Summer 2026

• Autonomous Racing Cars / 191.119 VU
• Bachelor Thesis for Computer Science and Business Informatics / 182.715 PR
• Computer Engineering Practical / 191.005 PR
• Computer Engineering Project / 191.006 PR
• PhD Seminar / 191.013 SE
• Project in Computer Science 1 / 191.008 PR
• Project in Computer Science 2 / 191.009 PR
• Scientific Project Computer Engineering / 191.007 PR
• Seminar Computer Engineering / 182.760 SE
• Seminar for Master Students in Computer Engineering / 180.778 SE
• Seminar for Master Students in Software Engineering (Computer Engineering) / 180.011 SE

Winter 2025

• Bachelor Thesis for Computer Science and Business Informatics / 182.715 PR
• Computer Engineering Practical / 191.005 PR
• Computer Engineering Project / 191.006 PR
• Project in Computer Science 1 / 191.008 PR
• Scientific Project Computer Engineering / 191.007 PR
• Seminar Computer Engineering / 182.760 SE
• Seminar for Master Students in Computer Engineering / 180.778 SE
• Seminar for PhD students / 182.008 SE
• Stochastic Foundations of Cyber-Physical Systems / 182.763 VU

• MATTO-GBM: Multimodality Artificial intelligence open-source Tools for Radiation Treatment Optimization in patients with Glioblastoma
  2024 – 2027 / Austrian Science Fund (FWF) / Publications (4)
• TA-CPS: Trustworthy Autonomous Cyber-Physical Systems
  2023 – 2028 / TTTech Auto AG / Publications (3)
• EdgeAI: EdgeAI: Technologies for Optimized Performance - Embedded Processing
  2022 – 2026 / Austrian Research Promotion Agency (FFG) / Publication
• NimbleAI: ULTRA-ENERGY EFFICIENT AND SECURE NEUROMORPHIC SENSING AND PROCESSING AT THE ENDPOINT
  2022 – 2026 / European Commission / Publications (2)
• ADEX: Autonomous-Driving Examiner
  2020 – 2024 / Austrian Research Promotion Agency (FFG) / Publications (4)
• ADEPTNESS: Automated Design-Operation Continuum Methods for Cyber-Physical Systems of Systems
  2020 – 2023 / European Commission / Publication
• PersoRad: Personalised Medicine: Multidisciplinary Research Towards IM
  2020 – 2023 / Austrian Science Fund (FWF) / Publication
• Agri-Tec 4.0: Development of a cloud-based prototype for smart / indoor farming applications
  2020 – 2021 / Fachhochschule Burgenland GmbH
• RoboticArm: Advanced Sensor and Data Analysis Application for Robotic Arm Systems
  2019 – 2021 / mechatronic systemtechnik gmbh
• AFarCloud: Aggregate Farming in the Cloud
  2018 – 2021 / Austrian Research Promotion Agency (FFG) / Publication
• iDev40: Integrated Development 4.0
  2018 – 2021 / European Commission
• COCO: COCO: Control Equivalence for Cyber-Physical Models
  2018 – 2020 / Austrian Science Fund (FWF) / Publication
• SIC: Self-stabilizing Byzantine Fault-Tolerant Distributed Algorithms for Integrated Circiuts
  2013 – 2018 / Austrian Science Fund (FWF) / Publication
• AMADEOS: Architecture for Multi-criticality Agile Dependable Evolutionary Open System-of-Systems
  2013 – 2016 / European Commission
• MODESEC: Model-based Design of Secure Cyber-Physical Systems
  2013 – 2016 / European Commission

2026

• Clement, M.-L., Farsang, M., Stanusoiu, M.-T., Rus, D., Hasani, R., Grosu, R., Bartocci, E. (2026). Evaluating Domain-Shift Generalization of Liquid Neural Networks in Autonomous Driving. In Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop. Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop, Rio de Janeiro, Brazil. Peer-reviewed.
  HDL: 20.500.12708/227937 / Project: DK - AR
• Lemmel, J., Resch, F., Farsang, M., Hasani, R., Rus, D., Grosu, R. (2026). Online Fine-Tuning of Pretrained Controllers for Autonomous Driving via Real-Time Recurrent RL. In Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop. Catch, Adapt, and Operate: Monitoring ML Models Under Drift Workshop, Rio de Janeiro, Brazil. Peer-reviewed.
  HDL: 20.500.12708/227938
• Lemmel, J., Kranzl, M., Lamine, A., Neubauer, P., Grosu, R., Neubauer, S. (2026). Online Fine-Tuning of Carbon Emission Predictions using Real-Time Recurrent Learning for State Space Models. In 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 6953–6958). IEEE. Peer-reviewed.
  DOI: 10.34726/12240 / Download: PDF
• Bolelli, F., Luca Lumetti, van Nistelrooij, N., Vinayahalingam, S., Di Bartolomeo, M., Marchesini, K., Pellacani, A., Candeloro, E., Rosati, G., Xi, T., Isensee, F., Kirchhoff, Y., Krämer, L., Rokuss, M., Ulrich, C., Maier-Hein, K., Jiang, Y., Liu, Y., Wang, L., … Costantino Grana. (2026). Multi-structure segmentation in CBCT volumes: The ToothFairy2 challenge. Medical Image Analysis, 112, Article 104095. Peer-reviewed.
  DOI: 10.1016/j.media.2026.104095 / Project: MATTO-GBM
• Bolelli, F., LUMETTI, L., van Nistelrooij, N., Vinayahalingam, S., Di Bartolomeo, M., Marchesini, K., Pellacani, A., Candeloro, E., Rosati, G., Xi, T., Isensee, F., Kirchhoff, Y., Krämer, L., Rokuss, M., Ulrich, C., Maier-Hein, K., Jiang, Y., Liu, Y., Wang, L., … Grana, C. (2026). Multi-structure segmentation in CBCT volumes: The ToothFairy2 challenge. Medical Image Analysis, 112, Article 104095. Peer-reviewed.
  DOI: 10.1016/j.media.2026.104095
• Grosu, R. (2026). ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks. In Engineering Safe and Trustworthy Cyber Physical Systems : Essays Dedicated to Werner Damm on the Occasion of His 71st Birthday (Vol. 15471, pp. 288–297). Springer. Peer-reviewed.
  DOI: 10.1007/978-3-031-97537-0_17

