Vandad Davoodnia

Enthusiastic machine learning scientist fascinated by natural language processing, computer vision, and their applications for virtual worlds.

I am a machine learning Ph.D. graduate from the Human-Centered Artificial Intelligence and Interactive Machines Laboratory (Aiim Lab) at Queen’s University. My most recent work lies in the intersection of NLP and 3D human pose estimation from cameras and pressure sensors. During my studies, I have worked on developing deep-learning methods for pose estimation in smart environments using ubiquitous sensors. I recently finished my internship at LaForge in Ubisoft Toronto Inc., designing, developing, and deploying a markerless motion capture application, resulting in fast and accurate prototyping in outdoor environments. Our research in multi-view pose estimation pushed the academic state of the art and performed similarly to industrial competitors.

Before my Ph.D., I gained diverse experience working at the Brain Engineering Research Center, IPM, while completing my B.Sc. from Sharif University of Technology, Iran. As a software developer, I collaborated with teams of engineers and international research groups to create mobile and desktop applications using Python, Matlab, Java, and C++. Furthermore, as a research scientist, I published several papers and gained valuable experience in signal processing, data science, and statistics.

I am actively seeking opportunities in the industrial research and development sectors, particularly in roles that allow me to apply my expertise on Artificial Intelligence. I am open to discussing potential roles that align with my skills and interests, such as research, data science, or AI engineering positions.

News

July '24 Our paper on human pose estimation with cross-view and temporal cues has been accepted to ECCV 2024.
June '24 Happy to announce that I have successfully obtained my Ph.D. degree. Thesis on "Estimating Human Pose from Pressure and Vision Data".
Apr '24 Check out our new paper on 3D human keypoint estimation estimation.
Apr '24 Check out our new paper on 3D human body shape and pose estimation estimation.
May '23 Secured $30,000 research grant from Mitacs in support of 8 months of additional research at Ubisoft Toronto.
Jan '23 Was selected as the top reviewer for AAAI R2HCAI workshop.
July '23 Our paper on enhancing driver experience through e-textiles was accepted at ACM DIS.
June '23 Our paper about 3D human pose estimation from pressure arrays was accepted at IEEE ICASSP.
Sep '22 Our paper on semi-supervised EEG representation learning was accepted at IEEE TAFFC.
May '22 I joined LaForge at Ubisoft Toronto Inc. as a research and developer programmer intern.
May '21 Our paper on pressure-based human pose estimation was accepted at IEEE ICASSP. An extended version was published in Journal of Applied Intelligence.