Library Building (LIB)

This session will address the visualization of standard deviation (s.d.), standard error of the mean (s.e.m.), and confidence interval (CI) error bars to build understanding of uncertainty in data analysis.

In this workshop, participants are invited to explore how to practically and creatively incorporate disability justice principles in participatory and community-engaged research.

Participants will gain practical experience with data collection, processing, and AI-assisted defect assessment, and learn how sensory measurements can be applied to support engineering decision-making.

This session will introduce participants to the concept of P values and their role in hypothesis testing, highlighting that P values reflect the probability of observing the data under the null hypothesis, and not necessarily the biological significance of the findings.

This workshop introduces fuzzy clustering methods, with emphasis on the Fuzzy C-Means algorithm. We discuss how partial membership values are assigned, how the objective function governs cluster formation, and when fuzzy approaches are preferable to hard partitions.

In this workshop, participants are invited to explore how to practically and creatively incorporate disability justice principles in participatory and community-engaged research.

This workshop introduces distribution-based clustering techniques, with emphasis on Gaussian mixture models. We discuss likelihood-based estimation, model selection, and cluster uncertainty.

This session will address the advantages of box plots over bar charts for displaying the spread and variability in data.

This session covers density-based clustering approaches such as DBSCAN and related methods. We explore core concepts including density thresholds, reachability, and handling irregular cluster shapes and noise.

Do you want to learn how to use copyrighted material in your research, and how to protect your own rights as an author? If so, this workshop is for you!

This session will introduce participants to various types of t-tests, including one-sample, two-sample, paired, and one-sided tests.

This workshop introduces graph-based clustering, including community-detection approaches such as spectral clustering and modularity-based methods. We examine how similarity networks are constructed and how graph topology guides cluster formation.

This session will introduce participants to non-parametric tests, which are useful when data distributions do not meet the assumptions of parametric tests.

This session focuses on preparing mixed-type datasets—categorical, ordinal, and continuous variables—for clustering. We cover distance measures, data transformations, and quantification techniques that preserve variable meaning.