2025

• Brunnbauer, A., Lemmel, J., Babaiee, Z., Neubauer, S., Grosu, R. (2025). Scalable Offline Reinforcement Learning for Mean Field Games. In S. Das, A. Nowé, Y. Vorobeychik (Eds.), Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems, {AAMAS} 2025, Detroit, MI, USA, May 19-23, 2025 (pp. 408–417). International Foundation for Autonomous Agents and Multiagent Systems. Peer-reviewed.
  DOI: 10.5555/3709347.3743555
• 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. Peer-reviewed.
  DOI: 10.1609/aaai.v39i17.34001
• Babaiee, Z., Mohseni Kiasari, P., Rus, D., Grosu, R. (2025). The Master Key Filters Hypothesis: Deep Filters Are General. In T. Walsh, J. Shah, Z. Kolter (Eds.), Proceedings of the 39th Annual AAAI Conference on Artificial Intelligence (pp. 1809–1816). AAAI Press. Peer-reviewed.
  DOI: 10.1609/aaai.v39i2.32175 / Projects: TA-CPS, MATTO-GBM
• Berducci, L., Aguilar, E. A., Ničković, D., Grosu, R. (2025). HPRS: hierarchical potential-based reward shaping from task specifications. Frontiers in Robotics and AI, 11, 1–23. Peer-reviewed.
  DOI: 10.3389/frobt.2024.1444188 / Project: ADEX
• Li, P., Wu, X., Grosu, R., Hou, J., Ilolov, M., Xiang, S. (2025). Applying Neural Network to Health Estimation and Lifetime Prediction of Lithium-Ion Batteries. IEEE Transactions on Transportation Electrification, 11(1), 4224–4248. Peer-reviewed.
  DOI: 10.1109/TTE.2024.3457621
• Valeh, F., Schütz, G. J., Grosu, R. (2025). Improving the Resolution of Single-Molecule Localization Microscopy by Leveraging Spatiotemporal Information. In International Conference on Engineering for Life Sciences : ENROL 2025 : Book of Abstracts (pp. 32–32). Peer-reviewed.
  DOI: 10.34726/9799
• Lygizou, E. M., Reiter, M., Maurer-Granofszky, M., Dworzak, M., Grosu, R. (2025). Deep Learning for Automating the Immunophenotyping Assessment in Childhood Acute Leukemia Diagnosis. In International Conference on Engineering for Life Sciences : ENROL 2025 : Book of Abstracts (pp. 17–17). Peer-reviewed.
  HDL: 20.500.12708/222457
• Farsang, M., Grosu, R. (2025). Liquid Capacitance-Extended Neural Circuits: Synaptic Activation and Dual Liquid Dynamics for Interpretable Bio-Inspired Models. In International Conference on Engineering for Life Sciences : ENROL 2025 : Book of Abstracts (pp. 15–15). Peer-reviewed.
  HDL: 20.500.12708/222456
• Babaiee, Z., Mohseni Kiasari, P., Daniela L Rus, Grosu, R. (2025). The Quest for Universal Master Key Filters in DS-CNNs. In Advances in Neural Information Processing Systems 39: Annual Conference on Neural Information Processing Systems 2025, NeurIPS 2025, Mexico City, MX, November 30 - December 5, 2025. NeurIPS 2025, United States of America (the). Peer-reviewed.
  HDL: 20.500.12708/222788
• Farsang, M., Grosu, R. (2025). Scaling Up Liquid-Resistance Liquid-Capacitance Networks for Efficient Sequence Modeling. In Advances in Neural Information Processing Systems 39: Annual Conference on Neural Information Processing Systems 2025, NeurIPS 2025, San Diego, US, December 2 - 7, 2025. NeurIPS 2025, United States of America (the). Peer-reviewed.
  HDL: 20.500.12708/223745
• Brunnbauer, A., Berducci, L., Priller, P., Ničković, D., Grosu, R. (2025). Scenario-Based Curriculum Generation for Multi-Agent Driving. In 2025 IEEE International Conference on Robotics and Automation (ICRA) (pp. 1824–1830). IEEE. Peer-reviewed.
  DOI: 10.1109/ICRA55743.2025.11128162 / Project: ADEX
• Babaiee, Z., Mohseni Kiasari, P., Rus, D., Grosu, R. (2025). Visual Graph Arena: Evaluating Visual Conceptualization of Vision and Multimodal Large Language Models. In Forty-second International Conference on Machine Learning : ICML 2025. Forty-second International Conference on Machine Learning (ICML 2025), Vancouver, Canada. Peer-reviewed.
  HDL: 20.500.12708/220011 / Project: TA-CPS
• Grosu, R. (2025). Neural Programs: Linking Probabilistic and Differential Programming. In Principles of Verification: Cycling the Probabilistic Landscape : Essays Dedicated to Joost-Pieter Katoen on the Occasion of His 60th Birthday, Part I (Vol. 15260, pp. 303–321). Springer. Peer-reviewed.
  DOI: 10.1007/978-3-031-75783-9_12

