This is the latest of an ongoing series of interviews with the Editorial Board of our new Open Access journal, Biological Imaging. Next up, we have Professor Jong Chul Ye an Executive Editor for the journal and Associate Editor for IEEE Trans. on Medical Imaging, and a Senior Editor of IEEE Signal Processing Magazine.

Can you tell us a bit about your background, and what your current research is focused on?

I received BSc. and MSc. degrees from Seoul National University (South Korea), and a PhD. from Purdue University, West Lafayette (USA). Before joining KAIST, I was a Senior Researcher at Philips Research, GE Global Research in New York, and was a postdoctoral fellow at University of Illinois at Urbana Champaign. I served as an associate editor of IEEE Trans. on Image Processing, IEEE Trans. on Computational Imaging, and an editorial board member for Magnetic Resonance in Medicine. I am currently an associate editor for IEEE Trans. on Medical Imaging, and a Senior Editor of IEEE Signal Processing Magazine. In addition, I am an IEEE Fellow, Chair of IEEE SPS Computational Imaging TC, and IEEE EMBS Distinguished Lecturer. I was a General Co-chair for 2020 IEEE Symp. On Biomedical Imaging (ISBI) (with Mathews Jacob)

What has been your biggest challenge/greatest achievement in your career so far?

My research activities are primarily focused on signal processing and machine learning approaches for high-resolution and high-sensitivity image reconstruction from real world bio-medical imaging systems. One of the most important and challenging issues in this regard is overcoming the fundamental limitations of resolution and sensitivity with minimal invasiveness. Such problems pose interesting challenges that often lead to investigations of fundamental problems in various branches of physics, mathematics, signal processing, biology, and medicine.

I would say that my approaches to biomedical imaging problems are unique in the sense that I believes that actual bio-medical imaging applications are a source of endless inspiration for new mathematical theories. Specifically, I see that the imaging system and algorithm design problem is indeed a signal sampling problem under physical and biological constraints, so the resolution and sensitivity benefits can be maximized rigorously under given measurement samples. Based on this approach, I believe that I have significantly contributed in this field by introducing several pioneering compressed sensing and machine learning approaches. For example, my group was the first winner of the 2009 Recon Challenge at the ISMRM workshop with k-t FOCUSS algorithm, and the runner-up at 2016 Low Dose CT Grand Challenge organized by the American Association of Physicists in Medicine (AAPM) with the world’s first deep learning algorithm for low-dose CT reconstruction.

Why did you decide to become an Executive Editor?

I have known Jean-Christophe Olivo Marin for many years and his leading role in biological imaging in both academia and industry. Therefore, when he shared his vision on the new journal on biological image computing, I liked the idea and accepted his offer to become an Executive Editor. In particular, when he invited me to oversee the machine learning area, which is a growing area in biological imaging, I felt so honored and accepted the role without any hesitation.

How will Biological Imaging benefit your research field?

Biological imaging generates huge amount of data, which needs to be acquired and analyzed carefully to extract the information as much as possible for many applications. In this sense, machine learning has become indispensable part of biological imaging. My research focuses on this field, so I believe the journal Biological Imaging will help me to broaden my knowledge in this field

What excites you about Biological Imaging?

There are many journals for medical imaging, microscopy, bioinformatics, etc. however, it is surprising to find any high-quality journals that cover the broad spectrum of biological imaging from acquisition, analysis, to applications. I believe that Biological Imaging fills the missing gap so that researchers working in biological imaging can publish and read high quality papers from acquisition to applications.

Why should authors publish in Biological Imaging?

Biological Imaging covers broad spectrum of the imaging research for biological applications. Previously, people who are interested in studying this field would have to search specific papers from various journals, but Biological Imaging provides an extensive array of research in this field, so it will be educative so that growing number of researchers may read articles in this journal.

How will this journal impact bioimaging research?

It aims to become a venue for interdisciplinary research covering various aspects of biological imaging.

How do authors benefit from Biological Imaging being open access?

Research articles in open access are more and more cited nowadays since many researchers use Google scholar to search papers, and open access articles can be easily downloadable. Therefore, authors may benefit from more exposure to the readers that may lead to the increased citation numbers for authors.

Biological Imaging is a single open access forum for important bioimaging research. It publishes original reports, reviews and other article types on techniques and methods that use imaging approaches including: microscopy, image acquisition and processing, data mining and analysis, mathematical modelling and machine learning. Submit your papers here and follow the journal on Twitter, @blg_journal.