Taking the Next Step for IEEE Signal Processing Magazine

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Taking the Next Step for IEEE Signal Processing Magazine

By: 
Robert W. Heath, Jr.

I am pleased to start my three-year term as editor-in-chief (EIC) of IEEE Signal Processing Magazine (SPM) as you read this first issue of the new year. Let me introduce myself. I started my career in signal processing at the University of Virginia. As I dreamt of becoming a patent attorney, I made my way through a B.S. degree in electrical engineering. However, while sitting in my  rst course on signal processing, I realized the magic of signals, systems, and transforms. It was during that summer of 1995 that I gave up on the prospects of law school and completed a research internship with my instructor, Prof. Georgios Giannakis (who later moved to Minnesota and is now a legend in the signal processing community). My salary barely paid the rent, but the experience was priceless. I spent time building up mathematical fundamentals and applying them to problems in image processing and wireless communication. I came to realize that one could view many engineering problems through the lens of signal processing.

Almost 20 years ago, I left the Uni versity of Virginia to attend graduate school in Stanford, California. For my Ph.D. degree, I was lucky to get in early on the revolution of multipleinput, mul tipleoutput (MIMO), implementing sev eral space–time processing algorithms on a cuttingedge base station prototype (at that time, it was Global System for Mobile Communications). This also led Digital Object Identifier 10.1109/MSP.2017.2770481 Date of publication: 9 January 2018 to an appreciation for making algorithms work on real signals. The late 1990s were a glorious time to be in Silicon Valley. There was so much excitement around signal processing technology, I felt proud to be an engineer. I even worked in a startup on the emerging area of MIMO communication with my advisor, Prof. Arogyaswami Paulraj. It was not a suc cess, and I still have the worthless stock certificate in my office. Prof. Paulraj’s next company was a hit, but by that time I had graduated and joined The University of Texas at Austin.

I want to relate my background to you for three reasons. First, it was my exposure to signal processing through education that brought me here. I think that SPM is first and foremost a tool where we educate our current and future IEEE Signal Processing Soci ety members. Second, I believe that sig nal processing is about the marriage of theory and practice (as I had to quickly learn while working toward my Ph.D. degree), where there is a natural inter play between practical systems, the data they generate, mathematical models, and new theory. SPM needs to embrace all of these aspects, to provide content that appeals to academics, entrepre neurs, and practicing engineers. Third, we are in the midst of the golden age for engineering. Signal processing is now the brain behind the revolutions in smart cities, transportation, energy, and health, as it was the new technology behind the Silicon Valley startups in the 1990s. SPM should be the illuminated publication for our era and a resource for all engineers and mathematicians.

Outgoing EIC Prof. Min Wu has given three years of outstanding leadership. She worked hard with her team of area editors, the Editorial Board, and associ ate editors to deliver striking content in diverse areas. Unfortunately, this creates two significant challenges for me. First, the diversity of topics already covered in special issues and features is remark able. But I will do my best to bring quality content to the magazine and continue to expand the scope beyond the print media. Second, the magazine already has an astonishing impact factor and is widely read. Can I get more people to read and cite SPM? Hopefully. More specifically, my plan as EIC is to enhance the content of the magazine and make it stronger in three main areas: industry, commercial ization, and humor.
SPM could use more industrial content from practitioners. The style of the articles and the content is immensely accessible to engineers in industry, yet the flow of information seems unidirectional. I plan to have more content from leaders and practitioners in industry. For example, I will be exploring having invited columns explaining “how I use signal processing.’’ These would focus more on a personal experience and not a specific application as in the “Applications Corner” column. In addition, I will explore the curation of technical articles that describe key uses of signal processing in industry, with theinformation measures as optimal infer ence problems can be used to derive learning algorithms, such as in [6], as well as estimates of information mea sures [5], [11].

Author

Osvaldo Simeone (osvaldo.simeone@ kcl.ac.uk) received his M.Sc. degree (with honors) and his Ph.D. degree, both in information engineering, from Politecnico di Milano, Italy, in 2001 and 2005, respectively. He is a profes sor of information engineering with the Centre for Telecommunications Research in the Department of In formatics of King’s College London. From 2006 to 2017, he was a faculty member with the Electrical and Computer Engineering Department at the New Jersey Institute of Technology. His research interests include wireless communications, information theory, opti mization, and machine learning. He is a corecipient of the 2017 JCN Best Paper Award, the 2015 IEEE Com munication Society Best Tutorial Paper Award, and the Best Paper Awards of IEEE SPAWC 2007 and IEEE WRECOM 2007. He was award ed a European Research Council Con solidator Grant in 2016. He is a Fellow of the IEEE.

References

[1] T. M. Cover and J. A. Thomas, Elements of Information Theory. Hoboken, NJ: Wiley, 2012.
[2] C. M. Bishop, Pattern Recognition and Machine Learning. New York: Springer, 2006.
[3] I. Csiszár, “Axiomatic characterizations of infor mation measures,” Entropy, vol. 10, no. 3, pp. 261– 273, Sept. 2008.
[4] P. Grünwald and P. Dawid, “Game theory, maxi mum entropy, minimum discrepancy and robust Bayesian decision theory,” Ann. Statist., vol. 32, no. 4, pp. 1367–1433, 2004.
[5] X. Nguyen, M. J. Wainwright, and M. I. Jordan, “Estimating divergence functionals and the likelihood ratio by convex risk minimization,” IEEE Trans. Inform. Theory, vol. 56, no. 11, pp. 5847–5861, Nov. 2010.
[6] S. Nowozin, B. Cseke, and R. Tomioka, “fGAN: Training generative neural samplers using varia tional divergence minimization,” in Proc. Neural Information Processing Systems Conf., Barcelona, Spain, 2016, pp. 4240–4248.
[7] C. E. Shannon, “A mathematical theory of com munication,” Bell Syst. Tech. J., vol. 27, no. 3, pp. 379–423, July 1948.
[8] J. Jiao, T. A. Courtade, A. No, K. Venkat, and T. Weissman, “Information measures: The curious case of the binary alphabet,” IEEE Trans. Inform. Theory, vol. 60, no. 12, pp. 7616–7626, Dec. 2014.
[9] J. C. Duchi, Lecture Notes for Statistics 311/ Electrical Engineering 377. Stanford, CA.
[10] M. H. DeGroot, “Changes in utility as informa tion,” Theory Decis., vol. 17, no. 3, pp. 287–303, Nov. 1994.
[11] V. Berisha, A. Wisler, A. Hero, and A. Spanias, “Empirically estimable classification bounds based on a nonparametric divergence measure,” IEEE Trans. Signal Proc., vol. 64, no. 3, pp. 580–591, Feb. 2016.
[12] M. Arjovsky, S. Chintala, and L. Bottou, “Wasserstein GAN,” arXiv Preprint, arXiv:1701.07875, Jan. 2017.

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