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Applied Statistics and Data Science Seminar on Monday February 24, 2025
February 24 @ 2:00 pm - 3:00 pm
Venue, time and date: ISRT, 2:00 pm, February 24, 2025
Speaker: Humayera Islam, PhD, Postdoctoral Scholar in Precision Health at the University of Chicago
Talk 1
Title: From Statistical Models to LLMs: The Evolution of Feature Representation in Predictive Modeling
Abstract:
With the digitization of healthcare and public health systems, data collection has expanded far beyond traditional numerical and categorical formats to include complex modalities such as natural language (e.g., clinical notes), medical images (e.g., radiology scans), genetic data (e.g., omics), and temporally extensive time-series data (e.g., electronic health records). This expansion was driven by advancements in data storage capacity, enabling the collection of massive, high-dimensional datasets. As the size and complexity of data grew, so did the need for more sophisticated feature representation techniques to effectively capture the underlying patterns for predictive tasks to enhance clinical decision making. This seminar traces the evolution of feature representation from traditional statistical models, which relied on manual feature engineering, to machine learning models that automated feature extraction, to deep learning architectures that learned hierarchical and temporal features, and finally to Large Language Models (LLMs) that leveraged self-attention mechanisms for contextual sequence modeling. The aim is to spark curiosity and inspire students to explore how to effectively handle these diverse data modalities and harness the power of advanced models for innovative research projects.
Talk 2
Title: Pathways to Growth: Preparing for Data Science and Informatics Graduate Programs in the US
Abstract:
This talk offers a comprehensive roadmap for students aspiring to pursue graduate programs in data science and informatics in the US. It will cover the key skill sets essential for enhancing data science expertise, including domain knowledge, emerging methodologies, technical proficiency, and leadership in professional development. Additionally, students will be provided with valuable resources such as open-source datasets and learning platforms to strengthen these skills during their time at ISRT and effectively prepare for graduate studies.
