The idea behind ubiquitous computing is the automatic use of small, low-cost computers connected to the internet for everyday tasks. CAIL lab studies causal inference, machine learning, and artificial intelligence methods with an interest in contributing to a deeper understanding of the cause and context in data-intensive healthcare and ubiquitous computing environments. We address new and fundamental challenges related to causal discovery, causal inferences, prior-knowledge infusion, predictive modeling, and associated uncertainty. Our work is collaborative and interdisciplinary in nature and focuses on societal impacts. Our research has applications in healthcare, pervasive computing, rehabilitation engineering, occupational science, social science, language, etc.
A Transfer Learning Approach to Surface Detection for Accessible Routing for Wheelchair Users
Valeria Mokrenko, Haoxiang Yu, Vaskar Raychoudhury, Janick Edinger, Roger O Smith, Md Osman Gani
2021 IEEE 45th Annual Computers, Software, and Applications Conference (COMPSAC)
Yours truly? Survey on accessibility of our personal data in the connected world
Manh Nguyen, Md Osman Gani, Vaskar Raychoudhury
2019 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)
A novel approach to complex human activity recognition
Md Osman Gani
Marquette University
A light weight smartphone based human activity recognition system with high accuracy
Md Osman Gani, Taskina Fayezeen, Richard J Povinelli, Roger O Smith, Muhammad Arif, Ahmed J Kattan, Sheikh Iqbal Ahamed
Journal of Network and Computer Applications
A survey of taxi ride sharing system architectures
Shrawani Silwal, Md Osman Gani, Vaskar Raychoudhury
2019 IEEE International Conference on Smart Computing (SMARTCOMP)