School of Engineering

Graduates the engineering leaders of tomorrow...

Lina J. Karam

Dean, School of Engineering

Professor, School of Electrical and Computer Engineering
IEEE Fellow
Editor-in-Chief, IEEE Journal of Selected Topics on Signal Processing
Lead Guest Editor, IEEE Signal Processing Magazine, Special Issue on Autonomous Driving (2019 – present)
Emerita Professor, Arizona State University, Tempe, AZ, USA

Biography

Dr. Lina J. Karam is the Dean of the School of Engineering and Professor of Electrical and Computer Engineering at LAU. She is an IEEE Fellow and Editor-In-Chief of the IEEE Journal on Selected Topics in Signal Processing (IEEE JSTSP). She received the B.E. degree in computer and communications engineering from the American University of Beirut and the M.S. and Ph.D. degrees in electrical engineering from the Georgia Institute of Technology, Atlanta, GA, USA. Prior to joining LAU, Dr. Karam was a tenured Full Professor in the School of Electrical, Computer & Energy Engineering, Arizona State University, where she also directs the Image, Video, & Usabilty (IVU) Research Laboratory. At ASU, Dr. Karam helped in establishing two transdisciplinary programs, the Computer Engineering Program (across Computer Science and Electrical Engineering) and the Robotics & Autonomous Systems (RAS) Program (across four schools). She served as the Computer Engineering Program Chair and as the Computer Engineering Director for Industry Engagement. Dr. Karam was awarded a U.S. National Science Foundation CAREER Award, a NASA Technical Innovation Award, 2018 IEEE Region 6 Award, IEEE SPS Best Paper Award, 2012 Intel Outstanding Researcher Award, and 2012 IEEE Phoenix Section Outstanding Faculty Award. Dr. Karam’s industrial experience includes image and video compression development at AT&T Bell Labs, Murray Hill, NJ, USA, multidimensional data processing and visualization at Schlumberger, and collaboration on computer vision, machine learning, image/video processing, compression, and transmission projects with various industries. At ASU, she directed numerous funded projects that led to successful technology transfer with industry partners. She recently initiated an ASU-Silicon Valley Industry Partnership Program for Engineering and Computer Science Graduate Students, and was recently invited to participate as a judge in Vision Tank 2019, a competition event held in Silicon Valley for startup companies. She initiated and helped in establishing the World’s First Visual Innovation Award in 2016 and more recently the World’s First Multimedia Star Innovator Award in 2019. Dr. Karam was featured on FOX 10 News (October 30, 2018) in a segment on driverless cars and in a news article in the Phoenix Business Review Journal on Waymo’s Self-Driving Truck Testing. She also participated on various panels on self-driving cars and smart cities in the US, including panels organized for law professionals, local cities, and public outreach. Dr. Karam has over 220 technical publications and she is an inventor on 7 issued US patents. She served on the IEEE Publication Services and Products Board (PSPB) Strategic Planning Committee, the IEEE Signal Processing Society’s (SPS) Board of Governors, the IEEE Circuits and Systems’ Fellow Evaluation Committee, and the IEEE SPS Conference Board. In addition to her service as EiC of IEEE JSTSP, Dr. Karam served on the editorial boards of several high-impact journals, and on the organizing committees of several premier international conference. Dr. Karam is currently serving on the IEEE Access Journal Senior Editorial Board, IEEE TechRxiv Advisory Board, IEEE Signal Processing Society’s (SPS) Awards and Publications Boards and as an elected member of the IEEE SPS Image, Video & Multidimensional Signal Processing (IVMSP) Technical Committee and the IEEE Circuits & Systems (CAS) Digital Signal Processing (DSP) Technical Committee. She co-founded the international QoMEX conference.

Highlights

Research Topics and Publications Highlights
Signal, Image, and Video Processing, Compression, and Transmission; Computer Vision; Machine Learning; Deep Learning; Perceptual-based Processing; Visual Attention Models; Automated Quality Assessment; Multidimensional Signal Processing; Digital Filter Design. Supervised to completion 45 graduate students (22 PhD and 23 MS Thesis students)

Author on more than 220 technical publications
Inventor on 7 issued patents
National Academy of Inventors, ASU Chapter
Expert Consulting in Patent Litigation, Image/Video Compression, Image/Video Processing, Computer Vision

Industry Collaborations and Technology Transfer
Image and video processing and compression development at AT&T Bell Labs (Murray Hill); multi-dimensional data processing and visualization at Schlumberger; R&D collaborations on computer vision, machine learning, image/video processing, compression, and transmission projects with various industries including Intel, Qualcomm, Google, NTT, Motorola, Microsoft, Freescale, General Dynamics, and NASA, among others.

