Optimization theory and algorithms are foundational building blocks of Operations Research and Data Science.
This is a major research area in the IEOR department, with significant emphasis on both discrete optimization (including integer and combinatorial optimization) and continuous optimization. Here, the main goal is to design efficient near-optimal algorithms for large-scale problems with provable performance bounds and advance the theoretical frontiers for such problems.
Another important aspect especially from the perspective of practical applications is handling uncertainty in input data. This is a very active research area in IEOR with focus on various approaches including stochastic, robust, online and dynamic optimization. Optimization algorithms are central to most computational problems in data science including statistical estimation, machine learning, and business analytics, and find applications in most problems in practice.
The research in IEOR in this area spans a broad spectrum of application areas including scheduling, matching markets, energy, health and financial and business analytics.
Our faculty and students collaborate regularly with colleagues in Computer Science and other engineering departments and play an active role in the Data Science Institute.
