Gregory Dudek
Gregory Dudek is a chaired professor of computer science at McGill University, was the Director of the McGill Center for Intelligent Machines from 2004 to 2007, and was the Director of the McGill University School of Computer Science from 2008 to 2016 . He served as the Scientific Director of the NSERC Canadian Field Robotics Network from 2012 to 2018 . He became Scientific Director and Lead Investigatior or it ssuccessor the NSERC Canadian Robotics Network. In 2018, Samsung announced that he would become a VP Research and Lead their new Samsung AI Center in Montreal (SAIC-Montreal) . He is  the son of poet Louis Dudek, he was made a Dawson Scholar of that university (an honorary title) and subsequently James McGill Chair (), and directs the mobile robotics laboratory there. He has written over 300 refereed articles on computer vision and robotics, and is co-author (with Michael Jenkin) of the book "Computational Principles of Mobile Robotics" which is used to teach robotics at a number of universities . Research. His research deals with sensing for robots and has included theoretical work on the complexity of robot localization  and the development of underwater and amphibious robots. He has worked  on the use of topological maps and the complexity of topological mapping, an abstract idealized form of robotics problem. He has also looked at robot position estimation using photographic data, and the automated detection of interesting images. He co-developed a novel underwater and amphibious robot and associated software which formed the basis of a company called Independent Robotics. He also has published papers on telecommunications, notably related to 5G systems with his colleagues at Samsung. Dudek mentored/led a team of students that won the 1999 AAAI mobile robotics competition, and with one of the team members, Andrew  Ladd, they examined the use of WiFi signature mapping for mapping and location estimation long before it was widely known. With his colleagues he produced the first formal proof of the complexity of global robot localization in a metric environment (i.e. how hard it is, in the worst possible case, for a robot to determine its position if it totally lost) . Dudek has a wide range of research interests many of which have the common theme of robotics. He works with students in the Mobile Robotics Lab (MRL) at McGill on many problems involving aspects of artificial perception, robot  navigation, sample theory (e.g. applications of the secretary problem to robotics and recommender systems. Education. Dudek attended St. George's School of Montreal and subsequently obtained his B.Sc. in Physics and Computer Science at Queen's University and his M.Sc. and Ph.D. in Computer Science at the University of Toronto. Activities. Gregory's research interests often overlap with his personal life and allow him to travel to many interesting places. He and other students in the MRL lab take trips to run experiments on Aqua, an amphibious robot. Greg visited Mexico in 2009. Greg has also visited many other countries . Selected Writings. 1. Dudek G, Tsotsos JK. Shape representation and recognition from curvature. In: 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Proceedings. ; 1991:35-41. doi:10/fct63v 2. Dudek G, Jenkin M, Milios E, Wilkes D. Robotic exploration as graph construction. IEEE Transactions on Robotics and Automation. 1991;7(6):859-865. doi:10.1109/70.105395 3. Dudek G, Jenkin M, Milios E, Wilkes D. A Taxonomy for Swarm Robotics. In: Proc. IEEE/RSJ IROS. ; 1993:441-447. 4. Daum M, Dudek G. On 3-D surface reconstruction using shape from shadows. In: Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern  Recognition (Cat. No.98CB36231). ; 1998:461-468. doi:10/b8wprs 5. Simhon S, Dudek G. A Global Topological Map formed by Local Metric Maps. In: Proceedings of the IEEE/RSJ Int. Conference on Intelligent Robots and Systems (IROS). Vol 3. ; 1998:1708-1714. 6. Georgiades C, German A, Hogue A, et al. AQUA: an aquatic walking robot. In: Proc. IROS 2004. ; 2004. 7. Marinakis D, Meger D, Rekleitis I, Dudek G. Hybrid Inference for Sensor Network Localization using a Mobile Robot. In: AAAI National Conference on Artificial Intelligence. ; 2007. 8. Girdhar YA, Dudek G. Online navigation summaries. In: ICRA. ; 2010:5035-5040. 9. Sattar J , Dudek G. Towards quantitative modeling of task confirmations in human-robot dialog. In: ICRA. ; 2011:1957-1963. 10. Xu A, Dudek G. Fourier Tag: A smoothly degradable fiducial marker system with configurable payload capacity. In: 8th Canadian Conference on Computer and Robot Vision (CRV ’11) (to Appear). ; 2011. 11. Manjanna S, Dudek G, Giguere P. Using gait change for terrain sensing by robots. In: 2013 International Conference on Computer and Robot Vision. IEEE; 2013:16-22. 12. Xu A, Dudek G. OPTIMo: Online Probabilistic Trust Inference Model for Asymmetric Human-Robot Collaborations. In: 2015 10th ACM/IEEE International Conference on Human-Robot  Interaction (HRI). ; 2015:221-228. 13. Kalmbach A, Girdhar Y, Sosik HM, Dudek G. Phytoplankton hotspot prediction with an unsupervised spatial community model. In: 2017 IEEE International Conference on Robotics and Automation (ICRA). ; 2017:4906-4913. doi:10.1109/ICRA.2017.7989568 14. Shkurti F, Dudek G. Topologically distinct trajectory predictions for probabilistic pursuit. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). ; 2017:5653-5660. doi:10.1109/IROS.2017.8206454 15. Hansen J, Dudek G. Coverage Optimization with Non-Actuated, Floating Mobile Sensors using Iterative Trajectory Planning in Marine Flow Fields. In: To Appear in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). ; 2018 . http://johannah.github.io/publications/iros2018driftercoverage.pdf 16. Dudek G, Joppa L, Lakshmanan V, Kumar V, Mukkavilli SK. EnviroNet: ImageNet analog for environment & global AI challenge. In: 99th American Meteorological Society Annual Meeting. AMS; 2019. 17. Chen X, Li H, Zhou C, Liu X, Wu D, Dudek G. FiDo: Ubiquitous Fine-Grained WiFi-based Localization for Unlabelled Users via Domain Adaptation. In: Proceedings of The Web Conference 2020. ; 2020:23-33. 18. Holliday A, Dudek G. Pre-trained CNNs as Visual Feature Extractors: A Broad Evaluation. In: 2020 17th Conference on Computer and Robot Vision (CRV). IEEE; 2020:78-84. 19. Manderson T, Wapnick S, Meger D, Dudek  G. Learning to Drive Off-Road on Smooth Terrains in Unstructured Environments Using an Onboard Camera and Sparse Aerial Images. In: Proceedings of the 2020 IEEE International Conference on Robotics and Automation. ; 2020. 20. Friedman N, Goedicke D, Zhang V, et al. Capturing attention with wind. In: Workshop on Approaches to Advance Physical Human-Robot Interaction (AVHC). ; 2020:2. https://vgrserver.eecs.yorku.ca/~jenkin/papers/2020/2020ICRAWorkshop.pdf 21. Xu YT, Chen X, Liu X, Meger D, Dudek G. PresSense: Passive Respiration Sensing via Ambient WiFi Signals in Noisy Environments. In: 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). ; 2020:4032-4039. doi:10.1109/IROS45743.2020.9341474 22. Hogan FR , Jenkin M, Rezaei-Shoshtari S, Girdhar Y, Meger D, Dudek G. Seeing Through your Skin: Recognizing Objects with a Novel Visuotactile Sensor. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. ; 2021:1218-1227. References. 1. Dudek, G. And Jenkin, M. Computational Principles of Mobile Robotics. Cambridge University Press, 2010. . 2. Dudek, G., Romanik, K., & Whitesides, S. (1998). Localizing a robot with minimum travel. SIAM Journal on Computing, 27(2), 583-604. 3. AI Magazine. Profile of a Winner: McGill University.