2026 8th International Conference on Advanced Bioinformatics and Biomedical Engineering (ICABB 2026) aims to gather professors, researchers, scholars and industrial pioneers all over the world. ICABB is the premier forum for the presentation and exchange of past experiences and new advances and research results in the field of theoretical and industrial experience. The conference welcomes contributions which promote the exchange of ideas and rational discourse between educators and researchers all over the world. We aim to building an idea-trading platform for the purpose of encouraging researcher participating in this event. ICABB 2026 welcome qualified persons to delivery a speech in the related fields. If you are interested, please send a brief CV with photo to the conference email box: icabb@cbees.net.
Keynote Speakers of ICABB 2026
Associ. Prof. Martin Steinegger
Seoul National University, South Korea
Dr. Steinegger is an Associate Professor in the Biology Department at Seoul National University, with a joint appointment to the Interdisciplinary Program in Bioinformatics. He conducted his doctoral studies at the Max Planck Institute for Biophysical Chemistry and was awarded a Ph.D. in computer science with summa cum laude honors from the Technical University of Munich in 2018, followed by a postdoctoral fellowship at Johns Hopkins University. Dr. Steinegger has published more than 50 papers covering a wide range of topics in bioinformatics, from detecting genomic assembly contamination to organizing the protein structure space. In 2024 he was awarded the Overton Prize for outstanding contributions to computational biology by the International Society for Computational Biology. He started his research group in 2020, focusing on the development of methods to analyze massive genomics and proteomic datasets. The group’s contributions to bioinformatics include widely used tools for predicting structures (ColabFold/AlphaFold2), clustering (Linclust), assembling (Plass), and searching sequences (MMseqs2) and protein structures (Foldseek). His group’s software and web services have been installed and used millions of times. Dr. Steinegger is an advocate for international collaboration at his home institution, as well as for open science and open-source software.
Prof. Michiaki Hamada
Waseda University, Japan
Michiaki Hamada is a Professor in the Faculty of Science and Engineering at Waseda University, Tokyo, and President of the Japanese Society for Bioinformatics (JSBi). He is concurrently a Fellow of the Center for Research and Development Strategy at the Japan Science and Technology Agency (JST-CRDS), an Invited Researcher at the Cellular and Molecular Biotechnology Research Institute, AIST, and a Visiting Professor at the Graduate School of Medicine, Nippon Medical School. He received B.Sc. and M.Sc. degrees in Mathematics from Tohoku University and a Ph.D. from Tokyo Institute of Technology in 2009, with a thesis on RNA secondary structure prediction. After eight years as a researcher in industry, he moved to the University of Tokyo in 2010 and to Waseda University in 2014, becoming a full Professor in 2018. His research spans computational biology, RNA informatics, and AI-assisted drug discovery, with a focus on RNA structure prediction, RNA-protein interaction analysis, and integrative multi-omics approaches. He has authored over 100 peer-reviewed publications and received the MEXT Young Scientists' Award (2017), the Waseda Research Award (2021), and the Okuma Memorial Academic Prize (Encouragement Award, 2024). He also serves as a Program Advisor for the FOREST program of JST and on the Program Evaluation Committee of AMED.
Speech Title: "Information Technologies Accelerating RNA Therapeutics"
Abstract: RNA-based therapeutics are emerging as a transformative class of medicines, yet their development faces long timelines, high costs, and low success rates. This keynote will introduce two complementary RNA-focused strategies that integrate artificial intelligence and computational biology to address these challenges. The first is RNA aptamer drug discovery, offering an alternative to small molecules. Our AI platform, RaptGen, combines HT-SELEX data, probabilistic modeling, and deep learning to design optimized aptamers, enabling in silico design, activity-guided mutagenesis, and rational truncation. Applications include generating high-affinity aptamers against Dengue virus and SARS-CoV-2. The second is RNA-targeted drug discovery, which treats RNAs themselves as therapeutic targets. We are developing a comprehensive database that integrates structural, interaction, and functional annotations for thousands of disease-associated non-coding RNAs, supporting the identification of druggable RNA motifs. Future directions include applying quantum computing-AI hybrid approaches to explore vast molecular sequence spaces and accelerate drug candidate optimization, bridging fundamental RNA biology and clinical innovation.
