Editorial Committee Members
Dr. David Atienza is an associate professor of electrical and computer engineering, and the head of the Embedded Systems Laboratory (ESL) at the Swiss Federal Institute of Technology Lausanne (EPFL), Switzerland. His research interests include system-level design methodologies for energy-efficient multi-processor system-on-chip (MPSoC) and Internet-of-Things (IoT) systems, including ultra-low power system architectures for edge AI and wearable devices targeting healthcare and medical applications, and new 2-D/3-D energy- and thermal-aware design for MPSoCs. He is a co-author of more than 350 publications in peer-reviewed international journals and conferences, several book chapters, and eleven patents in these fields. He has earned several best paper awards and he is (or has been) an Associate Editor of IEEE TC, IEEE D&T, IEEE TCAD, IEEE T-SUSC, ACM TECS and ACM JETC. He was the Technical Programme Chair of IEEE/ACM DATE 2015 and General Programme Chair of IEEE/ACM DATE 2017. Dr. Atienza has received among other awards the ICCAD Ten Year Retrospective Most Influential Paper Award in 2020, the DAC Under-40 Innovators Award in 2018, the IEEE TCCPS Mid-Career Award in 2018, an ERC Consolidator Grant in 2016, the IEEE CEDA Early Career Award in 2013, the ACM SIGDA Outstanding New Faculty Award in 2012, and a Faculty Award from Sun Labs at Oracle in 2011. He is an IEEE Fellow and an ACM Distinguished Member.
Arrate Muñoz-Barrutia received an MS in Telecommunication engineering from the Public University of Navarra, Pamplona, Spain in 1997. For her Final Diploma Project, she worked on optoelectronics, as an ERASMUS student, at King's College London, UK. Then, she moved to the Swiss Federal Institute of Technology Lausanne (EPFL) where she attended the Doctoral School in Communication Systems. She received her PhD from the same university working under the supervision of Prof. Michael Unser on multiresolution image processing in 2002. After that, she was a research scientist at the Centro de Estudios e Investigaciones Técnicas (CEIT-IK4) being holder of a Torres Quevedo grant. In 2005, she joined the Cancer Imaging Lab at the Center for Applied Medical Research (CIMA) of the University of Navarra as a staff scientist and was successively adjunct, assistant and associated professor at the Engineering School of this university. From 2005 to 2010, she was a holder of a Ramon y Cajal contract. She has enjoyed research stays at the California Institute of Technology (2008, 2009) and Johns Hopkings University (2015). From 2014, she is with the Bioengineering and Aerospace Engineering Department of the Universidad Carlos III de Madrid. From July 2018, as a tenured Associated Professor.
Dr. Muñoz-Barrutia has more than 80 contributions in international peer reviewed journals and top conferences in the field of the Biomedical Image Processing. Her main research motivation relates to the development of novel imaging technologies to improve early disease detection, disease characterization and therapy delivery.
She is member of the Bio Imaging and Signal Processing Technical Committee of the IEEE Signal Processing Society (vice-chair (2015), chair (2016-2017) and past-chair (2018)) and was also a member of the IEEE Life Sciences Initiative Steering Committee (2015-2017). She serves as an Associate Editor of the IEEE Transactions on Medical Imaging (from 2015) and BMC Bioinformatics (from 2014). She was Associate Editor of the IEEE Signal Processing Letters (from 2012 to 2015).
Omer T. Inan received his B.S., M.S., and Ph.D. degrees in Electrical Engineering from Stanford University in 2004, 2005, and 2009, respectively. He worked at ALZA Corporation in 2006 in the Drug Device Research and Development Group. From 2007-2013, he was chief engineer at Countryman Associates, Inc., designing and developing several high-end professional audio products. From 2009-2013, he was a visiting scholar in the Department of Electrical Engineering at Stanford. In 2013, he joined the School of ECE at Georgia Tech as an assistant professor. Dr. Inan is generally interested in designing clinically relevant medical devices and systems, and translating them from the lab to patient care applications. One strong focus of his research is in developing new technologies for monitoring chronic diseases at home, such as heart failure.
Dr Marianna Laviola is a Research Fellow at the Division of Clinical Neuroscience - School of
Medicine, University of Nottingham (UK). Marianna obtained her Master’s degree in Biomedical
Engineer and PhD in Bioengineering at Politecnico of Milano (Italy).
Her main research interests include the bioengineering of respiratory and cardiovascular systems,
the development of computational models of human physiology and pathology, animal models of
respiratory diseases (i.e. Acute Respiratory Distress Syndrome, [ARDS]), physiological measurements and ultrasonography of the diaphragm.
Her research at The University of Nottingham focuses on the development of computational patientsurrogates to investigate the physiological mechanisms of apnoea and rescue strategies during severe hypoxemia (e.g. in lean, obese, and pregnant subjects), to better understand the
pathophysiology of critical illness diseases (e.g. ARDS, COVID-19, cardiac arrest) and to examine
tailored ventilation and intra/post resuscitation strategies.
