMSB 2011: the 9th ACM International Conference on Computational Methods in Systems Biology, Paris, France, EU. ACM. https://doi.org/10.1145/2037509.2037525
  • From Cardiac Cells to Genetic Regulatory Networks / Grosu, R., Batt, G., Fenton, F. H., Glimm, J., Le Guernic, C., Smolka, S. A., & Bartocci, E. (2011). From Cardiac Cells to Genetic Regulatory Networks. In Computer Aided Verification (pp. 396–411). LNCS / Springer. https://doi.org/10.1007/978-3-642-22110-1_31

 

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  • Towards dependable CPS/IoT ecosystems / Isakovic, H. (2022). Towards dependable CPS/IoT ecosystems [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2022.103104
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