I am a final year PhD student with the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Techology, Atlanta. I am advised by Prof. Thomas Ploetz and Prof. Irfan Essa. I received my Master’s degree in May 2019 from Georgia Tech, where my thesis studied the role of representations in human activity recognition using wearables. I was advised by Prof. Thomas Ploetz and Prof. David Anderson. I have been supported by funding from the National Science Foundation (through the AI-CARING Institute), Optum AI, and Google.
Research
My research broadly involves learning representations for time-series data, with a special focus on developing techniques that require minimal supervision. I develop unsupervised and self-supervised learning algorithms for data from wearable sensors, including accelerometers, gyroscopes and intertial measurement units (IMUs). Subsequently, I use such representations to analyse human behavior, through movements and activities..
@article{haresamudram2025past,title={Past, present, and future of sensor-based human activity recognition using wearables: A surveying tutorial on a still challenging task},author={Haresamudram, Harish and Tang, Chi Ian and Suh, Sungho and Lukowicz, Paul and Ploetz, Thomas},journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},volume={9},number={2},pages={1--44},year={2025},publisher={ACM New York, NY, USA},url={https://dl.acm.org/doi/pdf/10.1145/3729467},}
Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition-And Ways to Overcome Them
Harish Haresamudram, Apoorva Beedu, Mashfiqui Rabbi, and 3 more authors
In Proceedings of the AAAI Conference on Artificial Intelligence, 2025
@inproceedings{haresamudram2025limitations,title={Limitations in Employing Natural Language Supervision for Sensor-Based Human Activity Recognition-And Ways to Overcome Them},author={Haresamudram, Harish and Beedu, Apoorva and Rabbi, Mashfiqui and Saha, Sankalita and Essa, Irfan and Ploetz, Thomas},booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},volume={39},number={1},pages={273--281},year={2025},url={https://ojs.aaai.org/index.php/AAAI/article/view/32004},}
Layout-agnostic human activity recognition in smart homes through textual descriptions of sensor triggers (TDOST)
Megha Thukral, Sourish Gunesh Dhekane, Shruthi K Hiremath, and 2 more authors
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2025
@article{thukral2025layout,title={Layout-agnostic human activity recognition in smart homes through textual descriptions of sensor triggers (TDOST)},author={Thukral, Megha and Dhekane, Sourish Gunesh and Hiremath, Shruthi K and Haresamudram, Harish and Ploetz, Thomas},journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},volume={9},number={1},pages={1--38},year={2025},publisher={ACM New York, NY, USA},url={https://dl.acm.org/doi/pdf/10.1145/3712278},}
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition
Harish Haresamudram, Irfan Essa, and Thomas Ploetz
@article{haresamudram2024towards,title={Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity Recognition},author={Haresamudram, Harish and Essa, Irfan and Ploetz, Thomas},journal={Sensors},volume={24},number={4},pages={1238},year={2024},publisher={Multidisciplinary Digital Publishing Institute},}
Towards Learning Discrete Representations via Self-Supervision for Wearables-Based Human Activity RecognitionSensors, 2024
