12/07/2021
By Mary Lou Kelly

The Operations & Information Systems Department (OIS) in the Manning School of Business cordially invites you to attend a doctoral dissertation proposal defense by Murtaza Nasir on "Essays on Data Science & Analytics: Methodology Development and Applications."

Date: Monday, Dec. 13
Time: 2 to 3 p.m.
Location: Pulichino Tong Business Center, Room 462
Dissertation Proposal Title: Essays on Data Science & Analytics: Methodology Development and Applications

Dissertation Advisor: Asil Oztekin, Associate Professor, OIS
Co-Advisor: Nichalin Summerfield, Assistant Professor, OIS

Dissertation Committee Members:

  • Yao Chen, Professor, OIS
  • Serhat Simsek, Montclair State University

Machine learning (ML) has become increasingly important in the domain of business decision making and understanding business problems. In this work, we propose novel machine learning methodologies that enable us to overcome some challenges with working with imbalanced and complex datasets. Furthermore, we present some of our research work that demonstrate applications of these as well as some existing ML methodologies as benchmark. The first methodology, titled “SANSA-a synthetic average neighborhood sampling algorithm”, significantly improves on existing synthetic data generation algorithms for imbalanced learning by providing a novel tuning parameter that allows the algorithm to adapt to a wide variety of datasets, which yields super results to existing methods. The second methodology presented, titled “SIFT, a simple interaction finding technique”, presents a novel methodology to make black-box machine learning models more transparent by analyzing and presenting how any given trained model makes decisions. The methodology can be used to identify novel interactions between predictors as well as any potential biases in the model. In the remaining chapters of this work, we show some applications of these novel algorithms, i.e. SANSA and SIFT. We present chapters on using machine-learning based analytics to understand important factors and their interactions for successful completion of substance use disorder treatment as well as for various COVID-19 diagnostic as well as prognostic outcomes.