Talwar Puneet (CRC): "From Data to Insight – Fast and Reproducible Analysis with R Shiny"

Monday 1 Jun 2026, 14:00 → 15:00 Europe/Brussels

B-30/0-000 - Big meeting room (CRC)

Abstract: Translating complex data into actionable insight requires tools that are not only statistically rigorous, but also fast, transparent, and reproducible. In this talk, From Data to Insight – Fast and Reproducible Analysis with R Shiny, I demonstrate how interactive applications built in R Shiny can bridge the gap between advanced statistical modeling and practical decision-making. Using Shiny applications, I will showcase workflows for generalized linear mixed models (GLMMs), featuring the ability to replicate SAS results, ensuring a seamless transition to open-source workflows. I will also present an interactive implementation of generalized additive models for location, scale, and shape (GAMLSS), enabling flexible distributional modelling and intuitive diagnostics. Beyond modelling, dedicated applications for power and sensitivity analysis, along with project dashboard, demonstrate how Shiny can streamline research workflows. The focus of this session is not only on statistical methodology, but on reproducibility, scalability, and cross-functional usability. By combining robust statistical frameworks with interactive visualization and automated reporting, Shiny applications can accelerate analysis cycles, reduce manual error, and enhance transparency.

Biosketch: Puneet Talwar is a translational research scientist working at the intersection of neurobiology, genomics, and advanced data analytics. Trained as a Biotechnology Engineer, he obtained his PhD in 2016 from the CSIR–Institute of Genomics and Integrative Biology (IGIB), in collaboration with the Department of Neurology at the Institute of Human Behaviour & Allied Sciences (IHBAS), New Delhi, India. His doctoral research focused on identifying clinical, genetic, and proteomic biomarkers for Alzheimer’s disease using integrated clinical phenotyping, computational, and experimental approaches, resulting in 14 international publications. His work highlighted the value of multimodal data in biomarker discovery. He also led pharmacogenomics research in Epilepsy as part of the Indian Genome Variation (IGV) Consortium. During his postdoctoral work at IHBAS, he expanded into neuroimaging-genetic and cognitive neurology research in Alzheimer’s disease. Currently, at GIGA-CRC Human Imaging (University of Liège), he leads multimodal studies integrating neuroimaging, sleep physiology, and genetic risk markers to better understand mechanisms underlying neurodegenerative disease progression within a multinational consortium. His expertise spans translational neuroscience, biomarker discovery, and reproducible statistical modeling using R-based pipelines. A strong advocate of open-source science, he develops interactive Shiny applications to streamline complex analyses.

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