Qin Lin(林勤)

Ph.D. Candidate
Delft University of Technology
E-mail: q.lin[at]tudelft[dot]nl
office: Building 28, 6.W.920, Tudelft


I am a forth-year PhD student in Group of Cyber Security, Delft University of Technology. My PhD supervisor is Dr. Sicco Verwer. My research interests include machine learning, grammatical inference, time series data mining, and hybrid model checking. (CV)


Research Area


My research is funded by Technologiestichting STW VENI project 13136 (MANTA) and NWO project 62001628 (LEMMA).


My Google Scholar profile

  1. Lin, Q. and Wang, J., 2014. Vertically correlated echelon model for the interpolation of missing wind speed data. IEEE Transactions on Sustainable Energy, 5(3), pp.804-812. (IF: 6.2)

  2. Gu, H., Wang, J., Lin, Q. and Gong, Q., 2015. Automatic contour-based road network design for optimized wind farm micrositing. IEEE Transactions on Sustainable Energy, 6(1), pp.281-289. (IF: 6.2)

  3. Lin, Q., Zhang, Y. Verwer, S. and Wang, J., 2018. MOHA: a Multi-mode Hybrid Automaton Model for Learning Car-following Behaviors, IEEE Transactions on Intelligent Transportation Systems. (IF: 4.0)

  4. Zhang, Y., Lin, Qin, Wang, J, Verwer, S., and Dolan J., 2018. Lane-change Intention Estimation for Car-following Control in Autonomous Driving, IEEE Transactions on Intelligent Vehicles.


  1. Lin, Q., Wang, J. and Qiao, W., 2013, November. Denoising of wind speed data by wavelet thresholding. In Chinese Automation Congress (CAC), 2013 (pp. 518-521). IEEE.

  2. Lin, Q, Hammerschmidt, C, Pellegrino, G. and Verwer, S., 2016, August. Short-term Time Series Forecasting with Regression Automata. In ACM SIGKDD’16 Workshop on Mining and Learning from Time Series (MiLeTS).

  3. Pellegrino, G., Hammerschmidt, C., Lin, Q. and Verwer, S., 2017, January. Learning Deterministic Finite Automata from Infinite Alphabets. In International Conference on Grammatical Inference (pp. 120-131).

  4. Hammerschmidt, C., Verwer, Sicco., Lin, Q., State R., 2016, Interpreting Finite Automata for Sequential Data, In Interpretable ML for Complex Systems NIPS 2016 Workshop.

  5. Zhang, Y., Lin, Q., Wang, J. and Verwer, S., 2017. Car-following Behavior Model Learning Using Timed Automata. IFAC-PapersOnLine, 50(1), pp.2353-2358.

  6. Zhang, Y., Lin. Q., Wang, J and Verwer S., Dolan J., Data-driven Behavior Generation Algorithm in Car-following Scenarios, 2017, August. In 25th International Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD).

  7. Pellegrino, G., Lin, Q., Hammerschmidt, C. and Verwer, S., 2017, May. Learning behavioral fingerprints from Netflows using Timed Automata. In Integrated Network and Service Management (IM), 2017 IFIP/IEEE Symposium on (pp. 308-316). IEEE.

  8. Liu, X., Lin, Q., Verwer S. and Jarnikov, D., 2017, June. Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata, In Thirty-Second Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) Workshop on Learning and Automata (LearnAut)

  9. Lin, Q., Adepu S., Verwer, S. and Mathur, A., 2018, June. TABOR: A Graphical Model-based Approach for Anomaly Detection in Industrial Control Systems, In ACM ASIA Conference on Information, Computer and Communications Security (ASIACCS, acceptance rate: 20%)


  1. Interpolation Techniques Based on Wind Speed Correlation for Anemometers’ Missing Data in Wind Farm, J. Wang, Q. Lin, Patent Number: ZL201310119605.8, Publication Date: 20-01-2016, Certificate Number: 1926442 (Chinese Invention Patent).

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