Town Hall Videos

• Town Hall 2021-08-23

• Town Hall 2021-08-24

Why vote for me?

1. History of creating highly successful and innovative initiatives:

   • 25 years of industry collaborations with HP, GE Healthcare, Lilly, etc. on high-impact products.

   • Started IEEE Trans. on Computational Imaging and CI Technical Committee

   • Started Signal Processing Society Special Interest Groups (SIGs) for growing new technical areas

2. History of nurturing diversity and working in teams to align efforts:

   • Created collaborative research group in Integrated Imaging that engages peoples’ strengths

   • Currently advising a diverse team of PhD students (7 female, 7 male)

3. Core leadership philosophy:

   • Improve access and affordability to better serve all members around the globe

   • Be agile and entrepreneurial to capture opportunities

   • Focus on quality in publications

   • Promote SP technology in Speech, Imaging, Sensing, Communications, Data Science, etc.

4. Organizational Thrusts:

   • Reduce member costs and barriers to entry

   • Increase society diversity including underrepresented minorities, women, geographical diversity

   • Promote open access publications

   • Create next generation conferences

   • Drive technology policy

5. Technology Thrusts:

   • Lead in emerging technologies

   • Promote technology initiatives in areas such as:

     • AI and machine learning for science, sensing, and signal processing

     • Autonomous and dynamic SP; graph-based SP; physics aware SP; open-source software

Election Statement Back to top

The disruptions of the past year have brought with them unprecedented challenges and opportunities. As IEEE Signal Processing Society President Elect and President, I would use the society’s technical and financial strength to capitalize on opportunities and meet challenges by taking an agile and entrepreneurial approach.

In my previous roles in the SP Society, I demonstrated my ability to create highly successful and innovative initiatives in, for example, the creation of the IEEE Transactions on Computational Imaging, and the Special Interest Groups (SIG) structure for the on-boarding of new technical topics. I believe in bringing people together to take on challenges, and I see huge opportunities in the society in areas such as AI, machine learning for signals and science, open-source software, community building, and education. My goal would be to continue to establish the society as both a friendly “go-to” forum for the dissemination of impactful results, and the place industry first considers when they want to solve a problem or meet leading experts. We need to continue to expand the depth, breath, and accessibility of technical forums and to employ a wider range of educational media so that authors can more widely disseminate their work and signal processing professionals can more easily meet their needs.

My focus would be on:

Growing Technical Opportunities – Machine learning, algorithms, and software offer vast opportunities in areas ranging from speech to imaging and from practice to theory. We need to provide ever more accessible and affordable technical forums and services to our stakeholders in these rapidly developing areas.

Transition to Open Access – The Society must move rapidly toward OA publication models that are both affordable and sustainable. We will need to develop more OA publication venues while simultaneously developing new business models that leverage the community and third-party investment.

Next Generation Conferences – The pandemic created a unique opportunity to reimagine the concept of conferences. When done well with enhanced social interaction, hybrid and online conferences offer the potential to reduce costs and provide greater access for geographically and technically diverse communities to discuss important topics in depth. I would lead the society to create targeted satellite meetings, to exploit the synergy between conferences and journals, and to expand educational, tutorial, and open-source software development activities.

Increasing Society Diversity – For a successful future, our society must proactively build diversity by reaching out to under-represented communities and partnering with representative organizations to create a pipeline of future talent. We need to demonstrate and communicate that signal processing can be a fun and rewarding career path for young people around the world with all backgrounds and experiences!

Technology Policy – Signal processing technologies play an increasingly important role in the world at large. The SP Society must engage with the resulting issues by helping to set priorities for technology investment both within and outside the society. I would seek to engage with a wide range of government and non-government organizations to enhance the future of the signal processing community.

Based on this, I ask for your vote for President Elect of the IEEE Signal Processing Society!

