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
2020
Book Chapter
On minimum discrepancy estimation for deep domain adaptation
Rahman, Mohammad Mahfujur, Fookes, Clinton, Baktashmotlagh, Mahsa and Sridharan, Sridha (2020). On minimum discrepancy estimation for deep domain adaptation. Domain adaptation for visual understanding. (pp. 81-94) edited by Richa Singh, Mayank Vatsa, Vishal M. Patel and Nalini Ratha. Cham, Switzerland: Springer International Publishing. doi: 10.1007/978-3-030-30671-7_6
2020
Conference Publication
Domain Adaptative Causality Encode
Moghimifar, Farhad, Haffari, Gholamreza and Baktashmotlagh, Mahsa (2020). Domain Adaptative Causality Encode. 18th Annual Workshop of the Australasian Language Technology Association, Online, 14–15 January 2021. Sydney, NSW Australia: Australasian Language Technology Association.
2019
Journal Article
Visualizing student opinion through text analysis
Cunningham-Nelson, Samuel, Baktashmotlagh, Mahsa and Boles, Wageeh (2019). Visualizing student opinion through text analysis. IEEE Transactions on Education, 62 (4) 8759085, 305-311. doi: 10.1109/TE.2019.2924385
2019
Conference Publication
Object graph networks for spatial language grounding
Hawkins, Philip, Maire, Frederic, Denman, Simon and Baktashmotlagh, Mahsa (2019). Object graph networks for spatial language grounding. APRS International Conference on Digital Image Computing - Techniques and Applications (DICTA), Perth, Australia, 2-4 December 2019. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/DICTA47822.2019.8946101
2019
Conference Publication
Multi-Component Image Translation for Deep Domain Generalization
Rahman, Mohammad Mahfujur, Fookes, Clinton, Baktashmotlagh, Mahsa and Sridharan, Sridha (2019). Multi-Component Image Translation for Deep Domain Generalization. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI United States, 7-11 January 2019. Piscataway, NJ United States: IEEE. doi: 10.1109/wacv.2019.00067
2019
Conference Publication
Learning factorized representations for open-set domain adaptation
Baktashmotlagh, Mahsa, Faraki, Masoud, Drummond, Tom and Salzmann, Mathieu (2019). Learning factorized representations for open-set domain adaptation. 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, United States, 6 - 9 May 2019. International Conference on Learning Representations, ICLR.
2017
Conference Publication
Speaker verification with multi-run ICA based speech enhancement
Al-Ali, Ahmed Kamil Hasan, Dean, David, Senadji, Bouchra, Baktashmotlagh, Mahsa and Chandran, Vinod (2017). Speaker verification with multi-run ICA based speech enhancement. 2017 11th International Conference on Signal Processing and Communication Systems (ICSPCS), Gold Coast, QLD Australia, 13-15 December 2017. Piscataway, NJ United States: IEEE. doi: 10.1109/icspcs.2017.8270505
2017
Conference Publication
Deep discovery of facial motions using a shallow embedding layer
Ghasemi, Afsaneh, Baktashmotlagh, Mahsa, Denman, Simon, Sridharan, Sridha, Tien, Dung Nguyen and Fookes, Clinton (2017). Deep discovery of facial motions using a shallow embedding layer. 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 17-20 September 2017. Piscataway, NJ, United States: IEEE. doi: 10.1109/icip.2017.8296545
2017
Conference Publication
From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach
Erfani, Sarah, Baktashmotlagh, Mahsa, Moshtaghi, Masud, Nguyen, Vinh, Leckie, Christopher, Bailey, James and Ramamohanarao, Kotagiri (2017). From Shared Subspaces to Shared Landmarks: A Robust Multi-Source Classification Approach. Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA United States, 4-9 February 2017. Palo Alto, CA United States: Association for the Advancement of Artificial Intelligence. doi: 10.1609/aaai.v31i1.10870
2017
Book Chapter
Learning Domain Invariant Embeddings by Matching Distributions
Baktashmotlagh, Mahsa, Harandi, Mehrtash and Salzmann, Mathieu (2017). Learning Domain Invariant Embeddings by Matching Distributions. Domain Adaptation in Computer Vision Applications. (pp. 95-114) Cham, Switzerland: Springer. doi: 10.1007/978-3-319-58347-1_5
2016
Journal Article
Distribution-matching embedding for visual domain adaptation
Baktashmotlagh, Mahsa, Harandi, Mehrtash and Salzmann, Mathieu (2016). Distribution-matching embedding for visual domain adaptation. Journal of Machine Learning Research, 17 108, 1-30.