2024

• Holzschuh, J., Mix, M., Freitag, M. T., Hölscher, T., Braune, A., Kötzerke, J., Vrachimis, A., Doolan, P., Ilhan, H., Marinescu, I. M., Spohn, S. K. B., Fechter, T., Kuhn, D., Gratzke, C., Grosu, R., Grosu, A.-L., Zamboglou, C. (2024). The impact of multicentric datasets for the automated tumor delineation in primary prostate cancer using convolutional neural networks on ¹⁸F-PSMA-1007 PET. Radiation Oncology, 19(1), Article 106. Peer-reviewed.
  DOI: 10.1186/s13014-024-02491-w
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-031-42171-6_6
• 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. Peer-reviewed.
  DOI: 10.1109/EMBC53108.2024.10781595
• Babaiee, Z., Kiasari, P., Rus, D., Grosu, R. (2024). We Need Far Fewer Unique Filters Than We Thought. In NeurIPS 2024 Workshop on Scientific Methods for Understanding Deep Learning. SciForDL’24, Vancouver, Canada. Peer-reviewed.
  HDL: 20.500.12708/219266 / Project: MATTO-GBM
• 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). Peer-reviewed.
  DOI: 10.1109/ICRA57147.2024.10610147
• 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). Peer-reviewed.
  DOI: 10.1145/3649329.3658250 / Projects: TAIGER, EdgeAI
• 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). Peer-reviewed.
  DOI: 10.1109/ICRA57147.2024.10611016
• 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). IEEE. Peer-reviewed.
  DOI: 10.1109/ICRA57147.2024.10611037
• 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). Peer-reviewed.
  DOI: 10.1109/WACV57701.2024.00803 / Project: MATTO-GBM
• 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. Peer-reviewed.
  HDL: 20.500.12708/203933

2023

• Cardelli, L., Grosu, R., Larsen, K. G., Tribastone, M., Tschaikowski, M., Vandin, A. (2023). Algorithmic Minimization of Uncertain Continuous-Time Markov Chains. IEEE Transactions on Automatic Control, 68(11), 6557–6572. Peer-reviewed.
  DOI: 10.1109/TAC.2023.3244093 / Project: COCO
• Holzschuh, J. C., Mix, M., Ruf, J., Hölscher, T., Kotzerke, J., Vrachimis, A., Doolan, P., Ilhan, H., Marinescu, I. M., Spohn, S. K. B., Fechter, T., Kuhn, D., Bronsert, P., Gratzke, C., Grosu, R., Kamran, S. C., Heidari, P., Ng, T. S. C., Könik, A., … Zamboglou, C. (2023). Deep learning based automated delineation of the intraprostatic gross tumour volume in PSMA-PET for patients with primary prostate cancer. RADIOTHERAPY AND ONCOLOGY, 188, Article 109774. Peer-reviewed.
  DOI: 10.1016/j.radonc.2023.109774
• 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. Peer-reviewed.
  DOI: 10.1109/DAC56929.2023.10247685 / Project: ADEX
• 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. Peer-reviewed.
  DOI: 10.1016/j.compmedimag.2023.102241 / Download: PDF / Project: PersoRad
• Hirsch, C., Davoli, L., Grosu, R., Ferrari, G. (2023). DynGATT: A dynamic GATT-based data synchronization protocol for BLE networks. Computer Networks, 222, Article 109560. Peer-reviewed.
  DOI: 10.1016/j.comnet.2023.109560 / Project: AFarCloud
• Iturbe, X., Abderrahmane, N., Abella, J., Alcaide Portet, S., Beyne, E., Charles, H.-P., Charpin-Nicolle, C., Chittka, L., Dávilaa, A., Erdmann, A., Estrada, C., Fernández, A., Fontanelli, A., Flich, J., Furano, G., Gloriani, A. H., Isusquiza, E., Grosu, R., Hernández, C., … Zaykov, P. (2023). NimbleAI: Towards Neuromorphic Sensing-Processing 3D-integrated Chips. In 2023 Design, Automation Test in Europe Conference Exhibition (DATE) (pp. 1–6). Peer-reviewed.
  DOI: 10.23919/DATE56975.2023.10136952 / Project: NimbleAI
• 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. Peer-reviewed.
  DOI: 10.1109/ICRA48891.2023.10160617

