A central focus in operations research and data science is the study of real-world problems with the aim of making better decisions.  Analytical techniques, such as mathematical programming, machine learning, data mining, probability and statistics, and mathematical modeling and simulation, are used to formulate and solve mathematical models and optimization problems.  A common feature of the research is the collection and use of data, ranging from big data (massive collections of data) and network domain data (e.g. street maps) to abstract knowledge and assumptions about how processes work.  Applications of operations research and data science abound in many areas of engineering, business, science and medicine, leading to collaborative interdisciplinary research that is both interesting and challenging.

The research of Rensselaer faculty is driven by many compelling real-world problems.  Current work includes developing effective emergency responses to natural disasters, tracking infectious diseases (such as tuberculosis) so that they may be controlled effectively, and applying advanced data analysis for more reliable and efficient health-care solutions.  Our faculty are also using data analysis and modeling to increase the effectiveness of tracking motions in videos, to improve energy sustainability by increasing wind turbine output, and to analyze medical surfaces.  Methods of nonlinear bi-level programming, optimization, probabilistic and computational differential geometry have been developed for general data analysis that are applicable to wide range of problems in data science.

Faculty Researchers:

• John Mitchell
• Yangyang Xu
• Kristin Bennett