Dr. Pokutta's research concentrates on combinatorial optimization and polyhedral combinatorics, and in particular focuses on cutting-plane methods and extended formulations. His industry research interests are in optimization and machine learning in the context of analytics with a focus on real-world applications, both in established industries as well as in emerging technologies. Application areas include but are not limited to supply chain management, finance, cyber-physical systems, and predictive analytics. To date, Dr. Pokutta has successfully deployed analytics methodology in 20+ real-world projects.
Dr. Serban's research interests on Health Analytics span various dimensions including large-scale data representation with a focus on processing patient-level health information into data features dictated by various considerations, such as data-generation process and data sparsity; machine learning and statistical modeling to acquire knowledge from a compilation of health-related datasets with a focus on geographic and temporal variations; and integration of statistical estimates into informed decision making in healthcare delivery and into managing the complexity of the healthcare system.
Dr. Lee works in the area of mathematical programming and large-scale computational algorithms with a primary emphasis on medical/healthcare decision analysis and logistics operations management. She tackles challenging problems in health systems and biomedicine through systems modeling, algorithm and software design, and decision theory analysis. Specific research areas include health risk prediction, early disease prediction and diagnosis, optimal treatment strategies and drug delivery, healthcare outcome analysis and treatment prediction, public health and medical preparedness, large-scale healthcare/medical decision analysis and quality improvement.