Piero Palacios
Data Scientist & Statistical Specialist
Lima, Peru
Actions
Piero Palacios
Biologist | Data Scientist | Bioinformatic Scientist
Hello! I’m Piero, a biologist from Peru with a strong foundation in biotechnology. My passion for data analysis and bioinformatics has led me to pursue specializations from Harvard University and MIT. In my free time, I enjoy creating reproducible and interactive reports using Quarto.
My bioinformatics expertise spans various domains, including RNA-Seq, Methyl-Seq, WGBS-Seq, Illumina Methylation Array, and multi-omics analyses. Additionally, I have extensive experience in machine learning (e.g., OLS, ridge and lasso regression, SVM, kernels, collaborative filtering, network analysis, high-dimensional reduction) and deep learning models (e.g., feed-forward neural networks, recurrent neural networks, convolutional neural networks, graph neural networks, transformers).
I'm excited to connect, share ideas, and explore innovative applications of Shiny at this conference!
Area of Expertise
Topics
Speaking to the Data: Democratizing AI-Driven Exploration with Shiny for Python and CIP Dataverse
In research, the ability to “speak” directly to the data not only simplifies analysis but also democratizes the use of AI—allowing anyone, regardless of coding expertise, to uncover meaningful insights. This talk will showcase a cutting-edge Shiny for Python application that seamlessly integrates interactive data exploration with AI assistance. Developed using LangChain, pandas, scikit-learn, and statsmodels, the application empowers researchers to upload datasets via DOI links from the International Potato Center (CIP) Dataverse. Once uploaded, users can select specific tables, engage with a conversational DataFrame agent, and instantly generate visualizations and statistical analyses—without writing code.
Beyond interactive exploration, the application features a streamlined workflow for reporting. Users can compile custom Quarto documents that include AI-generated plots, user-created word clouds, conversation transcripts, and table outputs. By blending an intuitive interface with AI-driven data analysis, we’re illustrating how Shiny for Python can serve as a powerful tool for life sciences and beyond. Attendees will learn practical strategies for building similar AI-assisted Shiny apps—from handling complex datasets to ensuring reproducible reports—offering a glimpse into the future of accessible, data-centric research.
Please note that Sessionize is not responsible for the accuracy or validity of the data provided by speakers. If you suspect this profile to be fake or spam, please let us know.
Jump to top