I am a PhD student in the Machine Learning, Digital Signal Processing, and Computational Imaging labs at Rice University. My research focuses on the development, application, and analysis of new signal processing algorithms. I am especially interested in algorithm design related to problems in computational imaging, machine learning, and communications. I work under the direction of professors Richard Baraniuk and Ashok Veeraraghavan.
prDeep: Robust Phase Retrieval with Flexible Deep Neural Networks C. Metzler, P. Schniter, A. Veeraraghavan, R. Baraniuk. Submitted 2018.
Imaging Through Extreme Scattering in Extended Dynamic Media A. Kanaev, A. Watnik, D. Gardner, C. Metzler, K. Judd, P. Lebow, K. Novak, J. Lindle. Accepted 2018.
An Expectation-Maximization Approach to Tuning Generalized Vector Approximate Message Passing C. Metzler, P. Schniter, R. Baraniuk. Accepted 2018.
Learned D-AMP: Principled Neural Network based Compressive Image Recovery C. Metzler, A. Mousavi, R. Baraniuk. Neural Information Processing Systems 2017.
Coherent Inverse Scattering via Transmission Matrices: Efficient Phase Retrieval Algorithms and a Public Dataset C. Metzler, M. Sharma, S. Nagesh, R. Baraniuk, O. Cossairt, A. Veeraraghavan. IEEE International Conference on Computational Photography 2017. ICCP best-paper runner-up.
BM3D-prGAMP: Compressive phase retrieval based on BM3D denoising C. Metzler, A. Maleki, R. Baraniuk. IEEE International Conference on Image Processing 2016.
From Denoising to Compressed Sensing C. Metzler, A. Maleki, R. Baraniuk. IEEE Transactions on Information Theory 2016.
Optimal recovery from compressive measurements via denoising-based approximate message passing C. Metzler, A. Maleki, R. Baraniuk. International Conference on Sampling Theory and Applications 2015.
BM3D-AMP: A new image recovery algorithm based on BM3D denoising C. Metzler, A. Maleki, R. Baraniuk. IEEE International Conference on Image Processing 2015. ICIP "Top 10%" paper.
Data Driven Computational Imaging: Improved Imaging Through Scattering Media with Visible Light Invited talk at Stanford Center for Image System Engineering 2018.
Phase Retrieval: Fast, Robust, and Data-driven Algorithms for Computational Imaging Invited talk at SPIE Photonics West QPI IV 2018.
Unrolling: A Principled Method to Develop Deep Neural Networks Talk at Rice Geo-Mathematical Imaging Group Project Review 2017.
BM3D-prGAMP: Compressive Phase Retrieval Based on BM3D Denoising Talk at ICME MM-SPARSE workshop 2016.
Connecting Bayesian and Denoising-based Compressed Sensing Invited talk at Asilomar 2015.
BM3D-AMP: A New Image Recovery Algorithm Based on BM3D Denoising Talk at ICIP 2015.
Learned D-AMP, D-AMP, & D-prGAMP Toolbox: Neural networks and algorithms for compressive sensing and compressive phase retrieval.
prDeep: A neural-network-based noise-robust phase retreival algorithm.
DatasetsTransmission Matrix Dataset: A public dataset for testing phase retrieval algorithms.
I received my BSEE and MSEE from Rice University in 2013 and 2014, respectively. I am currently working towards my PhD in electrical and computer engineering, again from Rice. My graduate education has been supported by the NSF GRF, DoD NDSEG, Texas Instruments, and Ken Kennedy Institute fellowship programs.
I have had six summer internships; one at the Naval Research Laboratory, two at Ball Aerospace, one at ViaSat, one at National Instruments, and one at the Technical University of Braunschweig. In high school I worked weekends and summers as a construction laborer and apprentice electrician.
I am a professional development chair of the Rice ECE graduate student association and I am an active member of the IEEE. I regularly review for the IEEE Transactions on Signal Processing, Image Processing, and Computational Imaging, in addition to several other journals and conferences.
My email address is chris.metzler at rice.edu.
My office is room 1033 in Duncan Hall.