Dror Baron

Prof. Baron's undergraduate studies were in the Electrical Engineering Department at the Technion - Israel Institute of Technology, where he belonged to the Technion's Program for Outstanding Students. Following his graduation, he designed hardware modems at Witcom, an Israeli startup specializing in broadband wireless communication. At the same time he was pursuing his MSc, also at the Technion. In 1999 he joined the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign, where he completed his PhD in 2003. During the Spring 2003 semester, he was a Visiting Assistant professor at the University of Illinois.

After completing his PhD, Prof. Baron was a Postdoctoral Research Associateiate in the Department of Electrical and Computer Engineering at Rice University from 2003 to 2006, where he performed research with the Digital Signal Processing research group, which was among the pioneering groups in compressed sensing. From 2007 to 2008 he was a Quantitative Research Analyst with Menta Capital in San Francisco, where he developed quantitative investment strategies. After spending 2008 to 2010 as a Visiting Scientist in the Electrical Engineering Department at the Technion, Prof. Baron joined NC State in 2010 as an Assistant Professor.

Generally speaking, Prof. Baron's research lies on the intersection of fast algorithms, signal processing, and information theory. He is particularly interested in developing systems that extract as much information as possible from data in a computationally timely manner.

Education

2003 - PhD in Electrical Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois
1999 - MSc in Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel
1997 - BSc in Electrical Engineering, Technion - Israel Institute of Technology, Haifa, Israel

Awards & Honors

Publications

• An Approximate Message Passing Framework for Side Information (2019)
• Analysis of Approximate Message Passing With Non-Separable Denoisers and Markov Random Field Priors (2019)
• Performance Limits With Additive Error Metrics in Noisy Multimeasurement Vector Problems (2018)
• An overview of multi-processor approximate message passing (2017)
• Analysis of approximate message passing with a class of non-separable denoisers (2017)
• Conditional approximate message passing with side information (2017)
• Generalized geometric programming for rate allocation in consensus (2017)
• Multiprocessor approximate message passing with column-wise partitioning (2017)
• Performance limits for noisy multimeasurement vector problems (2017)
• Approximate Message Passing Algorithm With Universal Denoising and Gaussian Mixture Learning (2016)