About Logan
Logan teaches Luddy's large introductory course, courses covering project management and databases, and the informatics capstone course. In addition to teaching, Logan administers the department’s internship for credit opportunities and is engaged in pedagogical research with collaborators around the campus funded by the Association of American Universities. He has been with the Luddy School since 2016.

Logan J Paul
Senior LecturerInformatics Scheduler
School Curriculum Committee
Email: lopaul@iu.edu
Office: Myles Brand Hall | Room: 203
Website: https://linkedin.com/in/loganpaul
Office Hours
Office Hours for Spring 2025
All office hours take place in Myles Brand 205/205 (Logan's Office).
Weeks 01 (01/13/2025 - 01/17/2025)
T 01:00 - 01:50 PM
W 10:00 - 10:50 AM
Week 02 (01/20/2025 - 01/24/2025)
F 12:00 - 12:50 PM
Week 03 (01/27/2025 - 01/31/2025)
M 01:00 - 01:50 PM
T 01:00 - 01:50 PM
Week 04 to Week 09 (02/03/2025 - 03/14/2025)
M 01:00 - 01:50 PM
T 11:00 - 11:50 AM
Week 10 (03/24/2025 - 03/28/2025)
W 10:00 - 10:50 AM
W 02:30 - 03:20 PM
Week 11 (03/31/2025 - 04/04/2025)
No office hours; at a conference.
Week 12 to Week 15 (04/07/2025 - 05/02/2025)
T 09:00 - 10:50 AM
Week 16 (05/05/2025 - 05/09/2025)
Finals week; no office hours.
If you are unable to make office hours, please send an email with your availability (blocks) for at least five business days and a brief agenda for why you need to meet.
Education
- M.S. in Information Science at Indiana University, Bloomington, 2015
- B.S. in Informatics at Indiana University, Bloomington, 2013
Courses Taught at Luddy
- INFO I101 Introduction to Informatics
- INFO I303 Organizational Informatics
- INFO I308 Information Representation
- INFO I491/391 Informatics Internships
- INFO I494/I495 Design and Development of an Information System
Biography
Luddy Research Areas
- Crosscutting
- Teaching and Learning @Luddy
- Departmental
- Business Data Analytics
- Data Science for Applications
- Database and Information Systems
- Databases and Data Mining
- Social and Ethical Aspects of Data Science
- Data Science