Established the World’s First Visual Innovation Award in 2016:
https://www.youtube.com/watch?v=pEZ2mT5sASw
https://fullcircle.asu.edu/faculty/visual-tech-visionaries-honored-innovation-awards/

Established the World’s First Multimedia Star Innovator Award in 2019.
Established in 2018 an ASU-Silicon Valley Fellowship/Internship Program for Graduate Students (Computer Engineering, Electrical Engineering, Computer Science, Industrial Engineering) with TCL Research America as founding partner.
Established and led in 2018 a University-wide Intel-ASU Collaborative Initiative on Automated Mobility/Autonomous Cars:

Judge on Vision Tank 2019 Start-Up Competition, Embedded Vision Summit, Santa Clara, CA, USA (May 22, 2019)

Technology Transfer

Dr. Karam has directed projects that led to successful technology transfer. Some select projects are listed below.

Dr. Karam directed the development of computer 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,” 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 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.

Education

Georgia Institute of Technology
Ph.D., Electrical Engineering, 1995
M.S., Electrical Engineering, 1992

American University of Beirut
B.E., Computer and Communications Engineering, 1989

Select Current Scientific and Professional Society Memberships

Patents

Note: Student authors are shown in boldface.

  1. Tinku Acharya, Lina J. Karam, and Francescomaria Marino, “The Compression of Color Images Based on a 2-Dimensional Discrete Wavelet Transform Yielding a Perceptually Lossless Image,” US Patent 6,154,493. Filed 1998 by Intel. Issued 2000.
  2. Tinku Acharya, Lina J. Karam, and Francescomaria Marino, “Real-time Algorithms and Architectures for Coding Images Compressed by DWT-Based Techniques,” US Patent 6,124,811. Filed 1998 by Intel. Issued 2000.
  3. Glen P. Abousleman, Tuyet-Trang Lam, and Lina J. Karam, “Communication System and Method for Multi-Rate, Channel-Optimized Trellis-Coded Quantization,” US Patent 6,717,990. Filed 2000 by Motorola. Issued 2004.
  4. Katherine S. Tyldesley, Glen P. Abousleman, and Lina J. Karam, “System and Method for Transmission of Video Signals using Multiple Channels,” US Patent 7551671 B2. Filed 2003 by General Dynamics. Issued June 2009.
  5. Lina J. Karam and Asaad F. Said, “Automatic Cell Migration and Proliferation Analysis,” United States Patent 9,082,164. Issued July 14, 2015.
  6. Lina J. Karam and Samuel Dodge, “Systems, Methods, and Media for Identifying Object Characteristics Based on Fixation Points,” United States Patent 9,501,710 B2. Issued November 22, 2016.
  7. Lina J. Karam and Jinjin Li, “Stereo Vision Measurement System and Method,” United States Patent 9,704,232 B2. Full Patent Filed March 18, 2015. Issued July 11, 2017.
    Technology Licensed by Intel through Arizona Technology Enterprises (AzTE).
  8. Lina J. Karam and Tejas Borkar, “Systems and Methods for Feature Corrections and Regeneration for Robust Sensing, Computer Vision, and Classification,” Provisional Patent Application 62/650,905 filed on 30 March 2018.

Select Recent Refereed Journal Papers

Note: Student authors are shown in boldface.

  1. Tejas S. Borkar and Lina J. Karam, “DeepCorrect: Correcting DNN Models against Image Distortions,” IEEE Transactions on Image Processing, vol. 28, issue 12, pp. 6022-6034, Dec. 2019.
  2. Samuel F. Dodge and Lina J. Karam, “Human and DNN Classification Performance on Images With Quality Distortions: A Comparative Study,” ACM Transactions on Applied Perception, vol. 16, issue 2, 18 pages, March 2019; doi 10.1145/3306241.
  3. Jinane S. Monsef and Lina J. Karam, “Augmented Sparse Representation Classifier (ASRC) for Face Recognition under Quality Distortions,” IET Biometrics Journal, vol. 8, issue 6, pp. 431-442, Nov. 2019.
  4. Charan D. Prakash, Farshad Akhbari, and Lina J. Karam, “Robust Obstacle Detection for Advanced Driver Assistance Systems using Distortions of Inverse Perspective Mapping of a Monocular Camera,” Robotics and Autonomous Systems Journal, vol. 114, pp. 172-186, 2019.
  5. Samuel F. Dodge and Lina J. Karam, “Quality Robust Mixtures of Deep Neural Networks,” IEEE Transactions on Image Processing, vol. 27, no. 11, pp. 5553-5562, Nov. 2018.
  6. Samuel F. Dodge and Lina J. Karam, “Visual Saliency Prediction Using a Mixture of Deep Neural Networks,” IEEE Transactions on Image Processing, vol. 27, no. 8, pp. 4080-4090, August 2018.
  7. Aditee Shrotre and Lina J. Karam, “Full Reference Objective Quality Assessment for Reconstructed Background Images,” Journal of Imaging, vol. 4, no. 6, 82; 24 pages, June 2018. https://doi.org/10.3390/jimaging4060082
  8. Samuel Dodge, Jinane Mounsef, and Lina J. Karam, “Unconstrained Ear Eecognition using Deep Neural Networks,” IET Biometrics, 8 pages. Accepted January 2018. doi: 10.1049/iet-bmt.2017.0208
  9. Milind S. Gide and Lina J. Karam, “Computational Visual Attention Models,” Foundations and Trends® in Signal Processing, vol. 10, no. 4, pp 347-427, 2017. http://dx.doi.org/10.1561/2000000055 (invited)
  10. S. Alirezah Golestaneh and Lina J. Karam, “Reduced-Reference Quality Assessment Based on the Entropy of DWT Coefficients of Locally Weighted Gradient Magnitudes,” IEEE Transactions on Image Processing, vol. 25, no. 11, pp. 5293-5303 (11 pages), Nov. 2016.
  11. Bashar M. Haddad, Sen Yang, Lina J. Karam, Jieping Ye, Nital Patel, and Martin Braun, “Multi-Feature, Sparse-Based Approach for Defects Detection and Classification in Semiconductor Units,” IEEE Transactions on Automation Science and Engineering,  15 pages, Aug. 2016, doi 10.1109/TASE.2016.2594288.
  12. Milind Gide and Lina J. Karam, “A Locally Weighted Fixation Density-Based Metric for Assessing the Quality of Visual Saliency Predictions,” IEEE Transactions on Image Processing, vol. 25, no. 8, pp. 3852-3861, Aug. 2016.
  13. Mahesh Subedar and Lina J. Karam, “3D Blur Discrimination,” ACM Transactions on Applied Perception, vol. 13, issue 3, article no. 12, 13 pages, May 2016, doi 10.1145/2896453.
  14. Jinjin Li, Bonnie L. Bennett, Lina J. Karam, and  Jeffrey S. 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.