2016
Conference Publication
R1STM: One-class support tensor machine with randomised kernel
Erfani, Sarah M., Baktashmotlagh, Mahsa, Rajasegarad, Sutharshan, Nguyen, Vinh, Leckie, Christopher, Bailey, James and Ramamohanarao, Kotagiri (2016). R1STM: One-class support tensor machine with randomised kernel. 2016 SIAM International Conference on Data Mining (SDM), Miami, FL United States, 5-7 May 2016. Philadelphia, PA United States: Society for Industrial and Applied Mathematics. doi: 10.1137/1.9781611974348.23
2016
Journal Article
Structure-Activity Studies of Cysteine-Rich α-Conotoxins that Inhibit High Voltage-Activated Calcium Channels via GABAB Receptor Activation Reveal a Minimal Functional Motif
Carstens, Bodil B., Berecki, Geza, Daniel, James T., Lee, Han Siean, Jackson, Kathryn A. V., Tae, Han-Shen, Sadeghi, Mahsa, Castro, Joel, O'Donnell, Tracy, Deiteren, Annemie, Brierley, Stuart M., Craik, David J., Adams, David J. and Clark, Richard J. (2016). Structure-Activity Studies of Cysteine-Rich α-Conotoxins that Inhibit High Voltage-Activated Calcium Channels via GABAB Receptor Activation Reveal a Minimal Functional Motif. Angewandte Chemie - International Edition, 55 (15), 4692-4696. doi: 10.1002/anie.201600297
2016
Conference Publication
Robust domain generalisation by enforcing distribution invariance
Erfani, Sarah M., Baktashmotlagh, Mahsa, Moshtaghi, Masud, Nguyen, Vinh, Leckie, Christopher, Bailey, James and Ramamohanarao, Kotagiri (2016). Robust domain generalisation by enforcing distribution invariance. 25th International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, United States, 9-15 July 2016. Palo Alto, CA United States: AAAI Press / International Joint Conferences on Artificial Intelligence.
2015
Conference Publication
Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs
Harandi, Mehrtash, Salzmann, Mathieu and Baktashmotlagh, Mahsa (2015). Beyond Gauss: Image-Set Matching on the Riemannian Manifold of PDFs. 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile, 7-13 December 2015. Piscataway, NJ United States: IEEE. doi: 10.1109/iccv.2015.468
2015
Conference Publication
R1SVM: A randomised nonlinear approach to large-scale anomaly detection
Erfani, Sarah M., Baktashmotlagh, Mahsa, Rajasegarar, Sutharshan, Karunasekera, Shanika and Leckie, Chris (2015). R1SVM: A randomised nonlinear approach to large-scale anomaly detection. AI Access Foundation.
2015
Conference Publication
R1SVM: A randomised nonlinear approach to large-scale anomaly detection
M. Erfani, Sarah, Baktashmotlagh, Mahsa, Rajasegarar, Sutharshan, Karunasekera, Shanika and Leckie, Chris (2015). R1SVM: A randomised nonlinear approach to large-scale anomaly detection. Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, TX, United States, 25-30 January 2015. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v29i1.9208
2014
Journal Article
Discriminative non-linear stationary subspace analysis for video classification
Baktashmotlagh, Mahsa, Harandi, Mehrtash, Lovell, Brian C. and Salzmann, Mathieu (2014). Discriminative non-linear stationary subspace analysis for video classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36 (12) 6857376, 2353-2366. doi: 10.1109/TPAMI.2014.2339851
2014
Conference Publication
Domain adaptation on the statistical manifold
Baktashmotlagh, Mahsa, Harandi, Mehrtash T., Lovell, Brian C. and Salzmann, Mathieu (2014). Domain adaptation on the statistical manifold. 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, United States, 23-28 June 2014. Piscataway, NJ, United States: I E E E Computer Society. doi: 10.1109/CVPR.2014.318
2014
Other Outputs
Learning Invariances for High-Dimensional Data Analysis
Baktashmotlagh, Mahsa (2014). Learning Invariances for High-Dimensional Data Analysis. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland. doi: 10.14264/uql.2014.183
Supervision
Availability
- Associate Professor Mahsa Baktashmotlagh is:
- Available for supervision
Before you email them, read our advice on how to contact a supervisor.
Supervision history
Current supervision
-
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
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
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
Semantic Segmentation for Crop Health and Damage Assessment
Principal Advisor
-
Doctor Philosophy
Parametric Deep Neural Networks for Computer Vision Problems
Principal Advisor
-
Doctor Philosophy
Generalizing Implicit Representations for Robotics Manipulation of Articulated Objects
Principal Advisor
Other advisors: Dr Peyman Moghadam
-
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
Two way Auslan Translation
Associate Advisor
-
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
Unsupervised Domain Adaptation on 3D Object Detection and Segmentation
Associate Advisor
Other advisors: Professor Helen Huang, Dr Yadan Luo
-
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
-
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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