2022

• Mehmood, U., Roy, S., Damare, A., Grosu, R., Smolka, S. A., Stoller, S. D. (2022). A distributed simplex architecture for multi-agent systems. Journal of Systems Architecture, 134, 102784. Peer-reviewed.
  DOI: 10.1016/j.sysarc.2022.102784
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-031-19759-8_7
• 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. Invited and peer-reviewed.
  DOI: 10.1007/978-3-031-19849-6_21 / Project: ADEX
• Li, P., Yang, Y., Grosu, R., Wang, G., Li, R., Wu, Y., Zeng, H. (2022). Driver Distraction Detection Using Octave-Like Convolutional Neural Network. IEEE Transactions on Intelligent Transportation Systems, 23(7), 8823–8833. Peer-reviewed.
  DOI: 10.1109/TITS.2021.3086411
• Gruenbacher, S. A., Lechner, M., Hasani, R., Rus, D., Henzinger, T. A., Smolka, S. A., Grosu, R. (2022). GoTube: Scalable Statistical Verification of Continuous-Depth Models. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (pp. 6755–6764). AAAI Press. Peer-reviewed.
  DOI: 10.1609/aaai.v36i6.20631
• 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. Peer-reviewed.
  DOI: 10.34726/4262 / Download: PDF
• Li, P., Zhang, Z., Grosu, R., Deng, Z., Hou, J., Rong, Y., Wu, R. (2022). An end-to-end neural network framework for state-of-health estimation and remaining useful life prediction of electric vehicle lithium batteries. RENEWABLE SUSTAINABLE ENERGY REVIEWS, 156, Article 111843. Peer-reviewed.
  DOI: 10.1016/j.rser.2021.111843
• Liu, Q., Liu, X., Grosu, R., Yang, C.-N. (2022). Introduction to the Special Issue on Intelligent Models for Security and Resilience in Cyber Physical Systems. CMES-COMPUTER MODELING IN ENGINEERING SCIENCES, 131(1), 23–26.
  DOI: 10.32604/cmes.2022.020646
• Mahyar, H., Tulala, P., Ghalebi, E., Grosu, R. (2022). DeepWafer: A Generative Wafermap Model with Deep Adversarial Networks. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) (pp. 126–131). Peer-reviewed.
  DOI: 10.1109/ICMLA55696.2022.00025
• Bogdan, P., Grosu, R., Lee, I. (2022). Introduction to the Special Issue on Internet-of-Medical-Things. ACM Transactions on Computing for Healthcare, 3(3), 1–1.
  DOI: 10.1145/3547656
• Neubauer, S. A., Grosu, R. (2022). Robustness Analysis of Continuous-Depth Models with Lagrangian Techniques. In Principles of Systems Design : Essays Dedicated to Thomas A. Henzinger on the Occasion of His 60th Birthday (Vol. 13660, pp. 625–649). Springer. Peer-reviewed.
  DOI: 10.1007/978-3-031-22337-2_30
• 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). Peer-reviewed.
  DOI: 10.1109/ICRA46639.2022.9811650
• He, J., Bartocci, E., Ničković, D., Isakovic, H., Grosu, R. (2022). DeepSTL - From English Requirements to Signal Temporal Logic. In ICSE ’22: Proceedings of the 44th International Conference on Software Engineering (pp. 610–622). Association for Computing Machinery. Peer-reviewed.
  DOI: 10.1145/3510003.3510171

2021

• 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. Peer-reviewed.
  DOI: 10.1007/978-3-030-87348-6_9
• 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. Peer-reviewed.
  DOI: 10.23919/acc50511.2021.9483155
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-030-85172-9_21
• 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. Peer-reviewed.
  HDL: 20.500.12708/58537
• 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). 22nd IEEE International Conference on Industrial Technology (ICIT2021), Valencia, Spain. IEEE. Peer-reviewed.
  DOI: 10.1109/icit46573.2021.9453496
• Spohn, S. K. B., Bettermann, A. S., Bamberg, F., Benndorf, M., Mix, M., Nicolay, N. H., Fechter, T., Hölscher, T., Grosu, R., Chiti, A., Grosu, A.-L., Zamboglou, C. (2021). Radiomics in prostate cancer imaging for a personalized treatment approach - current aspects of methodology and a systematic review on validated studies. Theranostics, 11(16), 8027–8042. Peer-reviewed.
  DOI: 10.7150/THNO.61207
• 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. Peer-reviewed.
  HDL: 20.500.12708/55625
• 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). 22nd IEEE International Conference on Industrial Technology (ICIT2021), Valencia, Spain. IEEE. Peer-reviewed.
  DOI: 10.1109/icit46573.2021.9453670
• 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. Peer-reviewed.
  HDL: 20.500.12708/55647
• 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. Peer-reviewed.
  HDL: 20.500.12708/55648
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-030-91265-9_13