Select Recent Refereed Conference Papers

Note: Student authors are shown in boldface.

  1. Tejas Borkar, Felix Heide, and Lina Karam, “Defending Against Universal Attacks Through Selective Feature Regeneration,” IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 11 pages, 2020.
  2. S. Alireza Golestaneh and Lina J. Karam, “Synthesized Texture Quality Assessment via Multi-scale Spatial and Statistical Texture Attributes of Image and Gradient Magnitude Coefficients,” IEEE CVPR Workshop, NTIRE: New Trends in Image Restoration and Enhancement Workshop and Challenges, 7 pages, June 2018.
  3. Jinane Mounsef and Lina J. Karam, “Augmented Sparse Representation Classifier for Blurred Face Recognition,” IEEE International Conference on Image Processing (ICIP), 2018.
  4. Lina J. Karam, Tejas Borkar, Junseok Chae, Yu Cao, “Generative Sensing: Transforming Unreliable Data for Reliable Recognition,” IEEE Multimedia Information Processing and Retrieval (IEEE MIPR), Apr. 2018.
  5. Samuel Dodge and Lina Karam, “Can the Early Human Visual System Compete with Deep Neural Networks?,” 7 pages, International Conference on Computer Vision (ICCV), Workshop on Mutual Benefits of Cognitive and Computer Vision (MBCC), Oct. 2017.
  6. Samuel Dodge and Lina Karam, “A Study and Comparison of Human and Deep Learning Recognition Performance Under Visual Distortions,” 7 pages, International Conference on Computer Communications and Networks (ICCCN), July-Aug. 2017.
  7. S. Alireza Golestaneh and Lina J. Karam, “Spatially-Varying Blur Detection Based on Multiscale Fused and Sorted Transform Coefficients of Gradient Magnitudes,” IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.
  8. Tong Zhu and Lina J. Karam, “Efficient Perceptual-Based Spatially Varying Out-Of-Focus Blur Detection,” IEEE International Conference on Image Processing, pp. 2673-2677, Sep. 2016.
  9. Milind S. Gide, Samuel F. Dodge, and Lina J. Karam, “Visual Attention Quality Database For Benchmarking Performance Evaluation Metrics,” IEEE International Conference on Image Processing, pp. 2792-2796, Sep. 2016.
  10. Bashar Haddad, Lina Karam, Jieping Ye, Nital Patel, and Martin Braun, “Multi-Feature Sparse-Based Defect Detection and Classification in Semiconductor Units,” IEEE International Conference on Image Processing, pp. 754-758, Sep. 2016.
  11. S. Alireza Golestaneh and Lina J. Karam, “Reduced-Reference Synthesized-Texture Quality Assessment Based on Multi-Scale Spatial and Statistical Texture Attributes,” IEEE International Conference on Image Processing, pp. 3783-3786, Sep. 2016.
  12. Samuel F. Dodge and Lina J. Karam, “Understanding How Image Quality Affects Deep Neural Networks,” International Conference on the Quality of Multimedia Experience (QoMEX), 6 pages, June 2016. doi: 10.1109/QoMEX.2016.7498955

Select Sponsored Projects
(* indicates Principal Investigator)


Copyright 1997–2020 Lebanese American University, Lebanon.
Contact LAU | Feedback