Member in the Spotlight: Neil Wachowski

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News and Resources for Members of the IEEE Signal Processing Society

Member in the Spotlight: Neil Wachowski

In this series we introduce a member of our Society by means of an interview. This month, we are happy to introduce Neil Wachowski from Colorade State University, who recently finished his PhD thesis, "Characterization of Multiple Time-Varying Transient Sources from Multivariate Data Sequences.”

What are your research interests in the signal processing field?

I have spent most of my time on problems involving transient detection and pattern recognition but, looking back, I realize that I have found something to like about most areas of signal processing that I have experimented with. Most recently, I have gained an interest in topics at the intersection of signal processing and computer science, such as algorithmic efficiency.

Could you briefly introduce your research?

The goal of my research was to provide generalized approaches for detecting and classifying transient signals using complicated data structures. In essence, I drew inspiration from many classical approaches to related problems that could not be easily extended. For instance, I focus on vector sequences rather than time series data, and always account for the possibility that structured interference many be present.

In your opinion, what was the most impressive result published in IEEE SPS journals and conferences within the last 12 months?

Personally, when I hear the word impressive relative to publications, I think of work that could end up having an extensive impact on a lot of other research in the field, meaning it is very fundamental in a sense. Based on this criterion, one paper that comes to mind is "Ramanujan Sums in the Context of Signal Processing," and its sequel, which recently appeared in the IEEE Transactions on Signal Processing.

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

It seems as though many advances in signal processing are just shy of being successfully introduced to the public due to a prevalent consumer expectation that features should work almost perfectly in almost every usage scenario (Microsoft's Kinect, for example). I think that any technology that can ease this transition, e.g., robust active learning, would significantly extend the reach of the incredible capabilities that have been developed by this community.

In which way have you been connected first with IEEE SPS?

I became connected through my university soon after I started graduate school. It was apparent right from the beginning that the conferences and publications affiliated with the IEEE SPS would introduce a vast majority of the research that is relevant to my interests.

In which way did you know the IEEE SPS e-NewsLetter?

The e-NewsLetter has always been a great centralized resource for getting up to speed on the recent developments in our field.

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