I'm a 4th year Ph.D. student in the Electrical and Computer Engineering Department at Carnegie Mellon University, Pittsburgh, advised by Anind Dey. My research interest lies in using applied Machine Learning, especially Location Prediction, to find solutions for the ever growing resource consumption problem of our society. By predicting a user's movements through open and closed spaces I believe it is possible to develop systems and applications that support Sustainability (esp. temperature control). Possible applications can be but are not limited to: automatic control of appliances, lights, or temperature regulation, elderly care, emergency response, but also pervasive systems that aim at changing a user's behavior.
My research uses lessons learned in Machine Learning and combines them with Human-Computer-Interaction (HCI) methods to not only explore, design, and develop sustainabile solutions that have an impact on current consumption, but also to make sure that the developed systems are well accepted by users.
To satisfy the latter goal I'm also intersted in how Machine Learning and ambient sensors can be used to detect a user's current comfort level and predict the impact of changing environmental variables (light, temperature, etc.) on the comfort of users. To achieve this it is important to understand impact factors on a person's comfort level and find ways to not only measure them, but also design systems that learn these highly individual factors.
I'm part of Anind Dey's Ubicomp Lab group at CMU. We are focusing on usable machine learning to solve real-world problems such as sustainability and health care. In addition I'm an external collaborator at the Interactions Lab at UNIST working with Prof. Ian Oakley.
I received my German Diplom (MSc equivalent) in Computer Science with a minor in Math from RWTH Aachen University, Germany in 2009. I received an FCT scholarship to support my research from the Portuguese science foundation in 2010.