Department of Electrical and Systems Engineering

Transcription

1 Department of Electrical and Systems Engineering Joseph A. O Sullivan, Hiro Mukai, Bijoy Ghosh, Ronald S. Indeck Report of the EE-SSM Merger Committee National Council Meeting, December 5, 2002

2 Merger Timeline: Previous Committee Future of Electrical Engineering at Washington University Report to National Council, April 2002 Re-establish a culture of excellence renewed emphasis on research new departmental leadership faculty turnover and renewal Reorganize to leverage existing strengths CoE to CS to enable CoE to thrive, combine EE with SSM to form Department of Electrical and Systems Engineering Maintain high caliber undergraduate program focus on quality more than quantity strengthen connection to research

3 Merger Timeline First Major Consequence: four Computer Engineering faculty moved from EE to CS July 1; renamed Department of Computer Science and Engineering EE-SSM merger committee appointed May 2002 Organized EE-SSM faculty meetings June-September Led discussions on future research directions Dean Byrnes presentation to faculty Sept. 20 Faculty vote to merge Sept. 30

4 Future Directions Leverage existing strengths that provide unique opportunities for Washington University Identify areas of significant future growth in Electrical and Systems Engineering Ongoing process Systems Biology and Biomedical Imaging Sensor and Control Networks Security Technologies

5 Systems Biology Oltvai, Barabasi, Science, Oct. 2002

6 Systems Biology Cells and microorganisms have an impressive capacity for adjusting their intracellular machinery in response to changes in their environment, food availability and developmental state. Although molecular biology offers many spectacular successes, detailed inventory of genes, proteins and metabolites is not sufficient to understand the cell s complexity. In a cell, the information storage, processing and execution takes place at various distinct levels of organization: the cells genome, transcriptome, proteome and metabolome. Integration of different organizational levels have forced us to view cellular functions as distributed among groups of heterogenous components that all interact within a network. Although individual components are unique to a given organism, topological properties of cellular networks share a surprising similarity to natural and social networks.

7 Systems Biology From Genetic sequencing to a systems level understanding of Pathways, Motifs, Modules and Full Network. Goal: understand the governing laws of cell biology.

8 Biomedical Imaging Washington University is a leader in Imaging Science and Engineering Biomedical imaging. CT, MRI, Ultrasound, Optical Imaging. Genomic imaging: gels, microarrarys Integrated CT-PET micro- CT, micro-pet, micro-mr, and micro-optical imaging NIBIB; $112M in FY2002; requested $121M in FY2003 Targeted initiative: biomedical sensors Targeted initiative: molecular imaging Imaging fundamentals. Related imaging.

9 Example: Micro-CT Imaging SkyScan website; images of the ankle of a living rat

10 Biomedical Imaging Biomedical Imaging Washington University is a leader in Imaging Science and Engineering Biomedical imaging. CT, MRI, Ultrasound, Optical Imaging. Genomic imaging: gels, microarrarys Integrated CT-PET micro- CT, micro-pet, micro-mr, and micro-optical imaging NIBIB Imaging fundamentals. Physics of sensors and scenes; volumetric and dynamic modeling; optimization theory; signal and image processing; information theory; implementations; complexity theory Remote sensing; object recognition; parameter estimation; semantic information; multisensor modeling Related imaging. Human and computer vision; multiresolution analysis; biometrics including fingerprints, retinal scans, hand scans, face images, DNA

11 Hiro Mukai J. A. O Sullivan, H. Mukai,

12 Biomedical Imaging Biomedical Imaging Washington University is a leader in Imaging Science and Engineering Biomedical imaging. CT, MRI, Ultrasound, Optical Imaging. Genomic imaging: gels, microarrarys Integrated CT-PET micro- CT, micro-pet, micro-mr, and micro-optical imaging NIBIB Imaging fundamentals. Physics of sensors and scenes; volumetric and dynamic modeling; optimization theory; signal and image processing; information theory; implementations; complexity theory Remote sensing; object recognition; parameter estimation; semantic information; multisensor modeling Related imaging. Human and computer vision; multiresolution analysis; biometrics including fingerprints, retinal scans, hand scans, face images, DNA