Labs
Computational Social Science Lab
The Computational Social Science Lab at Purdue University focuses on analyzing audiovisual data from sources like television broadcasts and public space camera networks. Led by Dr. Dietrich, the lab aims to be at the forefront of this rapidly developing field, exploring complex social interactions and non-linear relationships across non-traditional data sources.
Governance and Responsible AI Lab (GRAIL)
GRAIL examines the social and ethical implications of AI algorithms in various domains, including criminal justice, education, and healthcare. The lab explores how policymakers understand AI and its impacts, and how government, industry, and civil society are governing AI. GRAIL embraces interdisciplinary research involving both quantitative and qualitative methodologies.
Virtual Reality and Artificial Intelligence (VRAI) Lab
Headed by Dr. Javier Gomez-Lavin, the VRAI Lab utilizes high-end computers to train and analyze machine learning algorithms, as well as virtual reality software to study perception and cognition in virtual spaces. The lab focuses on developing and analyzing language models, exploring new theories to assess AI cognitive abilities, and providing hands-on experience for students in machine learning techniques.
Laboratory for Computational Anthropology (LCA)
Professor Otárola-Castillo’s Laboratory for Computational Anthropology (LCA) at Purdue University is dedicated to advancing the understanding of human evolution, behavior, and ecology through an integrated approach that bridges theory, methods, and empirical science. Central to the lab's mission is developing and applying computational methods, including advanced statistical modeling, artificial intelligence, and machine learning. These are essential tools in driving innovation and uncovering new insights. The lab's work is centered on three interconnected pillars:
Theory Development: The lab constructs and refines quantitative theoretical models in human evolutionary biology, ecology, behavior, and theoretical morphology, providing new conceptual frameworks for understanding human adaptation and variation.
Methods Innovation: The lab develops and applies cutting-edge computational statistics, AI, and machine learning tools to analyze complex anthropological, archaeological, and biological patterns and test hypotheses, enabling novel ways to study past and present human populations.
Empirical Testing: The lab rigorously tests models and hypotheses using archaeological, ecological, ethnographic, and evolutionary biological data, combining fieldwork, laboratory experiments, and large-scale quantitative analyses to validate and refine theoretical predictions.