2020

• Roy, S., Mehmood, U., Grosu, R., Smolka, S. A., Stoller, S. D., Tiwari, A. (2020). Learning distributed controllers for V-formation. In 2020 IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS) (pp. 119–128). IEEE. Peer-reviewed.
  DOI: 10.1109/ACSOS49614.2020.00033
• Lechner, M., Hasani, R., Rus, D., Grosu, R. (2020). Gershgorin Loss Stabilizes the Recurrent Neural Network Compartment of an End-to-end Robot Learning Scheme. In 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE. Peer-reviewed.
  DOI: 10.34726/242 / Download: PDF
• Hasani, R., Lechner, M., Amini, A., Rus, D., Grosu, R. (2020). The Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits. In Proceedings of the 37th International Conference on Machine Learning (ICML 2020), Vienna, Austria, PMLR 119, 2020.
  DOI: 10.34726/241 / Downloads: PDF, PDF
• Mehmood, U., Roy, S., Grosu, R., Smolka, S. A., Stoller, S. D., Tiwari, A. (2020). Neural Flocking: MPC-Based Supervised Learning of Flocking Controllers. In Foundations of Software Science and Computation Structures 23rd International Conference, FOSSACS 2020, Held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020, Dublin, Ireland, April 25–30, 2020, Proceedings (pp. 1–16). Springer. Peer-reviewed.
  DOI: 10.1007/978-3-030-45231-5_1
• Phan, D. T., Grosu, R., Jansen, N., Paoletti, N., Smolka, S. A., Stoller, S. D. (2020). Neural Simplex Architecture. In NASA Formal Methods : 12th International Symposium, NFM 2020, Moffett Field, CA, USA, May 11–15, 2020, Proceedings (pp. 97–114). Peer-reviewed.
  DOI: 10.1007/978-3-030-55754-6_6
• Phan, D., Grosu, R., Jansen, N., Paoletti, N., Smolka, S. A., Stoller, S. D. (2020). Neural simplex architecture. In Neural simplex architecture (pp. 97–114). Springer.
  HDL: 20.500.12708/58180
• Hasani, R., Lechner, M., Amini, A., Rus, D., Grosu, R. (2020). A Natural Lottery Ticket Winner: Reinforcement Learning with Ordinary Neural Circuits. In Proceedings of the 37th International Conference on Machine Learning (pp. 4082–4093). Peer-reviewed.
  HDL: 20.500.12708/58294
• Isakovic, H., Crespo, E. A., Grosu, R. (2020). An Energy Sustainable CPS/IoT Ecosystem. In Science and Technologies for Smart Cities 6th EAI International Conference, SmartCity360° (pp. 305–322). Springer. Peer-reviewed.
  DOI: 10.1007/978-3-030-76063-2_22 / Project: ADEPTNESS
• Mehmood, U., Roy, S., Grosu, R., Smolka, S. A., Stoller, S. D., Tiwari, A. (2020). Neural Flocking: MPC-based Supervised Learning of Flocking Controllers. In Neural Flocking: MPC-based Supervised Learning of Flocking Controllers (pp. 1–16). Springer.
  HDL: 20.500.12708/55572
• Grünbacher, S., Cyranka, J., Lechner, M., Islam, A., Smolka, S. A., Grosu, R. (2020). Lagrangian Reachtubes: The Next Generation. arXiv.
  HDL: 20.500.12708/141430
• Zhang, X., Xu, X., Xu, X., Gao, D., Gao, H., Wang, G., Grosu, R. (2020). Intelligent Sea States Identification Based on Maximum Likelihood Evidential Reasoning Rule. Sci, .(22(7)), 25. Peer-reviewed.
  DOI: 10.3390/e22070770
• Roy, S., Mehmood, U., Grosu, R., Smolka, S. A., Stoller, S. D., Tiwari, A. (2020). Learning Distributed Controllers for V-Formation. arXiv.
  DOI: 10.48550/arXiv.2006.00680
• Grosu, R. (2020). ResNets, NeuralODEs and CT-RNNs are Particular Neural Regulatory Networks. arXiv.
  DOI: 10.48550/arXiv.2002.12776
• Grosu, R., Lukina, A., Smolka, S. A., Tiwari, A., Varadarajan, V., Wang, X. (2020). V-Formation via Model Predictive Control. arXiv.
  DOI: 10.48550/arXiv.2002.08955
• Mehmood, U., Stoller, S. D., Grosu, R., Roy, S., Damare, A. (2020). A Distributed Simplex Architecture for Multi-Agent Systems. arXiv.
  HDL: 20.500.12708/141428
• Grünbacher, S., Hasani, R., Lechner, M., Cyranka, J., Smolka, S. A., Grosu, R. (2020). On The Verification of Neural ODEs with Stochastic Guarantees. arXiv.
  HDL: 20.500.12708/141429
• Lechner, M., Hasani, R., Amini, A., Henzinger, T. A., Rus, D., Grosu, R. (2020). Neural Circuit Policies Enabling Auditable Autonomy. Nature Machine Intelligence, 2(10), 642–652. Peer-reviewed.
  DOI: 10.1038/s42256-020-00237-3
• Gruenbacher, S., Cyranka, J., Lechner, M., Islam, Md. A., Smolka, S. A., Grosu, R. (2020). Lagrangian Reachtubes: The Next Generation. In 2020 59th IEEE Conference on Decision and Control (CDC). 59th IEEE Conference on Decision and Control (CDC), Jeju, Korea (the Republic of). IEEE. Peer-reviewed.
  DOI: 10.1109/cdc42340.2020.9304042

