In recent years, we have been observing a revolution in the use of data science, machine-learning algorithms, and optimization methods in “softer” areas such as human resources (HR), human behavior, mental illness, and learning disabilities, as well as in more conventional areas such as manufacturing and logistics systems. Despite these advances, there are still substantial gaps in our understanding of how users can implement machine-learning algorithms and optimization methods to address challenges in these domains. A user’s willingness to utilize the outputs of machine-learning algorithms is likely to be predicated upon his or her ability to understand the model’s behavior, rather than perceiving it as a black box. My research is geared towards addressing the above challenges, and in particular, towards understanding how to enhance the usability as well the explainability of these algorithms’ outputs while solving problems from these domains. My two main (and complementary) methods of research are: (i) the implementation and adaptation of existing interpretable machine-learning algorithms, i.e., models in which the results can provide practical insights into problems from various domains, and (ii) the development of new optimization methods and interpretable machine-learning algorithms that outperform other known algorithms in these domains.
- Machine learning and business analytics
- Machine learning for medical diagnosis
- Human resource analytics
- Applications of information theory to industrial and service systems
- Modeling and solution methods for optimal control problems in stochastic environment