I'm a 5th year Ph.D. student in the Electrical and Computer Engineering Department at Carnegie Mellon University, Pittsburgh, advised by Anind Dey, Ian Oakley, and Jennifer Mankoff. My research interest lies in using applied Machine Learning to understand, model, and predict human behavior, especially structured spatiotemporal behavior. I apply the lessons learned 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 Human-Computer-Interaction (HCI) methods and combines them with applied Machine Learning 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 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.