Dr Frederic Maire
Faculty of Engineering,
School of Electrical Engineering & Robotics
Biography
Academic background- Completed in parallel a 5-year Computer Science Engineering at ENSEIRB and a 5-year degree in pure math at University Pierre et Marie Curie
- Did a PhD in Discrete Math under the supervision of Claude Berge on graph optimization problems
Research interests
- Joined QUT mid-1995 as an associate lecturer to work on pattern recognition and neurocomputing.
- Research interests shifted toward machine learning, computer vision and robotics
- Note for current and prospective students: for project topics (from 4th year to PhD), visit https://wiki.qut.edu.au/display/cyphy/Student+project+topics+proposed+by+Frederic+Maire
Personal details
Positions
- Senior Lecturer
Faculty of Engineering,
School of Electrical Engineering & Robotics
Keywords
Artificial Intelligence, Computer Vision, Machine Learning, Pattern Recognition, Robotics
Research field
Artificial Intelligence and Image Processing, Interdisciplinary Engineering
Field of Research code, Australian and New Zealand Standard Research Classification (ANZSRC), 2008
Qualifications
- PhD (Universite de Paris VI)
Professional memberships and associations
- Note for current and prospective students: for project topics (from 4th year to PhD), visit https://wiki.qut.edu.au/display/cyphy/Student+project+topics+proposed+by+Frederic+Maire
- CyPhy member https://wiki.qut.edu.au/display/cyphy/Frederic+Maire
- IEEE member
Teaching
My approach to teaching AI is to focus on providing solid foundations on search techniques and the principles behind machine learning. The assignments are carefully chosen so that students can develop a good understanding of the capabilities and limits of the approaches taught in the lectures.
Note for current and prospective students: for project topics (from 4th year to PhD), visit https://wiki.qut.edu.au/display/cyphy/Student+project+topics+proposed+by+Frederic+Maire
Experience
My early work on neural networks and AI for strategic games did not involve industry partners but in the last few years my projects have become more applied:
- Development of deep learning based software to monitor dugong populations.
- Robotic solution for textile recycling
- Smart bin
- I have been involved through the Rail CRC in the development of vision based techniques to detect near-miss incidents at railroad crossings.
- With colleagues from CARRS-Q, I have applied machine learning techniques to driver safety problems.
- I have also worked on projects for Mining3.
Publications
QUT ePrints
For more publications by Frederic, explore their research in QUT ePrints (our digital repository).
Supervision
Completed supervisions (Doctorate)
- Driver Stress Level Detection Based on Multimodal Measurements (2019)
- Emotion Classification Using Advanced Machine Learning Techniques Applied to Wearable Physiological Signals Data (2019)
- Video Analytics for the Detection of Near-Miss Incidents at Railway Level Crossings and Signal Passed at Danger Events (2017)
- An assessment system for evaluation of driving competencies (2011)
- Computational Approaches to the Visual Validation of 3D Virtual Environments (2011)
- Vision-based Topological Mapping and Localisation (2010)
- Bearing-only SLAM - A Vision-Based Navigation System for Autonomous Robots (2009)
- Robot Navigation in Sensor Space (2009)
- Algorithmic Approaches for Playing and Solving Shannon Games (2008)
- Automatic Generation and Evaluation of Recombination Games (2008)