Overview
Background
Mahsa Baktashmotlagh is currently an Associate Professor and an ARC Future Fellow at UQ, developing machine learning techniques applied in: Visual data analysis, Biomedical data (Antibacterial activity prediction), and Cyber Security.
Availability
- Associate Professor Mahsa Baktashmotlagh is:
- Available for supervision
Qualifications
- Doctor of Philosophy, The University of Queensland
Works
Search Professor Mahsa Baktashmotlagh’s works on UQ eSpace
2013
Conference Publication
Non-linear stationary subspace analysis with application to video classification
Baktashmotlagh, Mahsa, Harandi, Mehrtash T., Bigdeli, Abbas, Lovell, Brian C. and Salzmann, Mathieu (2013). Non-linear stationary subspace analysis with application to video classification. 30th International Conference on Machine Learning, Atlanta, GA, United States, 16 - 21 June 2013. Germany: International Machine Learning Society (IMLS).
2013
Conference Publication
Unsupervised domain adaptation by Domain Invariant Projection
Baktashmotlagh, Mahsa, Harandi, Mehrtash T., Lovell, Brian C. and Salzmann, Mathieu (2013). Unsupervised domain adaptation by Domain Invariant Projection. 2013 IEEE International Conference on Computer Vision (ICCV), Sydney, Australia, 1-8 December 2013. Piscataway, NJ, United States: IEEE. doi: 10.1109/ICCV.2013.100
2012
Conference Publication
A wireless mesh sensor network for hazard and safety monitoring at the Port of Brisbane
Ahmadi, Amin, Bigdeli, Abbas, Baktashmotlagh, Mahsa and Lovell, Brian C. (2012). A wireless mesh sensor network for hazard and safety monitoring at the Port of Brisbane. 37th Annual IEEE Conference on Local Computer Networks (LCN 2012), Clearwater, FL, United States, 22-25 October 2012. Washington, DC, United States: IEEE. doi: 10.1109/LCN.2012.6423601
2012
Conference Publication
Directional space-time oriented gradients for 3D visual pattern analysis
Norouznezhad, Ehsan, Harandi, Mehrtash T., Bigdeli, Abbas, Baktash, Mahsa, Postula, Adam and Lovell, Brian C. (2012). Directional space-time oriented gradients for 3D visual pattern analysis. 12th European Conference on Computer Vision, ECCV 2012, Florence, Italy, 7 - 13 October 2012. Heidelberg, Germany: Springer. doi: 10.1007/978-3-642-33712-3_53
2011
Conference Publication
Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs
Baktashmotlagh, Mahsa, Bigdeli, Abbas and Lovell, Brian C. (2011). Dynamic resource aware sensor networks: Integration of sensor cloud and ERPs. 8th IEEE International Conference on Advanced Video and Signal-Based Surveillance (AVSS), 2011, Klagenfurt, Austria, 30 August - 2 September 2011. Piscataway, NJ, United States: IEEE. doi: 10.1109/AVSS.2011.6027375
Supervision
Availability
- Associate Professor Mahsa Baktashmotlagh is:
- Available for supervision
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Supervision history
Current supervision
-
Doctor Philosophy
Enhancing Safety and Reliability of machine learning models using lifelong multi-modal learning
Principal Advisor
Other advisors: Professor Helen Huang, Dr Yadan Luo
-
Doctor Philosophy
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
Doctor Philosophy
Semantic Segmentation for Crop Health and Damage Assessment
Principal Advisor
-
Doctor Philosophy
Generalizing Implicit Representations for Robotics Manipulation of Articulated Objects
Principal Advisor
Other advisors: Dr Peyman Moghadam
-
Doctor Philosophy
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
Doctor Philosophy
Revisiting Assumptions and Evaluation Metrics in Domain Generalization
Principal Advisor
-
Doctor Philosophy
Enhancing Robustness and Generalizability in Computational Models
Principal Advisor
Other advisors: Dr Xin Yu
-
Doctor Philosophy
Exploring Facets of Model Generalizability on Out-of-Distribution Data
Principal Advisor
Other advisors: Professor Guido Zuccon
-
Doctor Philosophy
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
Doctor Philosophy
Enhancing Plant Phenotyping Accuracy through Analysing Video Data
Principal Advisor
Other advisors: Professor Scott Chapman, Dr Yadan Luo
-
Doctor Philosophy
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
Doctor Philosophy
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
Doctor Philosophy
Revisiting Assumptions and Evaluation Metrics in Domain Generalization
Principal Advisor
-
Doctor Philosophy
The role of duality in machine learning and computer vision.
Associate Advisor
Other advisors: Professor Brian Lovell
-
Doctor Philosophy
Automatic Retinal Health Monitoring through Multi-modal Medical Imaging
Associate Advisor
Other advisors: Dr Xin Yu
-
-
Doctor Philosophy
Towards Analysis of Contextual Melanoma Indicators and Identification of Total-Body Ugly Duckling Lesions with Deep Neural Networks
Associate Advisor
Other advisors: Dr Shakes Chandra
-
Doctor Philosophy
Two way Auslan Translation
Associate Advisor
-
Doctor Philosophy
Unsupervised Domain Adaptation on 3D Object Detection and Segmentation
Associate Advisor
Other advisors: Professor Helen Huang, Dr Yadan Luo
Completed supervision
-
2024
Doctor Philosophy
Exploring Facets of Model Generalizability on Out-of-Distribution Data
Principal Advisor
Other advisors: Professor Guido Zuccon
-
2023
Doctor Philosophy
Monocular 3D Reconstruction: Shape Representation, Scalability and Generalization.
Principal Advisor
Other advisors: Dr Yadan Luo
-
2022
Doctor Philosophy
On Encoding Causality for Natural Language Understanding
Principal Advisor
Other advisors: Professor Xue Li
-
2024
Doctor Philosophy
Towards Analysis of Contextual Melanoma Indicators and Identification of Total-Body Ugly Duckling Lesions with Deep Neural Networks
Associate Advisor
Other advisors: Dr Shakes Chandra
-
2023
Doctor Philosophy
On improving vision model transferability to address domain shift in an open world
Associate Advisor
Other advisors: Professor Helen Huang
-
2021
Doctor Philosophy
Visual Learning from Imperfect Data via Inductive Bias Modelling
Associate Advisor
Other advisors: Professor Helen Huang
Media
Enquiries
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