Christian Muise
Associate Professor
Kingston, Ontario, Canada
Summary
👤
Senior🎓
Top SchoolChristian Muise is an associate professor and researcher with two decades of experience at the intersection of automated planning, multi-agent systems, and optimization, currently leading MuLab at Queen’s University in Kingston. He completed a PhD at the University of Toronto and held research positions at the University of Melbourne, MIT CSAIL, and the MIT‑IBM Watson AI Lab, where he focused on robust plan synthesis, human-agent collaboration, and OR-based decision support for temporal and networked problems. His work blends deep theoretical knowledge with practical algorithm engineering—evidenced by contributions to scheduling solvers and heuristic large-neighbourhood search in open-source projects—and a history of supervising and scaling research groups. Comfortable moving between academia and industry, he has a track record of securing funding, organizing collaborative visits, and translating complex planning models into deployable decision-support tools.
20 years of coding experience
6 years of employment as a software developer
Computer Science, Computer Science, Computer Science, Computer Science at Carleton UniversityDoctor of Philosophy (PhD), Artificial Intelligence, Doctor of Philosophy (PhD), Artificial Intelligence at University of Toronto
Stackoverflow
625reputation
29kreached
30answers
4questions
Github Skills (18)
algorithms10
python10
scheduler10
scheduling10
sched10
optimization10
data-structures8
data-structure8
syntax6
planning6
user-input6
sql6
dns6
workspace6
phpmyadmin6
Programming languages (18)
Github contributions (5)
aosabook/500lines
Feb 2014 - Nov 2014
500 Lines or Less
Role in this project:
Back-end Developer
Back-end DeveloperContributions:29 commits, 6 comments in 9 months
Contributions summary:Christian primarily contributed to the development of a flow shop scheduling solver. Their work involved parsing problem data, implementing random solution generation, and evaluating solution quality. The user progressively improved the solver by adding visualization features, fixing bugs related to idle time computation, and implementing a large neighbourhood search strategy with dynamic heuristic selection. Their contributions centered on algorithmic optimization and heuristic development for a specific scheduling problem.
linesmodular-designlistsjava
QuMuLab/planner-for-relevant-policies
Jun 2012 - Oct 2022
Contributions:59 commits, 4 PRs, 16 pushes in 10 years 6 months
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