Peyman Bateni

I recently started Beam AI with the wonderfully knowledgable Dr. Leonid Sigal where I currently serve as CEO. At Beam AI, we enable smartphone apps to monitor user pulse, HRV and stress (according to the Baevsky stress index) through the selfie camera. Learn more at Beam AI!

I completed my graduate studies in Machine Learning at the University of British Columbia under the supervision of Dr. Frank Wood where I divided my time between UBC PLAI Group as a graduate researcher and Inverted AI as a research engineer. Previously, I completed my undergraduate studies in Computer Science at the University of British Columbia. During my time at UBC, I also had the pleasure of collaborating with Dr. Leonid Sigal and Dr. Guiseppe Carenini on a range of Computer Vision / NLP problems.

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Research / Publications

I'm broadly interested in developing methods that allow for intelligent reasoning within the visual space. My past research has been primarily focused on few-shot and semi-supervised learning of object classifiers and detectors. Recently, I've begun work on computer vision for healthcare, specifically developing fast and effective methods for video-based vitals measurement and monitoring.

	On Label-Eﬀicient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers Peyman Bateni - Supervisor: Frank Wood, Second Reader: Leonid Sigal Master's Thesis UBC cIRcle Archives / Code / Bibtex / PapersWithCode / Video
	Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning Peyman Bateni, Jarred Barber, Raghav Goyal, Vaden Masrani, Jan-Willem van de Meent, Leonid Sigal, Frank Wood IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Special Issue on Learning with Fewer Labels in Computer Vision, 2022 (in submission) ArXiv / Code / Bibtex / PapersWithCode
	Enhancing Few-Shot Image Classification with Unlabelled Examples Peyman Bateni, Jarred Barber, Jan-Willem van de Meent, Frank Wood IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2022 - Algorithm Track Paper / ArXiv / Code / Bibtex / PapersWithCode / Video
	Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable Simulation Adam Scibior, Vasileios Lioutas, Daniele Reda, Peyman Bateni, Frank Wood IEEE International Conference on Intelligent Transportation (ITSC), 2021 (A short-form version of this paper was also presented at the Autonomous Driving: Perception, Prediction and Planning Workshop at CVPR 2021 where it was awarded Best Paper) Paper / ArXiv / Bibtex / PapersWithCode / Video
	Neural RST-based Evaluation of Discourse Coherence Grigorii Guz, Peyman Bateni*, Darius Muglich, Giuseppe Carenini Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL) and the International Joint Conference on Natural Language Processing (IJCNLP)*, 2020 Paper / ArXiv / Code / Bibtex / PapersWithCode / Video
	Improved Few-Shot Visual Classification Peyman Bateni, Raghav Goyal, Vaden Masrani, Frank Wood, Leonid Sigal IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020 (A short-form version of this paper was also presented at the Visual Learning with Limited Labels Workshop at CVPR 2020) Paper / ArXiv / Code / Bibtex / PapersWithCode / Video

Website adapted from Jon Barron