2019

• 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. Peer-reviewed.
  DOI: 10.1016/j.asoc.2019.105683
• 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. Peer-reviewed.
  DOI: 10.1016/j.tcs.2018.02.005
• Phan, D., Paoletti, N., Grosu, R., Jansen, N., Smolka, S. A., Stoller, S. D. (2019). Neural Simplex Architecture. arXiv.
  DOI: 10.48550/arXiv.1908.00528
• Ghalebi, E., Mayhar, H., Grosu, R., Taylor, G. W., Williamson, S. A. (2019). Sequential Edge Clustering in Temporal Multigraphs. arXiv.
  DOI: 10.48550/arXiv.1905.11724
• 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.
  DOI: 10.48550/arXiv.1908.09813
• Ghalebi, E., Mayhar, H., Grosu, R., Taylor, G. W., Williamson, S. A. (2019). A Nonparametric Bayesian Model for Sparse Temporal Multigraphs. arXiv.
  DOI: 10.48550/arXiv.1910.05098
• 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. Peer-reviewed.
  DOI: 10.1109/tcbb.2018.2885274
• 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.
  DOI: 10.29007/ns8p
• 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. Peer-reviewed.
  DOI: 10.1109/access.2019.2891969
• 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. Peer-reviewed.
  DOI: 10.1007/s10009-018-0485-6
• Bartocci, E., Cleaveland, R., Grosu, R., Sokolsky, O. (Eds.). (2019). From Reactive Systems to Cyber-Physical Systems - Essays Dedicated to Scott A. Smolka on the Occasion of His 65th Birthday. Springer-Verlag Berlin Heidelberg.
  DOI: 10.1007/978-3-030-31514-6
• 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.
  HDL: 20.500.12708/30240
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-030-23703-5_12
• 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. Peer-reviewed.
  DOI: 10.1109/isce.2019.8900974
• 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. Peer-reviewed.
  DOI: 10.1109/iecon.2019.8927756
• 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. Peer-reviewed.
  DOI: 10.1109/isce.2019.8901012
• 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. SAC ’19: The 34th ACM/SIGAPP Symposium on Applied Computing, Limassol, Cyprus. Association for Computing Machinery.
  DOI: 10.1145/3297280.3297370
• 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). Peer-reviewed.
  HDL: 20.500.12708/58142
• 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, Canada. Peer-reviewed.
  DOI: 10.1109/ijcnn.2019.8851954
• 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). Peer-reviewed.
  HDL: 20.500.12708/58140
• 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. Peer-reviewed.
  DOI: 10.1109/saso.2019.00013

2018

• Isakovic, H., Grosu, R. (2018). A Mixed-Criticality Integration in Cyber-Physical Systems : A Heterogeneous Time-Triggered Architecture on a Hybrid SoC Platform. In Advances in Systems Analysis, Software Engineering, and High Performance Computing (pp. 169–194). IGI Global. Peer-reviewed.
  DOI: 10.4018/978-1-5225-2845-6.ch007
• 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). Peer-reviewed.
  DOI: 10.1007/s13278-018-0506-1
• 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. Peer-reviewed.
  DOI: 10.1098/rstb.2017.0379
• 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. Peer-reviewed.
  DOI: 10.1007/s10703-018-0319-x
• 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. Peer-reviewed.
  DOI: 10.1109/tcad.2018.2858460
• 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. Peer-reviewed.
  HDL: 20.500.12708/57634
• 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. Peer-reviewed.
  DOI: 10.1145/3167132.3167222
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-030-01090-4_25
• 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. Peer-reviewed.
  DOI: 10.1109/test.2018.8624870
• 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. Peer-reviewed.
  DOI: 10.1109/cdc.2018.8619647
• 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). 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), Porto, Portugal. IEEE Computer Society.
  DOI: 10.1109/iccps.2018.00021
• 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.
  DOI: 10.1109/mt-cps.2018.00015
• 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). 2018 ACM/IEEE 9th International Conference on Cyber-Physical Systems (ICCPS), Porto, Portugal. IEEE Computer Society.
  DOI: 10.1109/iccps.2018.00050
• 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. Peer-reviewed.
  HDL: 20.500.12708/57635
• 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. Peer-reviewed.
  DOI: 10.1109/icphys.2018.8387640
• 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. Peer-reviewed.
  HDL: 20.500.12708/57653
• 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, Canada. IEEE. Peer-reviewed.
  DOI: 10.1109/ijcnn.2018.8489422
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-030-05414-4_8
• 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. Peer-reviewed.
  HDL: 20.500.12708/57650
• 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. Peer-reviewed.
  HDL: 20.500.12708/57636
• 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. Peer-reviewed.
  DOI: 10.1109/icphys.2018.8387659

2017

• 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. Peer-reviewed.
  DOI: 10.1016/j.nahs.2016.03.008
• 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. Peer-reviewed.
  DOI: 10.1109/cjece.2017.2703093
• 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. Peer-reviewed.
  HDL: 20.500.12708/148019
• 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. Peer-reviewed.
  DOI: 10.3390/s17092049
• 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. Peer-reviewed.
  DOI: 10.1007/s10703-016-0265-4
• Amorim, T., Ratasich, D., Macher, G., Ruiz, A., Schneider, D., Driussi, M., Grosu, R. (2017). Runtime Safety Assurance for Adaptive Cyber-Physical Systems : ConSerts M and Ontology-Based Runtime Reconfiguration Applied to an Automotive Case Study. 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. Peer-reviewed.
  DOI: 10.4018/978-1-5225-2845-6.ch006
• Taheri, S. M., Mahyar, H., Firouzi, M., Ghalebi, E., Grosu, R., Movaghar, A. (2017). HellRank: a Hellinger-based centrality measure for bipartite social networks. Social Network Analysis and Mining. Peer-reviewed.
  DOI: 10.1007/s13278-017-0440-7 / Download: PDF
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-67471-1_2
• 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.
  DOI: 10.1007/978-3-319-66284-8_12
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-68167-2_29
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-59147-6_34
• 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, Canada. Peer-reviewed.
  DOI: 10.1109/ijcnn.2017.7966126
• 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. Peer-reviewed.
  DOI: 10.1109/icit.2017.7915579
• 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. Peer-reviewed.
  DOI: 10.1109/acsd.2017.23
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-63387-9_19
• 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).
  HDL: 20.500.12708/57234
• 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). Peer-reviewed.
  HDL: 20.500.12708/57235
• 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). Peer-reviewed.
  HDL: 20.500.12708/57236
• 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). Peer-reviewed.
  HDL: 20.500.12708/57237
• 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). Peer-reviewed.
  HDL: 20.500.12708/57238
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-63387-9_17
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-662-54580-5_17
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-59153-7_46
• 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. Peer-reviewed.
  DOI: 10.1109/isorc.2017.7

