Author: Stephen Purpura

I'm an Information Science Ph.D. student at Cornell University where I am working on providing adaptable search experiences. An adaptable search experience is one that changes as necessary to facilitate effectively finding information or entertainment. This research requires me to understand and participate in the research areas of Natural Language Processing, Machine Learning/Artificial Intelligence, Human-Computer Interaction, Information Retrieval, and Information Theory.

Most people would classify me as an empiricist -- my Master's Thesis used statistical matching methods to tease actionable information from a large online poll -- but I primarily work on problems with rich non-empirical traditions and practices in the Political, Public Policy, Economics, Legal, and Communication Analysis domains. This is a challenging balance because the research culture of machine learning is very different than the cultures of HCI or Public Policy. So, rather than a rationalist or empirical perspective, I draw from methods across disciplines in a hybrid approach.

Finally, my research and teaching are motivated by my work history with information technology. Since 1990, I've worked in a variety of positions, including software engineer, software development manager, program manager, and product manager. My past influences my choice of projects and also my interaction with students. A big part of my teaching is related to effective methods for building real systems. This focus on effectiveness draws students enrolled in MBA, Information Science, and Computer Science programs to work with me on projects related to Product Management, Information Science, and Software Engineering. Usually, I try to limit the diversity of these projects, but I've recently had fun experiments in social media and mobile computing in addition to machine learning, natural language processing, and information retrieval.