Dr Mahsa Baktashmotlagh
Senior Lecturer in Data Analytics
School of Information Technology and Electrical Engineering
+61 7 336 57597
Researcher biography
Mahsa Baktashmotlagh is currently a Senior Lecturer at UQ with a research focus, developing machine learning techniques applied in: Visual data analysis (Visual domain generalization, Video classification), Road traffic networks (Mining large scale road traffic networks), Biomedical data (Antibacterial activity prediction), Cyber Security (Detecting websites defacement), and Finance (Hedging foreign exchange trading risks).
| Featured projects | Duration |
|---|---|
| ML and data mining | |
| Using ML to detect cyber-attacks | |
| Interpretable machine learning models for medical imaging |
Journal Articles
Luo, Yadan, Huang, Zi, Chen, Hongxu, Yang, Yang, Yin, Hongzhi and Baktashmotlagh, Mahsa (2021). Interpretable signed link prediction with signed infomax hyperbolic graph. IEEE Transactions on Knowledge and Data Engineering, PP (99), 1-1. doi: 10.1109/TKDE.2021.3139035
Al-Saffar, Ahmed, Bialkowski, Alina, Baktashmotlagh, Mahsa, Trakic, Adnan, Guo, Lei and Abbosh, Amin (2020). Closing the gap of simulation to reality in electromagnetic imaging of brain strokes via deep neural networks. IEEE Transactions on Computational Imaging, 7 9274540, 13-21. doi: 10.1109/tci.2020.3041092
Rahman, Mohammad Mahfujur, Fookes, Clinton, Baktashmotlagh, Mahsa and Sridharan, Sridha (2020). Correlation-aware adversarial domain adaptation and generalization. Pattern Recognition, 100 107124. doi: 10.1016/j.patcog.2019.107124
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
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
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
Conference Papers
Chen, Zhuoxiao, Luo, Yadan and Baktashmotlagh, Mahsa (2021). Conditional Extreme Value Theory for Open Set Video Domain Adaptation. MMAsia '21: ACM Multimedia Asia, Gold Coast, QLD Australia, 1 - 3 December 2021. New York, NY United States: Association for Computing Machinery. doi: 10.1145/3469877.3490600
Wang, Zijian, Luo, Yadan, Qiu, Ruihong, Huang, Zi and Baktashmotlagh, Mahsa (2021). Learning to diversify for single domain generalization. 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC Canada, 10-17 October 2021. Piscataway, NJ USA: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICCV48922.2021.00087
Wang, Zijian, Luo, Yadan, Huang, Zi and Baktashmotlagh, Mahsa (2020). Prototype-matching graph network for heterogeneous domain adaptation. MM '20: 28th ACM International Conference on Multimedia, Online, October 2020. New York, NY, United States: ACM. doi: 10.1145/3394171.3413662
Luo, Yadan, Huang, Zi, Wang, Zijian, Zhang, Zheng and Baktashmotlagh, Mahsa (2020). Adversarial bipartite graph learning for video domain adaptation. ACM International Conference on Multimedia, Seattle, WA, United States, 12-16 October 2020. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/3394171.3413897
Luo, Yadan, Huang, Zi, Zhang, Zheng, Wang, Ziwei, Baktashmotlagh, Mahsa and Yang, Yang (2020). Learning from the past: continual meta-learning with Bayesian Graph Neural Networks. The Thirty-Fourth AAAI Conference on Artificial Intelligence/ The Thirty-Second Conference on Innovative Applications of Artificial Intelligence/ The Tenth Symposium on Educational Advances in Artificial Intelligence, New York, United States, 7-12 February 2020. Palo Alto, CA, United States: Association for the Advancement of Artificial Intelligence (AAAI). doi: 10.1609/aaai.v34i04.5942
Moskvyak, Olga, Maire, Frederic, Dayoub, Feras and Baktashmotlagh, Mahsa (2020). Learning Landmark Guided Embeddings for Animal Re-identification. IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Snowmass, CO United States, 1-5 March 2020. Piscataway, NJ United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/WACVW50321.2020.9096932
Luo, Yadan, Wang, Zijian, Huang, Zi and Baktashmotlagh, Mahsa (2020). Progressive graph learning for open-set domain adaptation. 37th International Conference on Machine Learning ICML 2020, Vienna, Austria, 12-18 July 2020 . International Machine Learning Society .
Michalkiewicz, Mateusz, Pontes, Jhony Kaesemodel, Jack, Dominic, Baktashmotlagh, Mahsa and Eriksson, Anders (2020). Implicit surface representations as layers in neural networks. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), Seoul, Korea, 27 October -2 November, 2019 . Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/ICCV.2019.00484
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
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.
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
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
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
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
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
Thesis
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