Alexander Schwing

Research & Bio

Alex's research is centered around machine learning and computer vision. He is particularly interested in algorithms for prediction with and learning of non-linear, multivariate and structured distributions, and their application in numerous tasks, e.g., for 3D scene understanding from a single image.

Alex Schwing is an assistant professor in the Department of Electrical and Computer Engineering at the University of Illinois in Urbana-Champaign and affiliated with the Coordinated Science Laboratory. Prior to that he was a postdoctoral fellow in the Machine Learning Group at University of Toronto collaborating with Raquel Urtasun, Rich Zemel and Ruslan Salakhutdinov. He completed his PhD in computer science in the Computer Vision and Geometry Group at ETH Zurich working with Marc Pollefeys, Tamir Hazan and Raquel Urtasun, and graduated from Technical University of Munich (TUM) with a diploma in Electrical Engineering and Information Technology.

Publications
Algorithms

I'm grateful for support by

NVIDIA Corp. donating a Titan X for our research
The NIPS foundation for providing a NIPS 2015 travel grant
The anonymous ICML area chairs supporting my ICML 2015 reviewer award
Re.Work providing the opportunity to talk at the Deep Learning Summit:
The CVPR 2015 organization committee for granting the CVPR 2015 Young Researcher Support
My thesis Committee, external referees and ETH Zurich for awarding my PhD thesis with an ETH medal
The NIPS foundation for providing a NIPS 2014 travel grant
The Fields Institute for awarding a Fields Postdoctoral Fellowship
NVIDIA Corp. donating a Tesla K40 GPU for our research

Winter 2016: Gaussian Processes (Guest Lecture in: Probabilistic Graphical Models)
Fall 2015: Structured Prediction (Guest Lecture in: Intro to Machine Learning)
Fall 2015: Neural Networks (Guest Lecture in: Intro to Image Understanding)
Winter 2015: Deep Learning and Structured Prediction (Guest Lecture in: Intro to Machine Learning)
Winter 2015: Continuous Latent Variable Models (Guest Lecture in: Intro to Machine Learning)
Fall 2014: All you wanted to know about Neural Networks (Guest Lecture in: Intro to Image Understanding)