2016

• Esterle, L., Grosu, R. (2016). Cyber-physical systems : challenge of the 21st century. Elektrotechnik Und Informationstechnik : E i, 133(7), 299–303. Peer-reviewed.
  DOI: 10.1007/s00502-016-0426-6 / Download: PDF
• 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. Peer-reviewed.
  DOI: 10.1109/memcod.2016.7797741
• 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. Peer-reviewed.
  DOI: 10.1109/prime.2016.7519486
• 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. Peer-reviewed.
  HDL: 20.500.12708/56824
• 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. Peer-reviewed.
  DOI: 10.1109/ispcs.2016.7579516
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-45177-0_9
• 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. Peer-reviewed.
  HDL: 20.500.12708/56839
• 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. Peer-reviewed.
  DOI: 10.1109/isie.2016.7744897
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-46982-9_30
• 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. Peer-reviewed.
  DOI: 10.1109/hldvt.2016.7748272
• 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.
  DOI: 10.1007/978-3-319-47169-3_28
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-47151-8_9
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-47166-2_4
• 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. Peer-reviewed.
  HDL: 20.500.12708/56706
• 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. Peer-reviewed.
  DOI: 10.1145/2883817.2883839
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-46982-9_13

2015

• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-26287-1_1
• 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. Peer-reviewed.
  DOI: 10.1016/j.tcs.2014.03.018
• 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. Peer-reviewed.
  DOI: 10.1109/cdc.2015.7403319
• 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. Peer-reviewed.
  DOI: 10.1109/memcod.2015.7340489
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-26287-1_2
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-319-23820-3_13
• 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. Peer-reviewed.
  DOI: 10.1109/iros.2015.7353387
• 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, United States of America (the). ACM. Peer-reviewed.
  DOI: 10.1145/2728606.2728609
• Haghighi, I., Jones, A., Kong, Z., Bartocci, E., Gros, R., Belta, C. (2015). SpaTeL: a novel spatial-temporal logic and its applications to networked systems. 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. Peer-reviewed.
  DOI: 10.1145/2728606.2728633

2014

• Bartocci, E., Höftberger, O., Grosu, R. (2014). Cyber-Physical Systems: Theoretical and Practical Challenges. ERCIM NEWS, 2014(97), 8–9. Invited.
  HDL: 20.500.12708/157429
• 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, Israel.
  HDL: 20.500.12708/55828
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-662-45231-8_16
• 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).
  HDL: 20.500.12708/55826
• 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. Peer-reviewed.
  HDL: 20.500.12708/55802

2013

• 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. Peer-reviewed.
  DOI: 10.1109/tcbb.2012.125
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-40787-1_9
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-39176-7_8
• Bartocci, E., Grosu, R. (2013). Monitoring with uncertainty. In Electronic Proceedings in Theoretical Computer Science (pp. 1–4). Electronic Proceedings in Theoretical Computer Science.
  DOI: 10.4204/eptcs.124.1

2012

• 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. Peer-reviewed.
  DOI: 10.1007/s10009-010-0184-4
• 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. Peer-reviewed.
  DOI: 10.1007/s10703-012-0171-3
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-31424-7_35
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-33386-6_9
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-33636-2_16
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-35632-2_18
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-29860-8_15

2011

• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-22110-1_31
• 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. ACM. Peer-reviewed.
  DOI: 10.1145/2037509.2037525
• 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. ACM. Peer-reviewed.
  DOI: 10.1145/2037509.2037532
• 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. Peer-reviewed.
  DOI: 10.1007/978-3-642-19835-9_30

• Farsang, M., Neubauer, S., Grosu, R. (2024, December 14). Liquid Resistance Liquid Capacitance Networks [Poster Presentation]. NeuroAI: Fusing Neuroscience and AI for Intelligent Solutions (NeuroAI @ NeurIPS2024), Canada.
  HDL: 20.500.12708/223289
• 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).
  DOI: 10.34726/4861 / Download: PDF / Project: NimbleAI
• Hirsch, C., Redl, M., Grosu, R. (2018). Towards an Agricultural IoT-Infrastructure for Micro-climate Measurements. Workshop on Smart Farming, Porto, Portugal.
  HDL: 20.500.12708/86817
• 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.
  HDL: 20.500.12708/86397

2026

• Leszczyk, J. (2026). Computational Modeling of Macaque Locomotion: From Motion Capture to Joint Torque Actuation [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2026.136100

2025

• Kenbeek, V. T. W. (2025). Improving technical documentation for digital design : using generative AI to enhance understanding of timing diagrams [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2025.128887 / Download: PDF
• Fromherz, T. (2025). Optimization strategies for locating extrema in a 5G cellular transceiver model [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2025.122762 / Download: PDF
• Clement, M.-L. (2025). Multimodal RGB-D autonomous agents steered by deep neural networks [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2025.129104 / Download: PDF
• Stănușoiu, M.-T. (2025). Modeling system dynamics In partially-observable environments using biologically-inspired recurrent neural networks [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2025.129105 / Download: PDF
• Brandstätter, A. (2025). Coordination and control of robotic multi-agent systems in confined environments [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2025.128748 / Download: PDF

