Training

Computational Social Science Training Program (CSSTP)

The UC Berkeley Computational Social Science Training Program (CSSTP) trains predoctoral students representing a variety of degree programs and expertise areas in the social sciences, including demography, public health, public policy, social epidemiology, social welfare, and sociology.

Launched in 2020 with a five-year, $1.2 million grant from the National Institutes of Health (NIH) Office of Behavioral and Social...

Computational Research for Equity in the Legal System Training Program (CRELS)

The UC Berkeley Computational Research for Equity in the Legal System Training Program (CRELS) trains doctoral students representing a variety of degree programs and expertise areas in the social sciences, computer science and statistics.

Launched in 2023 with a $3-million grant from the National Science Foundation (NSF), this five-year multidisciplinary training program in data science and social science disciplines fosters a new computational social science research community and leads the integration of research on the social implications of AI. CRELS is...

GraphXD – Graph Analysis Across Domains

BIDS GraphXD – Graph Analysis Across Domains is a cross-domain initiative that promotes interdisciplinary collaboration and training for researchers, scientists, and theorists interested in using graphs and network analysis for applications in a variety of fields across STEAM including (but not limited to) anthropology, art, biology, computer science, economics, history, linguistics, mathematics, physics, and sociology.

Graphs and networks are models of data – usually composed of nodes and vertices connected by edges – that can be used to analyze and to visualize...

ImageXD – Image Analysis Across Domains

BIDS ImageXD – Image Analysis Across Domains convenes researchers, scientists, and theorists (of all learning levels) who work with images as a primary source of data, to learn about the latest developments in a wide range of research domains and to promote interdisciplinary collaboration.

Incredible advances are being made in image processing techniques, tools, and sampling modalities which, together with an increased accessibility to modern imaging equipment, has made image data ubiquitous across many fields, with scales ranging from microscopy to radio astronomy...

TextXD – Text Analysis Across Domains

BIDS TextXD – Text Analysis Across Domains brings together researchers from across a wide range of disciplines, who work with text as a primary source of data, whether they identify as computer, social, data, or information scientists, including linguists. We work to identify common principles, algorithms and tools to advance text-intensive research, and break down the boundaries between domains, to foster exchange and new collaborations among like-minded researchers. TextXD aims to:

Foster a cross-disciplinary community of text processing experts from...

Machine Learning and Science Forum

The BIDS Machine Learning and Science Forum (originally the Berkeley Statistics and Machine Learning Forum) was launched in Spring 2018 and currently meets biweekly (during the spring and fall semesters) to discuss current applications across a wide variety of research domains in the physical sciences and beyond. These active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. This Forum is hosted by BIDS Faculty Affiliate...

Data Science By Design (DSxD)

Data Science By Design (DSxD) is a community of practice to curate ideas about data narratives, innovative communication approaches and aesthetic visual design principles. This project aims to conscientiously develop the future of data science with diversity, inclusion, and open education as guiding principles that incorporate transparent research practices and accessible training. Our events and resources will empower and support attendees to produce visual content that effectively and artfully communicates the practice of data science research in the form of how-to...