Jie Yang is starting as Assistant Professor at the Web Information Systems Group of the Faculty of Engineering, Mathematics and Computer Science (EEMCS/EWI), Delft University of Technology. Before coming back to TU Delft, he was a Machine Learning Scientist at Alexa Shopping, Amazon Research, based in Seattle, and a Senior Researcher at the eXascale Infolab, University of Fribourg - Switzerland. He received his Ph.D. from TU Delft in 2017, M.Sc. from TU Eindhoven in 2013, and B.Eng. from Zhejiang University in 2011. During his M.Sc., he also interned at Philips Research.
Jie's research focuses on human-centered machine learning for Web-scale information systems, aiming at leveraging the joint power of human and machine intelligence for understanding and making use of data in large-scale information systems. Over the past few years, Jie has worked on integrating human computation with model training in active learning, transfer learning, and weakly-supervised learning settings, to allow models to effectively and efficiently learn from small, sparse, and noisy data. More recently, he is focusing on developing human-centered approaches for better performance, more robust machine learning systems.
An Analysis of Music Perception Skills on Crowdsourcing Platforms
Ioannis Petros Samiotis / Sihang Qiu / Christoph Lofi / Jie Yang / Ujwal Gadiraju / Alessandro Bozzon
Optimizing Camera Configuration and Finger Pressure for Biometric Authentication
Weizheng Wang / Marek Vette / Qing Wang / Jie Yang / Marco Zuniga
Embedded AI Enabled Air-Writing for a Post-COVID World
K.S. Goedemondt / J. Yang / Q. Wang
How can Explainability Methods be Used to Support Bug Identification in Computer Vision Models?
Agathe Balayn / Natasa Rikalo / Christoph Lofi / Jie Yang / Alessandro Bozzon
Human-in-the-Loop Rule Discovery for Micropost Event Detection
Akansha Bhardwaj / Jie Yang / Philippe Cudré-Mauroux
CCC 1st Prize Blue Sky Ideas
The Ninth AAAI Conference on Human Computation and Crowdsourcing
Douglas Engelbart Best Paper Award
28th ACM Conference on Hypertext and Social Media, HT 2017
Best Paper Award
Weaving Relations of Trust in Crowd Work