We develop new technologies in computational imaging, machine learning, and their applications in biomedicine, Earth science, security and defense.
Our work ranges from theory to real tested implementations. We house state-of-the-art laboratories for spectral imaging from the visible to the IR, X-ray spectral tomography and LiDAR.
Applications from students interested in joining our group who share our passion for imaging and machine learning are welcomed.
Charles Black Evans Professor in the Electrical and Computer Engineering Department. Long CV.
JPMorgan-Chase Faculty Fellow at the Institute of Financial Services Analytics.
Nokia-Fulbright Distinguished Chair in Information and Communications Technologies.
Dr. Gonzalo Arce’s fields of interest include computational imaging and machine learning for computational LIDAR. His active fields of research are:
Dr. Gonzalo Arce teaches ELEG/FSAN 815 Analytics I: Statistical Learning, ELEG 404/604 Digital Imaging and Photography, ELEG 833 Nonlinear Signal Processing, ELEG 636 Statistical Signal Processing, and ELEG 867 Compressive Sensing and Sparse Signal Representation.
The Computational Imaging and Machine Learning Group, lead by Dr. Arce, draws from expertise in optical physics, signal processing and computer science.
The Computational Imaging and Machine Learning Group has developed 2 experimental laboratories: an optics laboratory for multi-modal imaging and spectroscopy and an X-ray imaging testing facility for compressive tomography.
Our research covers time of flight multispectral imaging, compressive spectral imaging, computational litography, large scale data science, nonlinear signal processing and electronic printing.
The College of Engineering at the University of Delaware (UD) has had a 15 year tradition of attracting bright engineering students from across Colombia into its various graduate and doctorate programs of study through dual degrees, internships and the summer research program.
Dr. Gonzalo Arce is the author of Computational Lithography, Modern Digital Halftoning Ed. 1 and 2, Nonlinear Signal Processing: A Statistical Approach, and Nonlinear Signal Processing: Theory, Methods, and Applications
Department of Electrical and Computer Engineering University of Delaware 140 Evans Hall, Newark, DE,19716, USA.
Phone: (302) 831-1493
E-mail: arce@udel.edu