Matthew Giamou

Dr. Matthew Giamou is an Assistant Professor in the Department of Computing and Software at McMaster University, leading the Autonomous Robotics and Convex Optimization (ARCO) Lab. His research focuses on global optimization, sensor calibration, SLAM, and motion planning, with applications in robotics, manufacturing, and geosciences. He emphasizes developing interpretable and safety-certifiable algorithms as alternatives to deep learning.

Education: B.A.Sc. in Engineering Science (University of Toronto, 2015), M.Sc. in Aeronautics and Astronautics (MIT, 2017), Ph.D. in Aerospace Studies (University of Toronto, 2022).
Postdoctoral work at Northeastern University’s Institute for Experiential Robotics.

Research interests include mobile robotics, convex optimization, computer vision, and machine learning. ARCO Lab’s current projects involve scalable spatiotemporal algorithms for robust perception and planning. His work bridges theoretical optimization with practical robotics challenges like multi-robot communication and medical robotics.

Publications reflect a focus on geometric robotics, optimization methods, and sensor systems. Key themes include inverse kinematics, SLAM, and calibration algorithms with formal safety guarantees.

• Scientific Awards:
  - 2020 IEEE IROS Best Workshop Paper Award
  - 2020 Robotics: Science and Systems Best Student Paper
  - 2019 RBC AI Graduate Fellowship

Advising: Actively mentoring graduate students in robotics and optimization. Lab activities emphasize collaboration with non-experts in fields like space exploration and manufacturing. Teaching includes advanced courses on group theory for optimization and machine learning (CAS 752).

Lab Infrastructure: ARCO Lab is housed in ABB C536, with facilities for robotics prototyping and algorithm testing. Ongoing projects aim to deploy safe autonomy tools for diverse end-user domains.