Advisory Board and Editors Computer Vision
Juan Pedro Dominguez-Morales
Prof. Juan Pedro Dominguez-Morales was born in Sevilla (Sevilla, Spain) in 1992. He received the B.S. degree in computer engineering, the M.S. degree in computer engineering and networks, and the Ph.D. degree in computer engineering (specializing in neuromorphic audio processing and spiking neural networks) from the University of Seville, in 2014, 2015 and 2018, respectively. His Ph.D. was granted with a research grant from the Spanish Ministry of Education and Science. Since January 2019, he has been working as Assistant Professor in the same university. He is member of the Robotics and Technology of Computers Lab since 2015. His research interests include neuromorphic engineering, spiking neural networks, audio processing and deep learning. He has been an IEEE member for four years.
Ahmed Elazab
Ahmed Elazab received his Ph.D. degree in pattern recognition and intelligent systems from Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences, China, Jan 2017. He was a postdoctoral research fellow from Jan 2018 to April 2020 at the School of Biomedical Engineering, Shenzhen University, Shenzhen, China where he is currently a research associate since Jan 2021. Dr. Elazab has authored and co-authored more than 80 peer-reviewed papers and has been a reviewer in prestigious peer-reviewed international journals. His main research interests include machine and deep learning, medical image analysis, brain anatomy analysis, and computer-aided detection and diagnosis.
Cornelia Fermuller
Cornelia Fermüller’s research is in the areas of Computer Vision and Human Vision. She has studied multiple view geometry and statistics, and her work includes view-invariant texture descriptors, 3D motion and shape estimation, image segmentation, and computational explanations and predictions of optical illusions. Her recent work focuses on the integration of perception, action and high-level reasoning to develop cognitive robots that can understand and learn human manipulation actions.
Carlos Fernandez-Lozano
Dr. Carlos Fernandez-Lozano is an Associate Professor at the University of A Coruña (UDC). He is a biomedical data scientist with a deep interest in discovering the complex relationships between different biological levels. His research track is multidisciplinary as he is trained in computer science, machine learning, bioinformatics, and biostatistics. His research line is focused on how biological interactions are manifested at the disease level through the use, development, and application of kernel-based computational approaches that integrate different levels of biological data on the microorganism, gene, protein, and medical imaging axis.
Simone Fontana
Prof. Simone Fontana is an assistant professor at Università degli Studi di Milano - Bicocca.
His main research activity is in the field of 3D robot perception, with special attention to point clouds registration, a problem for which he has developed a benchmark. More recently, Dr. Fontana's research has focused on the use of informatics techniques for neuropsicology and neuroscience.
He is a co-investigator of the DriveWin project, which aims to investigate the effects of different types of non-invasive neurostimulation on attention while driving. Attention was assessed on a driving simulator and two age groups were compared.
Prof. Fontana is also a lecturer at the School of Law and at the Advanced Specialization School in Neuropsychology.
Tarek Gaber
Tarek Gaber is a Senior Lecturer (Associate Professor) at the University of Salford (UK) and a Full Professor of Computer Science at Suez Canal University (Egypt). He has over two decades of academic and research experience across cybersecurity, artificial intelligence (AI), secure systems, and Safe AI. His work focuses on developing resilient AI models, secure digital infrastructures, and innovative applications for industry and public sector transformation. Dr. Gaber has authored over 100 scholarly publications, including journal articles, conference papers, book chapters, and edited volumes — with more than 40 published in Q1 journals. He has led or co-led research projects exceeding £6 million in funding, supported by Innovate UK, UKRI, Research England, and UKAEA. His research excellence has earned him recognition among Stanford University’s top 2% of scientists globally. He has served as Programme Leader for the MSc Cyber Security programme at Salford, contributed to several Knowledge Transfer Partnerships (KTPs), and engaged in interdisciplinary projects with SMEs to deploy secure and explainable AI solutions. Dr. Gaber is a Fellow of the UK Higher Education Academy (FHEA), a member of IEEE, and frequently serves as a keynote speaker, journal reviewer, and editorial board member in his field.
