Data Analytics for Complex Human Behavior Lab

At Data Analytics for Complex Human Behavior (DACHB) Lab, we develop and apply data science techniques to complex problems. While we are computer scientists, our work is often interdisciplinary and our lab welcomes collaborations.

Our Lab

Our lab started in 2015. We use modern workstations and massive storage to generate insight from large datasets. Check our current students, alumni and collaborators.

Our Director

Dr. Philippe J. Giabbanelli joined Miami University as associate professor in Fall 2019. Previously, he worked at several nationally-ranked American Universities and was a researcher at the University of Cambridge (UK). He teaches data science courses and supervises research students.

Our Values

As members of the scientific and intellectual community at Miami University, we adhere to the university’s values. In particular, we encourage all members of the lab to be active within this community, and we place a strong emphasis on supporting student success. While we are a team working towards common objectives, we also understand that members are driven by different goals. To support our students, we seek to identify each individual’s goals and development needs such that they can receive the right mentorship.

Our research vision is built on the following three pillars. First, our research is highly Interdisciplinary. Being in the field of health informatics, our students and collaborators come from a variety of disciplines such as computer science, public health, or psychology. We encourage our members to develop their core technical skills in data science and health informatics while learning the right amount about other disciplines in order to better communicate and work together in an interdisciplinary setting. Second, we aim to be highly Innovative in our research. This can be either in the form of methodological contributions (e.g., in machine learning, discrete simulations, or interactive visualizations) or findings in specific fields (e.g., using simulations to find potential public policies). In other words, if a method is under-utilized in a field, then we see value in explaining and demonstrating its potential. Similarly, when we develop a new method, it is always motivated by a practical problem; we may publish papers on the method itself but eventually, we aim to see it being applied to the problem at hand. Finally, we value Initiatives. Our members are always encouraged to suggest potential research projects in health informatics. When good questions are identified and adequate data supports the investigation, we then seek more manpower to ensure that we can deliver.