Prof. Lau Sian Lun received both his MSc and Dr.-Ing. at the University of Kassel, Germany, where he worked as a full-time researcher from 2004. He has contributed and managed various German national as well as EU-funded projects, such as the EU FP6 MobiLife, ITEA S4ALL, BMBF MATRIX and the EU FP7 SEAM4US schemes. He has a distinguished publishing record, having authored over 50 publications for conferences, workshops, book chapters and journals such as World Applied Sciences Journal, IEEE Vehicular Technology Magazine, Jurnal Teknologi and more. He is currently a senior member of the Institute of Electrical and Electronics Engineers (IEEE). His research interests focus on areas of ubiquitous computing, sustainable smart cities, context-awareness and applied machine learning.

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Dr. Wan Fairos Wan Yaacob is an Associate Professor at Universiti Teknologi MARA, Kelantan, and Head of the Business Datalytics Research Group. She holds a PhD in Statistics and specializes in statistical modeling and data mining, with a focus on panel count models for road accidents and Monte Carlo simulations. Her research interests include traffic accident modeling and dengue disease modeling, using hierarchical Bayesian methods for spatial-temporal analysis. Dr. Wan Fairos has published over 30 papers in renowned journals and conferences, with her work cited in over 400 papers (H-Index 12). She is an associate fellow of the Institute of Big Data Analytics and Artificial Intelligence and a fellow of the Malaysian Institute of Transport. She has led multiple government-funded research projects and is a certified Rapid Miner Data Analyst. Dr. Wan Fairos is also a frequent invited speaker at workshops and conferences on panel count models and data mining.

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"Crafting Publishable Graphs with R and Bioconductor" is a 2-hour pre-conference workshop. Participants will learn to create publication-quality graphs for biomedical research using R and Bioconductor. The session covers various chart types including scatter plots, heatmaps, and volcano plots, helping researchers effectively visualize their data.

In this talk, Automated Machine Learning (AutoML) with R: Streamlining the Model Building Process, AutoML will be introduced, highlighting its importance in automating tasks such as feature selection and hyperparameter tuning to streamline model development. Using the H2O package in R, a demonstration will showcase how AutoML enables efficient and scalable machine learning by running multiple algorithms and selecting the best model, providing attendees with practical knowledge to apply AutoML in their own projects.

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The COVID-19 pandemic led to a global surge in scientific research, and Malaysia was no exception. This talk presents a bibliometric analysis of Malaysia’s COVID-19-related research, using data from the Web of Science database. Bibliometric analysis, which quantitatively evaluates publications through metrics like citation counts, publication volume, and collaboration networks, provides insights into key research trends, influential contributors, and collaborative efforts. By employing R and Python for the analysis, this case study demonstrates how bibliometric techniques can uncover valuable patterns and trends within Malaysia’s pandemic research landscape.

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Ts. Dr. Mohamad Arif Awang Nawi is a Lecturer of Biostatistics at the School of Dental Sciences, Universiti Sains Malaysia (USM) and serves as the biostatistics coordinator at the USM Health Campus. He holds a BSc in Mathematics & Computing and a Master’s in Mathematical Sciences from Universiti Malaysia Terengganu (UMT), along with a PhD in Statistics from Universiti Sultan Zainal Abidin (UniSZA).  “Applying R to Health Data Prediction: Overcoming Small Data Limitations" discussed strategies to improve health data predictions using R, despite small datasets. It covered techniques like data augmentation, bootstrapping, and synthetic data generation to enhance predictive accuracy and model performance. Practical case studies demonstrated these methods' effectiveness, showcasing R's capability to tackle small data challenges and improve health research outcomes.

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Ts. Dr. Liew Kok Jun, known as James, is a Senior Bioinformatician at Codon Genomics Sdn. Bhd. He holds a Bachelor of Science (Honours) in Biotechnology from Universiti Tunku Abdul Rahman, as well as a Master of Science (MSc) and Doctor of Philosophy (Ph.D.) from Universiti Teknologi Malaysia. His expertise spans microbiology, molecular biology, protein science, next-generation sequencing, and bioinformatics. Over the course of his research career, Dr. Liew has excelled in analyzing prokaryotic and eukaryotic genomes and transcriptomes, with a special focus on amplicon and shotgun metagenomics studies.

With an h-index of 7, Dr. Liew has made significant contributions to understanding microbial diversity in various environmental samples such as hot springs, biofilms, seawater, and peat soil. His notable work includes pioneering the recovery of metagenome-assembled genomes (MAGs) from Malaysian hot spring biofilms, emphasizing his expertise in omics technologies. His publications reflect his dedication to advancing the field, with significant focus on transcriptomics. Notably, his research delves into the transcriptomic responses of pathogens like Candida albicans, Pseudomonas aeruginosa, and Staphylococcus aureus to plasma medicine.

Currently, Dr. Liew is integrating R programming into his transcriptomics research, applying advanced R techniques for data analysis and visualization. His work with R has been instrumental in enhancing the depth of his transcriptome studies, allowing for more robust and efficient data interpretation. He is also exploring the application of machine learning and AI-driven bioinformatics to study environmental microbiomes, agriculture, and horticulture, with the aim of driving future innovations. His ongoing efforts promise new insights with significant scientific and practical implications.

In this talk, Ts. Dr. Liew Kok Jun (James), Senior Bioinformatician at Codon Genomics Sdn. Bhd., will showcase the practical applications of R in genomics, with a focus on transcriptome analysis. Using real-world examples, he will demonstrate how R is applied in commercial settings to process and analyze RNA-Seq data. Attendees will learn how R enhances cost-efficiency and scalability in genomic projects. The session will conclude with a live demo on creating insightful visualizations from transcriptome data using ggplot2, offering a hands-on glimpse into R’s powerful capabilities for genomics research and commercial use.

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This talk will explore the diverse applications of R in credit risk modeling, showcasing its capabilities in tackling various challenges throughout the modeling lifecycle. We'll delve into real-world examples and best practices, highlighting R's strengths in data manipulation, model development, validation, and visualization.

The spatio-temporal model using the Bayesian method has been widely used in epidemiology, disease mapping and environmental studies. Using the CARBayesST package in R, this talk will demonstrate how the spatial and temporal Bayesian Poisson Generalised Linear Mixed Model offers a better model for predicting dengue incidence as compared to the conventional model. Furthermore, the power of the tmap package in R will be showcased, highlighting its ability to create detailed and informative spatial maps, thereby enriching the visual representation and analysis of spatial data.

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