2024

• Kresse, F. G. (2024). Deep off-policy evaluation with autonomous racing cars [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2024.117422 / Download: PDF
• Freinberger, D. (2024). A hybrid quantum-classical framework for reinforcement learning of atari games [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2024.112162 / Download: PDF
• Engl, M. (2024). Coordinated control of ground and aerial vehicle during takeoff and landing [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2024.108801 / Download: PDF

2023

• Scheuchenstuhl, D. (2023). Attentional neural network based dynamic object detection for autonomous multi-agent systems [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2023.101601 / Download: PDF
• Resch, F. (2023). Autonomous racing with attention-based neural networks [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2023.101602 / Download: PDF
• Neubauer, S. (2023). Robustness analysis of continuous-depth neural networks [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2023.115404 / Download: PDF
• Ulmer, S. (2023). Attention based neural network for autonomous driving agents [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2023.101600 / Download: PDF

2022

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

2021

• Temper, A. (2021). Rotating quadcopter flight for collision avoidance with static front facing LiDAR sensor [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2021.59502 / Download: PDF
• Brunnbauer, A. (2021). Model-based deep Reinforcement learning for autonomous racing [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2021.86588 / Download: PDF
• Brantner, H. (2021). Neural network arena: Investigating long-term dependencies in deep models [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2021.88580 / Download: PDF

2020

• Stelzhammer, P. (2020). Efficient detection of influential users in social recommender systems [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2020.66368 / Download: PDF
• Ghalebi, E. (2020). Modeling and analysis of time-evolving sparse networks [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2020.81081 / Download: PDF
• Lemmel, J. (2020). Reinforcement learning ohne Backpropagation in Neural Regulatory Networks : eine erste Abschätzung : a preliminary assessment [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2020.81325 / Download: PDF
• Deliorman, C. (2020). Performance evaluation of a middleware framework for CPS/IoT ecosystem [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2020.76280 / Download: PDF
• Hasani, R. (2020). Interpretable recurrent neural networks in continuous-time control environments [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2020.78942 / Download: PDF
• Puchinger, T. (2020). IoT sensor swarm for agricultural microclimate measurements [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2021.57661 / Download: PDF

2019

• Tulala, P. (2019). Deep generative clustering of spatial wafer patterns [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2019.60502 / Download: PDF
• Brandstätter, A. (2019). Local positioning system for quadcopters [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2019.32156 / Download: PDF
• Müllner, B. (2019). Better end-to-end object detection in low SNR environments with time-of-flight cameras [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2019.67763 / Download: PDF
• Dukkon, Á. (2019). CPS/IoT Ecosystem : exploring operational scopes for industrial internet-of-things and an analysis of quality-of-service properties [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2019.59481 / Download: PDF
• Ratasich, D. (2019). Self-healing cyber-physical systems [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2019.64761 / Download: PDF

2018

• Lukina, A. (2018). Adaptive optimization framework for verification and control of cyber-physical systems [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2018.68341 / Download: PDF
• Javin, M. (2018). ODYNN : an optimization suite for biological neural circuits [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2018.56746 / Download: PDF
• Jakšić, S. (2018). Real-time monitoring for correctness and robustness [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2018.60092 / Download: PDF
• Fuchs, M. (2018). A cell-level neural simulation suite for the analysis of learning and adaptive behavior in the C. elegans’ nervous system [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2018.50785 / Download: PDF
• Lung, D. (2018). OpenWorm: design and evaluation of neural circuits on the virtual worm, caenorhabditis elegans [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2018.53821 / Download: PDF
• Wang, G. (2018). Neural computation methods for industrial data processing [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2018.62942 / Download: PDF

2017

• Lechner, M. (2017). Brain-inspired neural control [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  HDL: 20.500.12708/158377
• Selyunin, K. (2017). Neural models for monitoring and control with applications in automotive domain [Dissertation, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2017.50304 / Download: PDF
• Heindl, M. (2017). Systematic testing of analog mixed-signal systems [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2017.37812 / Download: PDF

2016

• Balún, O. (2016). Towards distributed controllers based on caenorhabditis elegans locomotory neural network [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2016.40139 / Download: PDF
• Alakhras, N. (2016). Model-driven compensation of the effects of environmental conditions on quartz oscillator based clocks [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2016.28040 / Download: PDF
• Petschina, B. (2016). Erstellung eines modellbasierten Designprozesses zur Entwicklung von cyber-physischen Systemen [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2016.31841 / Download: PDF
• Gabriel, C. (2016). Development of an advanced protection concept for automotive wire harnesses [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2016.29927 / Download: PDF

2015

• Grahsl, J. (2015). Hardware / software architecture of a wearable eye tracking system [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  HDL: 20.500.12708/158755

2014

• Frischenschlager, A. (2014). Autonomous path planning using probabilistic maps [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2014.25301 / Download: PDF
• Chmelar, M. (2014). An adaptive computer-vision-based method to calculate vehicle-motion parameters [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2014.21222 / Download: PDF
• Ratasich, D. (2014). Generic low-level sensor fusion framework for cyber-physical systems [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2014.21220 / Download: PDF
• Macher, D. (2014). Intercommunication framework for autonomous real-time systems [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2014.21221 / Download: PDF
• Brugger, S. (2014). Integrating probabilistic information of dynamic environment into maps for enhanced action planning [Diploma Thesis, Technische Universität Wien]. reposiTUm.
  DOI: 10.34726/hss.2014.21223 / Download: PDF

Soon, this page will include additional information such as reference projects, activities as journal reviewer and editor, memberships in councils and committees, and other research activities.

Until then, please visit Radu’s research profile in TISS.