Invited Speakers
Assoc. Prof. Ai Ye
Singapore University of Technology and Design, Singapore
Dr. Ye Ai is currently an Associate Professor with tenure and Director of Healthcare Education at Singapore University of Technology and Design (SUTD). He has been selected as a 2019 Lab on a Chip Emerging Investigator by the journal Lab on a Chip and listed among the top 2% scientists in a global list by Stanford University from 2021 to 2024. Associate Professor Ye Ai’s research interest focuses on developing innovative microfluidic technologies and biomedical devices for solving challenging biological and medical problems. His research group has developed precise acoustic tweezing technology for single-cell manipulation, biophysical cytometry for multidimensional label-free single-cell analysis, high-efficiency intracellular delivery for cell engineering, and microfluidic molecular diagnostic devices. He has published one book and 120 peer-reviewed scientific articles in leading international journals with a total citation of 8547 and an h-index of 55. Multiple technologies and IPs from Dr. Ai’s research have been licensed for commercialization.
Speech Title: "Advanced Microfluidics for Precision Medicine"
Abstract: Biological cells in human bodies, as the fundamental unit of life, are heterogeneous in nature. It is essential to identify and sort specific cell populations from highly heterogeneous biological samples for a variety of applications in biology, diagnostics, and medicine. Microfluidic technologies capable of accurate cell analysis and precise cell manipulation can provide unprecedented capabilities to automate conventional biological assays. In this talk, I will present our acoustic microfluidic platforms for precise microscale manipulation with high-frequency ultrasonic waves. We have further integrated single cell fluorescence detection with our acoustic microfluidic platforms to implement flow-based high-throughput fluorescence activated sorting of single cells. We have demonstrated the integrated acoustic microfluidic platform for different biomedical applications, for example sorting of live cells from cryopreserved cell samples in single cell sequencing, sorting of iPSC-derived cardiomyocytes for personalized drug testing and sorting of transfected cells for gene editing and cell therapy. This new type of acoustic manipulation platform has enabled high-precision and on-demand single cell sorting in diverse precision medicine applications.
Prof. Yusaku Fujii
School of Science and Technology, Gunma University, Japan
Yusaku Fujii is a Professor in the School of Science and Technology, Gunma University, Japan. He received his B.E. (1989), M.E. (1991), and Ph.D. (2001) degrees from the University of Tokyo. He worked at the National Research Laboratory of Metrology (NRLM) from 1995 to 2002, conducting research in measurement science, including superconducting magnetic levitation. He joined Gunma University in 2002. His current research spans measurement science, artificial intelligence governance, bioengineering, public health engineering, and social technology. He has proposed the Extended Kelvin Principle, linking trust infrastructure, trust, measurement, and understanding, as well as approaches to verifiable AI output governance. In biomedical and public-health engineering, he has proposed "PAPR for Everyone," extending positive-pressure respiratory protection to the general public. His current work explores "PAPR for Everyone" as a preparedness strategy for future pandemics and the integration of AI, sensing, and verifiable trust infrastructure for future biomedical and public-health applications.
Speech Title: "Governing Biomedical AI Without Blinding It: Verifiable Output Governance for Privacy-Preserving Bioinformatics"
Abstract: Biomedical AI increasingly relies on rich and sensitive data, including medical records, genomic information, physiological measurements, and continuously collected health data. Conventional privacy protection often restricts, minimizes, or anonymizes data before analysis. While essential in many contexts, excessive restriction may reduce AI’s ability to discover clinically or scientifically important relationships that were not anticipated in advance. This talk explores a complementary approach: governing AI at the output stage. Verifiable Records of AI Output (VRAIO), combined with the Governance-Legitimacy of Output (GLO) framework, aims to preserve analytical capability while making consequential AI disclosures and actions verifiable, governable, and auditable. The underlying principle is simple: an AI system’s ability to know or infer something does not imply permission to disclose or act upon it. I will also introduce "PAPR for Everyone," my complementary engineering approach to pandemic preparedness. Public-oriented powered air-purifying respirators (PAPRs) can provide positive-pressure personal exposure control, allowing individuals to directly manage the air they inhale. By making protection measurable and controllable, this approach seeks to establish personal exposure control as an additional preparedness layer alongside vaccination, ventilation, surveillance, and other established public-health measures.
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