Biography Back to top

Prof. Bouman has played a seminal role in the creation of the emerging field of Computational Imaging through his research, product development with industry partners, and the creation of both a highly successful long running conference on Computational Imaging and the IEEE Transaction on Computational Imaging. These contributions have led to his induction in the National Academy of Inventors. His research resulted in the first commercial model-based iterative reconstruction (MBIR) system for medical X-ray computed tomography (CT), and he is co-inventor on over 50 issued patents that have been licensed and used in millions of consumer imaging products.

Prof. Charles A. Bouman has served in numerous roles in the IEEE including Editor-in-Chief for the IEEE Transactions on Image Processing; a Distinguished Lecturer for the IEEE Signal Processing Society; a Member at Large of the Board of Governors; a Vice President of Technical Activities for the IEEE Signal Processing Society; a member of the Technical Liaison Committee for the IEEE Transactions on Computational Imaging; and a member of the IMDSP, BISP, and CIC Technical Committees. He has also been an associate editor of the IEEE Transactions on Image Processing, the IEEE Transactions on Pattern Analysis and Machine Intelligence, and the SIAM Journal on Imaging Sciences.

While Prof. Bouman was VP for Technical Directions, he led the creation of the IEEE Transactions on Computational Imaging, the system of Special Interest Groups, and the creation of what has become the Computational Imaging Technical Committee. Prof. Bouman has also been the Vice President of Publications and a member of the Board of Directors for the IS&T Society, and he is the founder and Co-Chair of the long-running SPIE/IS&T conference on Computational Imaging.

Prof. Bouman received a B.S.E.E. degree from the University of Pennsylvania in 1981 and a MS degree from the University of California at Berkeley in 1982. From 1982 to 1985, he was a full staff member at MIT Lincoln Laboratory and in 1989 he received a Ph.D. in electrical engineering from Princeton University. He joined the faculty of Purdue University in 1989 where he is currently the Showalter Professor of Electrical and Computer Engineering and Biomedical Engineering.

Prof. Bouman is a member of the National Academy of Inventors, a Fellow of the IEEE, a Fellow of the American Institute for Medical and Biological Engineering (AIMBE), a Fellow of the society for Imaging Science and Technology (IS&T), a Fellow of the SPIE professional society. He is the recipient of the 2014 Electronic Imaging Scientist of the Year award, the IS&T’s Raymond C. Bowman Award, and in 2019 his PhD student received the IEEE Signal Processing Society Best Student Paper award. Most recently, his paper on Plug-and-Play Priors in the IEEE Transactions on Computational Imaging won the SIAM Imaging Science Best Paper Prize.

Organizational Themes Back to top

1. Transition to OA publications

   • Build sustainable open access publication model

   • Recapture revenue stream

   • New interdisplinary journal/conference on Machine Learning for Science and Signals

2. Next Generation Conferences

   • Hybrid Conferences: On-site with online support, but remain agile

   • Solicit conference proposals for specific venues

   • Create synergy between conferences and journals

3. Diversity

   • Adopt a proactive approach with an outcome-oriented focus

   • Make SPS attractive to URM populations

   • Partner with organizations that have credibility and a track-record of success

   • Make SP a different kind of fun!!

4. Technology Policy

   • Define SP technology roadmap and influence technology policy

   • Meet with national leadership: NSF, AFRL, DOE, NIST, etc.

   • Create standing structure on Technology Policy to provide leadership

Technical Themes Back to top

1. Adaptation Strategy:

   • Build on Special Interest Groups (SIGs)

   • Create more ongoing processes that roll in new technology areas

2. AI/Machine Learning Context:

   • Signal Processing Society should leverage government and industry initiatives

   • National Security Commission on Artificial Intelligence

   • $250B U.S. Innovation and Competition Act

3. Interdisciplinary initiatives in Machine Learning for Science and Sensing:

   • Why is this a natural strength for SPS?

     • More emphasis on quantitation and reproducibility

     • Better interation of signal modeling and physics

   • What is the opportunity?

     • Current ML conferences present huge barriers to entry

     • Create venues that include physical and ML scientists

4. Emerging Areas:

   • Autonomy and dynamic signal processing

   • Graph based signal processing

   • Physics aware machine learning

   • Support and leverage Open-Source communities