Research Interests:
In general, I am interested in Machine Learning and its applications in real
world problems. I am currently interested in semi-supervised and active learning methods for activity recognition,
human behaviour understanding and user modeling. In the past couple of years I was involved in the following areas:
1) Gaussian Random Fields
2) Krylov Subspace Methods
3) Multi-pole methods and N-body Learning
4) Document Classification
5) Object Class Recognition
Refereed Publications:
Maryam Mahdaviani, Nando de Freitas, Bob Fraser and Firas Hamze. Fast Computational Methods for Visually Guided Robots. ICRA 2005(Acceptance rate = 43%). [PDF]
Nando de Freitas, Yang Wang, Maryam Mahdaviani, Dustin Lang. Fast Krylov Methods for N-Body Learning. NIPS 2005(Acceptance rate = 27%). [PDF]
Maryam Mahdaviani and Nando
de
Freitas . User Adaptive Image Ranking for Search Engines. User Adaptive Systems workshop (acceptance rate = 33%), NIPS
2006.
Maryam Mahdaviani and Tanzeem Choudhury . Fast and Scalable Training of
Semi-Supervised CRFs with Application to Activity Recognition . NIPS
2007 (Acceptance rate = 22%).
Miresmailli, S., Badulescu, D., Mahdaviani, M., Zamar, R. H. and Isman, M.B. 2009. Integrating plant chemical ecology, sensors and artificial intelligence for accurate pest monitoring. In: Columbus F. (Ed). Tomatoes:
Agricultural procedures, pathogen interaction and health effects. Nova Science Publishers, NY. In Press
Other Publications: My masters thesis:
Semi-supervised and Active Trainng of Conditional Random Fields for
Activity Recognition.
[PDF] My undergrad honours
thesis: Fast Object Class
Recognition. [PDF] Tech Report: Integrating
Gaussian Processes with Word-Sequence Kernels for
Bayesian Text Categorization, with Sara Forghanizadeh and Giuseppe Carenini[ps.gz] An updated version of this technical report has been submitted to HLT-NAACL
2007. Presentations: ICRA 2005 [PPT] NIPS 2005 [PPT] NIPS 2006 User Adaptive Systems Workshop [PPT] Last update: August 14, 2009