Member in the Spotlight: Hamid Krim

In this series, we aim to introduce senior society members and other experts of the signal processing field. This month, we are happy to introduce Prof. Hamid Krim in the Electrical and Computer Engineering Department, North Carolina State University, Raleigh, leading the Vision, Information and Statistical Signal Theories and Applications group, whose research interests are in statistical signal and image analysis and mathematical modeling with a keen emphasis on applied problems.

When did you first come into touch with signal processing? What was your motivation of following a career in this domain?

I have done communications since my graduate days at Univ. of Washington in the 80’s to subsequently join Bell Laboratories where I was involved in R&D work in Digital Communications. I was then increasingly getting interested in estimation problems, and the more I did the more I realized I should get my Ph.D., which I did in the 90’s and the rest is history.

What was the first signal processing algorithm you ever implemented? In which context was it used?

The first SP problem I worked on was on adaptive filtering to compensate flat fading of Digital Radio for long-haul telecommunications. I actually also implemented this in hardware duly integrated in the system. So testing, and impact over error rate and all, also had to be carefully done.

What is the most significant change you saw in the field of signal processing from the time you did your PhD until now?

It may sound non-objective, but I actually see SP everywhere and hence it is sort of difficult to pinpoint out something specific ….but the one definite trend that I witnessed is how the complexity of the problems SP researchers  took on, also required an unprecedented amount of sophisticated mathematics, which also allowed a lot of applied mathematicians and physicists to join the effort as well. This new highly inter-disciplinary approach to SP is a change and a good and enriching one, in my opinion.

What are your current research interests in the signal processing field and how these fit in the more general SP research trends?

I have always viewed myself as an engineer and continue to do so, and to fulfill that mission I stay true to applications (some off the beaten path). But I also like to approach problems in an as formal way as possible, with a solution, which is probably widely valid. When I say formal, I mean mathematical with an applied goal, as I always tell my students, we are engineers NOT MATHEMATICIANS. My interests in applications has broadened tremendously, from array processing to now vision and imaging, network signal processing, machine learning and optimization, with a now a great addition of so-called unstructured data. This area, I believe, presents a tremendously fertile ground for innovative research, from a theoretical viewpoint as well as application viewpoint.

Could you introduce an important state-of-the-art research issue (or technology) in this field?

What I find fascinating is how applied problems bring seemingly distinct areas of mathematics and make a continuum out of them. We are now witnessing a wedding of geometry/topology and optimization, which is called upon by very difficult applied problems in so-called machine learning addressing problems in social media.

From your experience, is there something the signal processing society can learn from other societies?

Definitively! The first is an openness to new tools and new ideas, which almost always have to come from outside our area. We tend to be conservative in what we view as being acceptable and within the main stream of SP.

What would be your advice to a new PhD student who wants to start a career in signal processing?

Don’t be afraid of being an “artist” and a “dreamer”, learn physics, biology, mathematics, every area can always teach something and provide an insight to that next great idea. That is my humble opinion!

You are a member of the 2015 Class of Distinguished Lecturers. Could you tell us about a particularly inspiring discussion or event that occurred during one of your lectures?

Having the honor of serving as a DL for IEEE-SP has probably been the most interesting part of my many years of lecturing as a faculty, delivering seminars, and serving in other capacities at IEEE and other places. It has been an enriching experience for me in the many interesting discussions I have had in my tours. The one that jumps to mind though is this Australian student who came up to me after my talk probing me with more questions about shapes and the geometry of shapes. He was working on some zoology/environmental preservation problem involving different wild animals. He believed that some result I had given could be a basis for a solution of differentiating two closely related animals, but only one of which he wanted to track.  We have had some email exchanges since, and I put him in touch with a former student who had all the up-to-date tools he could certainly use to investigate.

Brief biography

As a Member of Technical Staff at AT&T Bell Labs, he has worked in the areas of telephony and digital communication systems/subsystems. Following an NSF postdoctoral fellowship at Foreign Centers of Excellence, LSS/University of Orsay, Paris, France, he became a Research Scientist at the Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, performing and supervising research. He is presently on the faculty in the ECE Department, North Carolina State University, Raleigh, leading the Vision, Information and Statistical Signal Theories and Applications group, whose research interests are in statistical signal and image analysis and mathematical modeling with a keen emphasis on applied problems. Dr. Krim is also an Associate Editor of IEEE Transactions on Signal Processing.