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Qin Lin(林勤)

postdoctoral Research Fellow
Carnegie Mellon University
E-mail: qinlin[at]andrew.cmu.edu
office: 3106 Newell-Simon Hall

Open positions

I will join the EECS department at Cleveland State University as a tenure-track assistant professor in January 2022. I am looking for BS/MS/Ph.D. students to join my lab as founding members. The students are expected to have backgrounds in math/CS/control (preferred: research experience in motion planning or control). I also welcome visiting students/scholars and remote interns. If you are interested, please send me your CV and schedule a chat. The advertisement in Chinese can be found here here.

News

About

I am a postdoc at the Robotics Institute of Carnegie Mellon University. My advisor is Prof. John M. Dolan. My research interests are in the intersection of machine learning, control theory, and formal verification towards the goal to enhance security and safety of safety-critical cyber-physical systems whilst deployed in dynamic, uncertain, and adversarial environments. I develop explainable and verifiable machine learning-based intrusion detection algorithms to protect industrial control systems, such as water treatment plants, from cyber attacks. I also rigorously verify safety properties of learning-enabled components and develop safety-guaranteed planning and control algorithms for autonomous driving systems.

Education

Research Area

Project

My research is funded by DARPA Assured Autonomy project.

Publications

My Google Scholar profile

^: supervised students, *: corresponding author

Journal

  1. Qin Lin, Stefan Mitsch, Andre Platzer, John M. Dolan, 2021. “Practically Safe and Recoverable Waypoint-following for Autonomous Vehicles”, IEEE Control Systems Letters (L-CSS), Accepted

  2. Shivesh Khaitan^, Qin Lin*, Dolan M. Dolan, 2021. “Safe planning and control under uncertainty for self-driving”, IEEE Transactions on Vehicular Technology, 70(10), pp. 9826-9837.

  3. Qin Lin, Yihuan Zhang, Sicco Verwer, and Jun Wang, 2019. “MOHA: a multi-mode hybrid automaton model for learning car-following behaviors.” IEEE Transactions on Intelligent Transportation Systems, 20(2), pp.790-796.

  4. Yihuan Zhang, Qin Lin, Jun Wang, Sicco Verwer, and John M. Dolan, 2018. “Lane-change intention estimation for car-following control in autonomous driving.” IEEE Transactions on Intelligent Vehicles, 3(3), pp.276-286.

  5. Huajie Gu, Jun Wang, Qin Lin and Qi Gong, 2015. “Automatic contour-based road network design for optimized wind farm micrositing”, IEEE Transactions on Sustainable Energy, 6(1), pp.281-289.

  6. Qin Lin and Jun Wang, 2014. “Vertically correlated echelon model for the interpolation of missing wind speed data”, IEEE Transactions on Sustainable Energy, 5(3), pp. 804-812.

Conference

  1. Omid Jahanmahin^, Qin Lin, Yanjun Pan, John M. Dolan, “Jerk-Minimized CILQR for Human-Like Driving on Two-Lane Highways”. in 32nd IEEE Intelligent Vehicles Symposium (IV), 2021. (Accepted)

  2. Qin Lin, Sicco Verwer, and John M. Dolan, “Safety Verification of a Data-driven Adaptive Cruise Controller”, in 31st IEEE Intelligent Vehicle Symposium (IV), 2020 (pp. 1875-1880). IEEE

  3. Yanjun Pan^, Qin Lin, Het Shah, John M. Dolan, “Safe Planning for Self-Driving Via Adaptive Constrained ILQR.” 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 2377-2383). IEEE.

  4. Qin Lin, Xin Chen, Aman Khurana, John M. Dolan, “ReachFlow: An Online Safety Assurance Framework for Waypoint-Following of Self-driving Cars”. in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), (pp. 6627-6632). IEEE.

  5. Qin Lin, Wenshuo Wang, Yihuan Zhang, and John M. Dolan, “Measuring Similarity of Interactive Driving Behaviors Using Matrix Profile”. In 2020 American Control Conference (ACC) (pp. 3965-3970). IEEE.

  6. Qin Lin, Sicco Verwer, Robert Kooij and Aditya Mathur, “Using Datasets from Industrial Control Systems for Cyber Security Research and Education”. In International Conference on Critical Information Infrastructures Security (pp. 122-133). Springer, 2019.

  7. Qin Lin, Sridha Adepu,Sicco Verwer,and AdityaMathur,“TABOR:AGraphicalModel-basedApproachforAnomaly Detection in Industrial Control Systems”. In Proceedings of the 2018 on Asia Conference on Computer and Communications Security (pp. 525-536). ACM. (acceptance rate: 62/320=20%)

  8. Gaetano Pellegrino, Christian Hammerschmidt, Qin Lin, and Sicco Verwer, “Learning Deterministic Finite Automata from Infinite Alphabets”. In 2017 International Conference on Grammatical Inference (pp. 120-131).

  9. Gaetano Pellegrino, Qin Lin, Christian Hammerschmidt, and Sicco Verwer, “Learning Behavioral Fingerprints from Netflows Using Timed Automata”. In Integrated Network and Service Management (IM), 2017 IFIPIEEE Symposium on (pp. 308-316). IEEE. (acceptance rate: 44154=28.6%)

  10. Xiaoran Liu^, Qin Lin, Sicco Verwer, and Dmitri Jarnikov, “Anomaly Detection in a Digital Video Broadcasting System Using Timed Automata”, Thirty-Second Annual ACM/IEEE Symposium on Logic in Computer Science (LICS) Workshop on Learning and Automata (LearnAut), 2017

  11. Yihuan Zhang, Jun Wang, Qin Lin, Sicco Verwer, and John M. Dolan, “A Data-driven Behavior Generation Algorithm in Car-following Scenarios”. In Dynamics of Vehicles on Roads and Tracks Vol 1: Proceedings of the 25th International Symposium on Dynamics of Vehicles on Roads and Tracks (IAVSD 2017), (pp. 227-232). CRC Press.

  12. Yihuan Zhang, Qin Lin, Jun Wang, and Sicco Verwer, “Car-following Behavior Model Learning Using Timed Au- tomata”. IFAC-PapersOnLine, 50(1), 2017 (pp.2353-2358)

  13. Qin Lin, Christian Hammerschmidt, Gaetano Pellegrino, and Sicco Verwer, “Short-term Time Series Forecasting with Regression Automata”, ACM SIGKDD 2016 Workshop on Mining and Learning from Time Series (MiLeTS)

  14. Christian Hammerschmidt, Sicco Verwer, and Qin Lin, “Interpreting Finite Automata for Sequential Data”, Inter- pretable Machine Learning for Complex Systems: NIPS 2016 workshop proceedings

  15. Qin Lin, Jun Wang, and Weiting Qiao, “Denoising of Wind Speed Data by Wavelet Thresholding”. In Chinese Automation Congress (CAC), 2013 (pp. 518-521). IEEE

Academic Services

Teaching Assistance

Students Supervision