معرفی
Minsu Park is an Assistant Professor of Social Research and Public Policy at New York University Abu Dhabi (NYUAD), with affiliate status at the Center for Data Science at New York University (NYU). He holds a PhD in Information Science from Cornell University, where he was advised by Michael W. Macy and Mor Naaman. His academic work bridges computational techniques and social science theory, focusing on cultural consumption, social networks, and human-centered data science.
- PhD in Information Science, Cornell University
His research centers on understanding how individuals form cultural preferences through social and psychological mechanisms, particularly in domains like music, food, fashion, and science. He leverages large-scale digital trace data—including social media, streaming platforms, and physiological signals from smart devices—to model behavior. His interdisciplinary approach draws from sociology, social computing, and data science, aiming to uncover both individual and global patterns in cultural dynamics. Key themes include variety-seeking behaviors, affective preferences, and the interplay between social position and taste.
The 15 most recent publications reflect a strong trend toward using computational methods to explore sociocultural phenomena. Topics span affective preference rhythms in music, testing sociological theories like cultural omnivory, imputing user attributes from digital behavior, and enhancing model interpretability. These works are published in high-impact venues such as Nature Human Behaviour and ICWSM, indicating recognition in both computer and social sciences.
Minsu Park is deeply committed to responsible data science, addressing issues like algorithmic bias, transparency, fairness, data privacy, and research ethics. He emphasizes mixed-methods approaches and critical reflection on data generation and usage.
- Published in Nature Human Behaviour
- Presented at ICWSM
- Focus on ethical and interpretable AI
- Advocate for reproducibility and data curation
He mentors students through capstone projects in computer science and teaches courses such as Human-Centered Data Science and Textual Analysis for the Social Sciences. While specific grants are not detailed in the text, his research program suggests involvement in interdisciplinary, data-intensive projects likely supported by academic or scientific funding bodies. His technical expertise includes Python, R, machine learning frameworks (TensorFlow, Keras), and tools like Gephi and AWS.
He leads or contributes to research initiatives involving smartwatch-based data collection, surname-based ethnicity matching, and global music consumption analysis, as evidenced by his public GitHub repositories. These projects highlight his commitment to open science and collaborative research.
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