Advisory Board and Editors Data Mining & Machine Learning
Harry Hochheiser
My research has covered a range of topics, including human-computer interaction, information visualization, bioinformatics, universal usability, security, privacy, and public policy implications of computing systems. I am currently working on a variety of NIH-funded projects, including areas such as bioinformatics research portals, visualization for review of chart records, and tools for aiding the discovery of animal models of human diseases.
Hongfei Hou
Hongfei Hou, a senior scientist at Pacific Northwest National Laboratory, has attained a Ph.D. in Computer Science from Washington State University. His research area includes cloud computing and machine learning.
Jacob J Hughey
I’m an Assistant Professor of Biomedical Informatics and Biological Sciences at Vanderbilt University. My group's research is centered around developing and applying computational methods to large, noisy datasets in order to quantify, model, and understand dynamic biological systems. We are particularly interested in the mammalian circadian system.
Eui-Nam Huh
Dr. Eui-Nam Huh is a Professor within the Department of Computer Science and Engineering at Kyung Hee University, South Korea.
His expertise is focused on cloud computing and machine learning.
Henkjan J. Huisman
Dr. H.J. Huisman received his Ph.D. in quantitative medical ultrasound in 1998 at the Radboud University Medical Center, Nijmegen, The Netherlands. He continued his research in quantitative MR and ultrasound in breast and prostate resulting in several publications, clinical applications and a patent on a Pharmacokinetic DCEMR processing. He started a research group in 2004 on Computer Aided Diagnosis and Intervention of prostate cancer focussing on computerized support systems for interpretation of multiparametric MR and MRL as well as image guided biopsy and intervention. Since June 2017 he is an Associate Professor in Pelvic Imaging Biomarkers. He has published over 100 papers and book chapters and has co-organized several workshops/challenges on prostate MR image analysis.
Eyke Hüllermeier
Eyke Hüllermeier is a full professor in the Department of Computer Science at the University of Paderborn, Germany, where he heads the Intelligent Systems group. He studied mathematics and business computing, received his PhD in computer science from the University of Paderborn in 1997, and a Habilitation degree in 2002. Prior to returning to Paderborn in 2014, he held professorships at the Universities of Dortmund, Magdeburg and Marburg.
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.
Martina Iammarino
Martina Iammarino is a Tenured Assistant Professor at the Department of Computer Science and Technologies at Pegaso University in Naples.
She holds a Laurea degree in Computer Engineering in 2019 and a PhD degree in Information Technology for Engineering from the University of Sannio in 2023.
Her research focuses on software engineering, data quality, and process engineering, with a growing emphasis on artificial intelligence. Specifically, her work in AI has been pivotal in addressing challenges in the medical field, with a special interest in Parkinson's disease. Through the application of machine learning and deep learning techniques, her research has advanced understanding and innovation in diagnosing, monitoring, and managing this neurodegenerative disorder.
She has published extensively on AI methodologies applied in various domains and has contributed to the AI and healthcare research community as a reviewer for several international conferences and journals.
In addition to serving on the program committee of several international conferences, Martina Iammarino is an Editorial Board Member for the journal Peerj, and is also one of the main organizers of the CISE Workshop "Computational Intelligence and Software Engineering" held at PROFES 2023.
Biju Issac
Dr Biju Issac is a Computer Science academic staff working at Northumbria University, UK. He has done PhD in Networking and Mobile Communications, MCA (Master of Computer Applications) and BE (Electronics and Communications Engineering). He is a Chartered Engineer (CEng), Senior IEEE member and Fellow of HEA. His research interests are in Wireless Networks, Cybersecurity, AI/Machine Learning applications (security, image processing, text mining etc) and Bio-inspired metaheuristic algorithms. His personal research website: https://www.bijuissac.com/
Rodolfo Jaffé
I`m interested in inter-disciplinary approaches, comprising population and community ecology, genomics and spatial statistics, to understand how the alteration of natural habitats influences biodiversity and the provision of ecosystem services.
Yaochu Jin
Professor of Computational Intelligence, University of Surrey, UK, Finland Distinguished Professor, Jyvaskyla, Finland, Changjiang Distinguished Professor, Northeastern University, China. Vice President for Technical Activities, IEEE Computational Intelligence Magazine, IEEE Distinguished Lecturer.
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.