ISyE faculty and students are working on theoretical and methodological advances in analytics and machine learning, as well as with companies and organizations to bring state-of-the-art analytics and big-data research to bear on real-life problems.
Analytics and machine learning have quickly become key business strategies amd tools for making better decisions.. Data streams are growing rapidly in size, speed, and diversity. When you add in high-performance computing capacity and advanced statistical and operations research algorithms, the combination becomes very powerful. The perspective and skills of analytics are in high demand in a wide range of industries, and the need for fundamental research in analytics and machine learning related areas is significant.
To improve our ability to analyze, predict, and optimize based on fast-moving and massive-scale data sets, we are working on cutting-edge research in many aspects of the theory, methodology, modeling, and application of modern analytics.
Current Methodological Research Areas
- Discovering new techniques for data mining, machine learning, compressed sensing, matrix completion, dimension reduction, cluster analysis, change-point and anomaly detection, and scalable and adaptive monitoring for high-dimensional streaming data.
- Developing parallelizable statistical optimization methods to exploit high-performance computing architecture, investigating computational issues associated with statistical principles and modeling in a big-data environment, and creating expert-guided and hierarchical process control approaches.
- Making fundamental discoveries in large-scale convex optimization, discrete optimization, and stochastic and dynamic decision-making.
- Developing new systematic procedures and methods for capture, storage, cleaning, security, cross-referencing, searching, visualization, and analysis of information.
Highlights of Research in Application Areas
We are conducting research in application areas that include aspects of business and industry like strategic planning and dynamic operating decision-making in:
- Supply chains, transportation, logistics, manufacturing, material handling, quality and reliability, and warehousing.
- Health care, including health policy and scarce resource allocation, quality and delivery of care, disease modeling and intervention strategies, hospital/clinic design and operation, preparedness, emergency response, equity, and predictive health.
- Energy-related planning, prediction, and operational decision-making.
- Finance, investment, sales, and computational advertising.
- Scientific discovery.
- Sports analytics, including decision-making, scheduling, and prediction.
