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Thomas Ploetz is a Computer Scientist with expertise and decades of experience in Pattern Recognition and Machine Learning research (PhD from Bielefeld University, Germany). His core research lies in the field of wearable and ubiquitous computing with specific focus on computational behavior analysis that is driven by the automated analysis of what people are doing and how this changes over time — all based on the automated analysis of multimodal time series data that are captured using a range of sensors that are either body worn or integrated into the built environment. His work draws from and contriibutes to Artificial Intelligence / Machine Learning, Wearable and Ubiquituous Computing, and Health Informatics. Main driving functions for his work are "in the wild" deployments and as such the development of systems and methods that have a real impact on people's lives.
Thomas has been very active in the mobile and ubiquitous, including wearable computing community. He is co-editor in chief of the Proc. of the ACM on Interactive, Mobile, Wearable, and Ubiquitous computing technology (IMWUT) — the flagship ACM journal in the field — and has twice been co-chair of the technical program committee of the International Symposium on Wearable Computing (ISWC), and was general co-chair of the 2022 Int. Joint Conf. On Pervasive and Ubiquitous Computing (Ubicomp).
Thomas is a Distinguished Member of the ACM.
Thomas Ploetz's teaching follows the von Humboldtian notion of a research-teaching-scholarship nexus, which he considers key to educating independent and critical minds that will thrive in their subjects and in society in general. At Georgia Tech he has been teaching two large classes on a regular basis -- Mobile and Ubiquitous Computing and Introduction to Artificial Intelligence. Both classes habe project-driven, practice-oriented focus, which allows him to both cover the foundations of the respective fields and to engage his students into contemporary research.
* "Layout-agnostic human activity recognition in smart homes through textual descriptions of sensor triggers (tdost)" Thukral, Megha, Dhekane, Sourish Gunesh, Hiremath, Shruthi K, Haresamudram, Harish, and Ploetz, Thomas, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2025
* "Past, present, and future of sensor-based human activity recognition using wearables: A surveying tutorial on a still challenging task", Haresamudram, Harish, Tang, Chi Ian, Suh, Sungho, Lukowicz, Paul, and Ploetz, Thomas, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2025
* "Transfer learning in sensor-based human activity recognition: A survey", Dhekane, Sourish Gunesh, and Ploetz, Thomas, ACM Computing Surveys 2025
* "Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition-And Ways to Overcome Them", Haresamudram, Harish, Beedu, Apoorva, Rabbi, Mashfiqui, Saha, Sankalita, Essa, Irfan, and Ploetz, Thomas, In Proceedings of the AAAI Conference on Artificial Intelligence 2025
* "Imugpt 2.0: Language-based cross modality transfer for sensor-based human activity recognition", Leng, Zikang, Bhattacharjee, Amitrajit, Rajasekhar, Hrudhai, Zhang, Lizhe, Bruda, Elizabeth, Kwon, Hyeokhyen, and Plötz, Thomas, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2024
* "Applying machine learning for sensor data analysis in interactive systems: Common pitfalls of pragmatic use and ways to avoid them", Ploetz, Thomas, ACM Computing Surveys (CSUR) Sep 2021