Marianna is an Associate Fellow at Higher Education Academy (UK) and she is currently supervising
three PhD students. She is Co-I and Research Co-I in several projects funded by the major UK
government agencies and industry. Her works have been published in the leading journals of
bioengineering, anaesthesia and critical care.
Marianna regularly reviews for the following journals: IEEE Journal of Biomedical and Health
Informatics, Respiratory Care, BMJ Open Respiratory Research and Frontiers in Medicine and she is also a member of the editorial board of the European Respiratory & Pulmonary Disease journal.
Finally, Marianna is the IEEE Engineering in Medicine and Biology Society Young Professional
representative (R 1-10) 2020 -2022.
Xi Long received the B.Eng. degree in electronic information engineering from Zhejiang University, Hangzhou, China, in 2006, and the M.Sc. and the Ph.D. (cum laude) degrees in electrical engineering from the Eindhoven University of Technology, Eindhoven, The Netherlands, in 2009 and 2015, respectively. From 2010 to 2011, he worked for Tencent in China on data mining and user research. He has ten years of R&D experience in healthcare industry, with Philips Research.
He is currently a Senior Scientist at Philips Research and an Assistant Professor at the Department of Electrical Engineering, Eindhoven University of Technology, The Netherlands. He advises, coordinates and participates in many collaborative projects with industrial, academic and/or clinical partners. His research interests include signal processing and machine learning in biomedical/healthcare applications. He has published over 90 scientific papers and reports in these fields and his inventions led to more than 10 patent applications or first filings.
Peter B. Shull received the B.S. in Mechanical Engineering and Computer Engineering from LeTourneau University in 2005 and M.S. and Ph.D. degrees in Mechanical Engineering from Stanford University in 2008 and 2012, respectively. From 2012 to 2013, he was a postdoctoral fellow in the Bioengineering Department of Stanford University. He is currently an Associate Professor in Mechanical Engineering at Shanghai Jiao Tong University. His research interests include wearable systems, hand gesture recognition, artificial intelligence, real-time movement sensing and feedback, and biomechanics.
Brian A. Telfer is a Senior Staff Member in the Human Health and Performance Systems Group at MIT Lincoln Laboratory. He received a BS in Electrical Engineering from Virginia Tech and an MS and PhD in electrical engineering from Carnegie Mellon. After working at the Naval Surface Warfare Center, where he was funded with an Office of Naval Research Young Scientist Award, he joined Lincoln Laboratory in 1995. His contributions have been in the areas of biosignal and image processing, machine learning, artificial intelligence and systems analysis, with applications to radar, physiological status monitoring, AI-enabled medical imaging, and trauma care. The focus of this work has been on prototyping and technology transition, with successful transitions leading to government contracts to industry for several hundred million dollars. Dr. Telfer has organized body sensor network workshops and has served as Technical Program Co-Chair for the International Body Sensor Networks Conference in 2013 and 2015, as well as three-time Guest Editor for the Journal for Biomedical and Health Informatics and for the journal Sensors. He is a Senior Member of the IEEE, a member of the IEEE EMBS Technical Committee on Wearable Devices, and received the 2018 IEEE Region 1 Technological Innovation Award. He has been a member of several MIT Lincoln Laboratory internal funding selection boards, as well as technical lead for MIT LL’s Introduction to Radar Systems course and other radar courses. He has led several biomedical studies for senior leaders in the U.S. Government and has co-authored over 70 publications.
Dr. Shanshan Wang is currently an Associate Professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences. She received her dual Ph.D degree from the University of Sydney and Shanghai Jiaotong University respectively in information technologies and biomedical engineering. Dr. Wang was a finalist for the Australian John Makepeace Bennett Best Thesis Award and won the 2018 OCSMRM Outstanding Research Award. Her research interests include machine learning, fast medical imaging and biomedical signal analysis. Dr. Wang has published over 70 journal and conference papers in these areas. Her fast imaging work published in Physics in Medicine and Biology was selected as a “2016 Research Highlight” and “Featured Article”. She has been the moderator for machine learning for image reconstruction and processing scientific sessions of ISMRM from 2018-2020. She is also the area chair for MIDL 2020. She also was invited to give a plenary talk in the “Gordon conference on in vivo MR” fearturing her pioneering work in accelerating MR imaging with deep learning. Dr. Wang is the associate editor of “Biomedical Signal Processing and Control”, the editorial board member of “Magnetic Resonance in Medicine” and “IEEE reviews in biomedical engineering”, on the ISMRM web editorial committee and also serves as a reviewer for many journals and conferences (IEEE TMI, TIP, TBME, TSP, MRM, PMB, SP). She received the journal awards for “outstanding contribution in reviewing for signal processing” and “distinguished service to MRM”. She is the principal investigator for oven 10 research projects in learning image reconstruction and analysis for medical imaging supported by different grant agencies such as NSFC and NSFG. She is IEEE Senior member, OCSMRM life member, BOT member and ISMRM member.