Juergen Gall
Juergen Gall obtained a Ph.D. in computer science from the Saarland University and the Max Planck Institut für Informatik in 2009. He was a postdoctoral researcher at the Computer Vision Laboratory, ETH Zurich, from 2009 until 2012 and senior research scientist at the Max Planck Institute for Intelligent Systems in Tübingen from 2012 until 2013. Since 2013, he is professor at the University of Bonn and head of the Computer Vision Group.
Jeonghwan Gwak
Dr. Jeonghwan Gwak received his Ph.D. degree in Machine Learning and Artificial Intelligence from Gwangju Institute of Science and Technology, Gwangju, Korea in 2014. From 2002 to 2007, he worked for several companies and research institutes as a Researcher and a chief technician. From 2014 to 2016, he worked as a Postdoctoral Researcher in GIST, and from 2016 to 2017 as a Research Professor. From 2017 to 2019, he was a Research Professor in Biomedical Research Institute & Department of Radiology at Seoul National University Hospital, Seoul, Korea. From 2019, he joined Korea National University of Transportation (KNUT) as an Assistant Professor and since 2021, he is an Associate Professor. He is the Director of the Algorithmic Machine Intelligence laboratory. His current research interests include deep learning, computer vision, image and video processing, AIoT, fuzzy sets and systems, evolutionary algorithms, optimization, and relevant applications of medical and visual surveillance systems.
Richang Hong
Dr. Richang joined School of Computer and Information, Hefei University of Technology (HFUT) as a Professor. His current research interests include multimedia content analysis and social media. He has authored over 100 journal and conference papers in these areas and the Google Scholar citations for those papers is more than 7000. He served as editor of the IEEE Transactions on Big Data, Information Sciences, Signal Processing and Neural Processing Letter, and the guest editors of several international journals, a steering committee member of MMM (international conference on multimedia modeling) conference series since 2019, and the technical program chairs of the 22th International conference on Multimedia Modeling 2016 and the 9th ACM International Conference on Internet Multimedia Computing and Services 2017. He served as area chairs of ACM Multimedia 2017,2018,2019, 2020 and a technical program committee member of over 20 prestigious international conferences, and a reviewer of over 20 prestigious international journals. He is a recipient of the Best Paper Award in ACM Multimedia 2010, Best Paper Award in ACM International Conf. on Multimedia Retrieval 2015 and Best Paper Honorable Mention Award of IEEE trans. Multimedia 2015. Dr. Hong is the CCF technical committee member on multimedia and the secretary of the ACM SIGMM China Chapter.
Yan Chai Hum
Dr. Hum Yan Chai is a researcher in artificial intelligence and computer vision. He received his B.Eng degree in biomedical engineering from the Universiti Teknologi Malaysia (UTM). He is currently serving as an Assistant Professor in the Department of Mechatronics and Biomedical Engineering, Lee Kong Chian Faculty of Engineering and Science, Universiti Tunku Abdul Rahman.
Zhaojie Ju
Zhaojie Ju (M'08-SM'16) received a BSc degree in automatic control and a MSc degree in intelligent robotics from the Huazhong University of Science and Technology, China. He received a Ph.D. degree in intelligent robotics from the University of Portsmouth, U.K. He held research appointments at University College London, London, U.K., before he started his independent academic position at the University of Portsmouth, in 2012. He has authored or co-authored over 200 publications in journals, book chapters, and conference proceedings, and received five Best Paper Awards, one book award, and one Best AE Award in ICRA2018. His research interests include machine intelligence, pattern recognition and their applications on human motion analysis, multi-fingered robotic hand control, human–robot interaction and collaboration, and robot skill learning.
Goo Jun
I am currently an assistant professor at the University of Texas Health Science Center at Houston. I work on statistical genetics, computational biology, bioinformatics, and sequence data analysis. With backgrounds in machine learning and data mining, my research is focused on development of computational and statistical methods for analysis of massive data to understand genetics and biology of complex traits. I have been working on the analysis of large-scale next-generation sequencing data, for which I developed statistical models and software pipelines for detecting sample contamination, variant discovery, machine-learning based variant filtering, and genotyping of structural variations. I also work on genetics of diabetes, obesity, and related traits and study of metabolomic and microbiome compositions related to genetics of common and complex traits.