Connor is currently leading the development of digital learning applications for the SCx program at MIT. His primary focus is on generating tools and systems that allow online students to learn more effectively.
Connor’s team researches user patterns, develops applications and implements them into the edX learning environment. These applications range from simple tools that engage students to advanced machine learning algorithms that ensure academic honesty. His team also creates other point and click tools that write back end scripts for SCx course staff.
Outside of development, Connor has instructed Statistics and Supply Chain Leadership at MIT. He has also helped to develop and teach content in optimization, statistics, regression, database systems, SQL and Machine Learning as part of the MIT SCx courses in edX.
Connor’s main research interests include prescriptive analytics and machine learning. His current research is focused on finding patterns and improving student engagement in the MITx program. His past research has been focused on developing efficient data structures and algorithms to facilitate horizontal collaboration on a large scale. Connor has also worked with the MIT Supply Chain Strategy Lab to help identify underlying structures that affect the success of multi-functional teams.