Prof. Daniel Huson (H-index: 86)

University of Tübingen

Daniel Huson is Professor of Algorithms in Bioinformatics at the University of Tübingen, Germany. He studied mathematics and physics at Bielefeld University, where he earned his PhD and Habilitation, and before joining Tübingen in 2002 he held research positions at Princeton University and at Celera Genomics in the United States. He has also held visiting professorships at the National University of Singapore and Nanyang Technological University.

His research designs algorithms and software for phylogenetics, genomics, and microbiome analysis. He is the author of several widely used programs, including SplitsTree for phylogenetic trees and networks, and MEGAN and MALT for the taxonomic and functional analysis of microbiome and ancient-DNA sequencing data; he also co-developed the fast sequence aligner DIAMOND. These tools are applied across fields ranging from environmental microbiology to the study of Neanderthal diet and disease from ancient dental calculus.

Huson's work has been cited more than 70,000 times. His distinctions include the PLOS Computational Biology Research Prize 2016 and first place in the 2013 DTRA Algorithms Challenge. His current research centres on phylogenetic networks and the continued development of the SplitsTree, MEGAN, and PhyloSketch software.

INVITED SPEAKERS

Assoc. Prof. Hussam Alsharif

Umm Al-Qura University

Dr. Hussam Alsharif received the B.Sc. degree from Pittsburg State University, USA, in 2010, the M.Sc. degree from Western Illinois University, USA, in 2012, and the Ph.D. degree from North Carolina Agricultural and Technical State University, USA, in 2020. He served as an Assistant Professor in the Department of Computer Science at the University College of Al Jamoum, Umm Al-Qura University, Makkah, Saudi Arabia, from 2020 to 2025, and has been an Associate Professor since 2026. His research interests include artificial intelligence, computer vision, medical image analysis, machine learning, deep learning, data science, computational biology, bioinformatics, big data analytics, and intelligent healthcare systems. He has authored numerous peer-reviewed publications in these fields and serves as a reviewer for several international journals.

Speech Title:"AI-Powered Prognostic Assessment of Impacted Maxillary Canines Using Hybrid Deep Learning"

Abstract: Artificial Intelligence (AI) is improving efficiency and consistency in dental and medical imaging, thereby advancing clinical decision-making. In orthodontics, predicting the prognosis of impacted maxillary canines remains challenging, as treatment outcomes rely heavily on clinicians’ interpretation of panoramic radiographs, often resulting in inter-observer variability. In this talk, we present a hybrid deep learning architecture that combines the strengths of the Swin Transformer and ResNet18 through an attention-based feature fusion strategy for prognostic assessment of impacted maxillary canines from panoramic radiographs. The proposed system classifies cases into three clinically meaningful prognostic categories: good, average, and poor. We will also discuss the role of hybrid deep learning systems in dental imaging, the challenges of AI implementation in routine clinical practice, and future directions for building explainable and clinically trustworthy AI systems. The proposed framework has the potential to support clinical decision-making by improving diagnostic consistency, reducing inter-observer variability, and assisting in more effective treatment planning.

PREVIOUS SPEAKERS

Prof. Yudong Zhang University of Leicester, UK	Prof. Dong Sun City University of Hong Kong, Hong Kong	Prof. Nicola Mulder University of Cape Town, South Africa	Prof. Stephen Kwok-Wing Tsui The Chinese University of Hong Kong, Hong Kong
Prof. Dong Ming Tianjin University, China	Prof. Tetsuo Shibuya The University of Tokyo, Japan	Assoc. Prof. Lin Meng Tianjin University, China	Assoc. Prof. Jie Luo Shanghai Jiao Tong University, China
Prof. Mitsuhiro Ogawa Teikyo University, Japan	Prof. Hao Jiang Renmin University of China, China	Assoc. Prof. Xingwei An Tianjin University, China	Assist. Prof. Faez Iqbal Khan Xi'an Jiaotong-Liverpool University, China
Prof. Chenjie Xu (H-index: 60) City University of Hong Kong, China	Prof. Yasukazu Nakamura (H-index: 65) National Institute of Genetics, Japan	Prof. Keiji Nakajima Nara Institute of Science and Technology, Japan	Assoc. Prof. Marwan El Rich Khalifa University, UAE