WiNGS Lab @ UW

The Wisconsin Wireless and NetworkinG Systems (WiNGS) Laboratory was established at UW-Madison in summer 2005. Research in the WiNGS lab is conducted in the areas of networking and distributed systems, with a primary focus on wireless and mobile networking. Research activities in the lab span a diverse set of activities, ranging from algorithmic design and analysis, and systems building through functional prototypes.

Vertical Research Group

The Vertical Research Group explores computer architecture, VLSI technology and semiconductor manufacturing processes with a vertically integrated approach. Changing technology constraints are leading to a scenario in which current techniques for building processors simply won't work. Emerging applications provide an opportunity to think in a fundamentally different way about how we build processors. We will explore techniques to build high-performance processors that can operate under future technology constraints of reliability and energy efficiency.

NSF funds sustainability project studying human component of green buildings

In 2012, the National Science Foundation (NSF) awarded $1.9 million in funds for a sustainability project that explores the role of human mediation in improving buildings' energy efficiency. Led by computer sciences associate professor Suman Banerjee, who is serving as principal investigator, this major research endeavor spans five disciplines and two campuses. The University of Wisconsin-Madison is partnering with Rensselaer Polytechnic Institute of Troy, New York, on the multidisciplinary research.

Machine learning paper prize awarded to Professor Zhu and co-authors

At the 30th International Conference on Machine Learning, held June 16-21, 2013, in Atlanta, UW professor Xiaojin (Jerry) Zhu and co-authors Zoubin Ghahramani and John Lafferty received the 2013 Classic Paper Prize. The team won the award for their paper "Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions." The Classic Paper Prize is designed to recognize papers that time and hindsight have proven to be of lasting value to the machine learning community. Their paper was published at ICML in 2003.


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