Computer Engineering Director
Professor, School of Electrical, Computer & Energy Engineering
Editor-in-Chief (Term starting January 1, 2019), IEEE Journal of Selected Topics on Signal Processing
Director, Image, Video, and Usability (IVU) Lab
Director, Real-Time Embedded Signal Processing (RESP) Lab
Office: Goldwater Building, Room 430 (GWC 430)
School of Electrical, Computer, and Energy Engineering
Arizona State University
Tempe, AZ 85287-5706
Phone: (480) 965-3694
Fax: (480) 965-0493
Fall 2018 Office Hours: Tuesdays noon to 1:00~pm and Thursdays 9:15am to 10:15am
Dr. Karam's current research interests are in the areas of image and video processing, compression, and transmission; computer vision; machine learning; visual quality assessment; perceptual-based processing; human visual perception; signal processing for intelligent systems and robotics; human-machine interactions; multidimensional signal processing; error-resilient source coding; digital filter design; and bio-medical imaging.
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Lina J. Karam received a BA in engineering from the American University of
Beirut in 1989, and MS and PhD degrees in electrical engineering from the Georgia
Institute of Technology in 1992 and 1995, respectively. She is currently a full professor, Computer Engineering Director, and the Director of the Image, Video, and Usability (IVU) Lab
Lab at ASU. She is also the President of PICARIS,LLC, a consulting company on media processing, compression, understanding, and analytics.
Dr. Karam is an IEEE Fellow, the highest grade level in IEEE which is conferred each year to no more than one-tenth of 1% of all IEEE voting members, for her contributions in the image and video processing, visual communications, and digital filtering areas.
Her industrial experience includes image and video processing and compression development at AT&T Bell Labs (Murray Hill), multi-dimensional data processing and visualization at Schlumberger, and collaborations on computer vision, image/video processing, compression, and transmission projects with various industries including Intel, Qualcomm, Google, NTT, Motorola, Freescale, General Dynamics, and NASA.
Dr. Karam is a recipient of the National Science Foundation CAREER Award, NASA Technical Innovation Award, the Intel Outstanding Researcher Award, the IEEE SPS Best Journal Paper Award, and the IEEE Phoenix Section Outstanding Faculty Award. Dr. Karam served on the IEEE PSPB Strategic Planning Committee, and the editorial boards of the IEEE Signal Processing Magazine, IEEE Journal on Selected Topics in Signal Processing, IEEE Transactions on Image Processing, and IEEE Signal Processing Letters. She served as a Lead Guest Editor for the Proceedings of the IEEE, IEEE JSTSP, and as a Guest Editor for the IEEE SP Magazine and EURASIP Journal on Image and Video Processing. She served as the General Chair of the 2016 IEEE ICIP, Technical Program Chair of the 2009 IEEE ICIP, General Chair of the 2011 IEEE DSP/SPE Workshops. She cofounded the International Conference on Quality of Multimedia Experience (QoMEX). She is currently serving as General Co-Chair for the IEEE International Conference on Multimedia and Expo (ICME). She is also currently serving on the IEEE SPS Board of Governors and on the IEEE CAS Fellow Evaluation Committee. She is on the Foundation and Trends in Signal Processing Journal Editorial Board. She is a member of the IEEE SPS IVMSP TC and IEEE CAS DSP TC. She has been selected by the IEEE Signal Processing Society to serve as the new Editor-In-Chief of the IEEE Journal on Selected Topics in Signal Processing (IEEE JSTSP) starting January 2019.
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Dr. Karam has directed projects that led to successful technology transfer.
Some select projects and patents are listed below.
Dr. Karam directed the development of coputer vision and machine learning systems for assistive technologies include ADAS technologies for smart cars. The developed real-time forward collision warning system prototype was demonstrated by our Intel industry collaborators at the 2015 Consumers' Electronic Symposium (CES) in Las Vegas. Dr. Karam directed the development and evaluation of image/video-based gender and age estimation systems for industry partners. Dr. Karam directed the development of scalable visual compression technologies that outperform existing video codecs in low-bandwidth environments. The developed codecs were commercialized by General Dynamics as SelectFocus Image and SelectFocus Video and were integrated as the core of General Dymanics' OTUS Integrated Mobile Situational Awareness System . More details can be found in Chien, Sadaka, Abousleman, and Karam, "Region-of-Interest-Based Ultra-Low-Bit-Rate Video Coding," SPIE Symposium on Defense & Security, March 2008.
Dr. Karam has directed the development of visual processing, computer vision, and machine learning algorithms for automated defect detection in semi-conductor units and 3D characterization. The developed systems are currently being used at Intel for automatically identifying issues early during the assembly and test process. The developed void detection system helped in enabling two industry standards, JEDEC JC 14-1 void guideline and IPC-7095C. More details can be found in Said, Bennett, Karam, and Pettinato, "Robust Automatic Void Detection in Solder Balls and Joints," IPC Printed Circuit Expo, April 2010, and in Said et al., "Automated Void Detection in Solder Balls in the Presence of Vias and Other Artifacts," to appear in the IEEE Transactions on Components, Packaging and Manufacturing Technology. The developed image-based non-wet solder joints detection system was granted a Divisional Recognition Award by Intel. More details can be found in Said, Bennett, Karam, and Pettinato, "Automated Detection and Classification of Non-Wet Solder Joints," IEEE Transactions on Automation Science and Engineering, Jan 2011. More details about the 3D Characterication including mage-based solder ball height and warpage measurements can be found in Li, Bennett, Karam, and Pettinato, "Stereo Vision Based Automated Solder Ball Height and Substrate Coplanarity Inspection," IEEE Transactions on Automation Science and Engineering, vol. 13, no. 2, pp. 757-771, April 2016. Details about machine learning based computer vision for defect detection can be found in Haddad, Yang, Karam, Ye, Patel and Braun, "Multi-Feature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units," accepted and to appear in the IEEE Transactions on Automation Science and Engineering, 14 pages, 2016. Dr. Karam was granted the 2012 Intel Outstanding Researcher Award in High-Volume Manufacturing.
Dr. Karam has directed the development of perceptual-based visual compression methods and algorithms. The work on JPEG2000 Encoding with Perceptual Distortion Control enabled the integration of adaptive perceptual-based visual processing and compression in the JPEG 2000 image coding standard and demonstrated improved performance in terms of visual quality and compression while maintaining full compatibility with the JPEG 2000 standard. For this significant contribution, Dr. Karam received a Technical Innovation Award from the US National Aeronautics and Space Administration (NASA).
Dr. Karam directed the development of automated biomedical image analysis i algorithms that enable high-thoughput cancer diagnostics and drug discovery. The developed automated image analysis technologies have been commercialized by Muscale, LLC, and have been used for cancer research at different institutions, including the Translational Genomics Institute (TGEN) and the New York School of Medicine.
Dr. Karam has developed as a consultant for PICARIS, LLC, image mosaicing technologies.
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