Date: Saturday 3rd June 2025, 13:30 to 17:30.

Title: Workshop on Machine Learning (ML) Algorithms and Their Applications

Instructor: Anwesha Bhattacharya and Agus Sudjianto, Wells Fargo

Description: In this four hour workshop, you will learn about commonly used ML algorithms and how they are used in practice. The algorithms covered will include Feedforward Neural Networks, Gradient Boosting, and Random Forest. PI_ML, an open-source toolbox with easy-to-use interface, will give participants hands-on experience in training and assessing performance of the algorithms on real datasets. Participants will also learn about how to interpret the results using post-hoc explainability techniques and assessing model robustness and model weaknesses. Applications of these algorithms in banking will also be described.

Date: To be decided

Title: Multimodal Causal Inference for Data Science and Biomedical Research

Instructor: Himel Mallick, Cornell University

Description: This short course introduces multimodal causal inference, a cutting-edge approach that integrates diverse data sources such as clinical, genomic, imaging, and electronic health records to improve causal discovery and effect estimation. Participants will learn state-of-the-art methodologies to address challenges like high-dimensionality, heterogeneity, missingness, and modality alignment. The course will also cover uncertainty quantification techniques, hands-on computational tools, and real-world applications in precision medicine and policy evaluation.

Category: Methodology

Target Audience:

• Statisticians, data scientists, and biomedical researchers with a foundational understanding of statistical inference principles and familiarity with machine learning or statistical modeling.

Prerequisites:

• Basic knowledge of statistical inference principles


• Familiarity with machine learning or statistical modeling


• Experience with Python or R (recommended but not required)

Computer and Software Requirements:

• Participants should bring a laptop with R/Python installed. Supplementary materials, including lecture slides, reading resources, and coding notebooks, will be provided.

Date: To be decided

Title: Boosting R Code performance via C++ Integration within Rstudio

Instructor: Priyam Das, Virginia Commonwealth University

Abstract:

Despite being the most popular coding platform among statisticians, R suffers from several drawbacks that hinder its full potential, particularly for computationally demanding algorithms. These drawbacks stem from R's poor performance in essential BLAS operations (e.g., matrix inversion, SVD) and its inefficiency in handling nested for-loops, making it significantly slower compared to Python, MATLAB, Julia, and a few other coding platforms. To address this gap, the computationally demanding operations of a function can be coded in C++ within the RStudio platform and subsequently called from the original master R file. This approach significantly reduces computation time, enhancing speed by up to 10–50 folds.

Pre-requisites:

R beginner, familiarity with R objects, R functions. No prior knowledge of C++ (CPP) is required. Participants are encouraged to bring their own laptop (optional) with RStudio installed, along with an established internet connection (while attending the session; optional).