Norma Grubb

Adjunct Professor

2018 Doctoral Prize Recipient for the best dissertation in the Information Science and Technology Program, School of Information Studies.

Norma is a Doctor of Professional Studies in Information Management (DPS, iSchool, Syracuse University) with dissertation work on computational linguistics. Her dissertation contributed state-of-the-art results (88 F1) in modeling negation prediction in social media data using supervised machine learning.

As a practitioner, Norma developed an extensive career as an information management and analytics professional, working for the private and public sectors in three countries (Argentina, Canada and the US). She is currently an Analytics and Artificial Intelligent Director for Cummins Inc, a global market leader in engines and power systems.

Through her career, Norma based the success of her projects on the design and implementation of applied research methods in order to make decisions that are based on rigorous evidence and scientific reasoning.


I am a fitness and nutrition enthusiast. I also enjoy very much listening to music, particularly American southern blues. I live in Columbus, Indiana, in a wonderful house on a lake, with my husband and a variety of wild pets such as rabbits, squirrels, raccoons, and the occasional possum.


My research focus is twofold. Primarily, I investigate the use of artificial intelligence models for predictive maintenance purposes, i.e. detecting emerging issues in products on the field fast and address those preventively before they impact the end user. My research touches the product quality and manufacturing analytics functions in the company. Secondly, and regarding natural language processing, I’m working on a solution that uses named entity recognition and linking for information retrieval and machine learning. Using several word embedding methods, the goal is to automatically identify key business vocabulary (such as product names and their components) to avoid manually intensive tagging. The final output becomes input for both information retrieval systems (i.e. search engine indexes) as well as feature buckets for optimizing machine learning models. In addition to that line of work, I investigate the advances of deep learning for corpus intelligence and management.


Besides my appointment as Adjunct Professor for computational linguistics courses at the iSchool, I designed the curricula and learning approach for “Data for Effective Policy Making”, an edX-IDB MOOC on foundations of statistical procedures and data literacy for evidence-based decision making processes by public policy makers, primarily for Latin American and Caribbean government audiences but readily applicable elsewhere.