Digital Humanities

Carl Illustrisimo

Consulting Drop-In Hours: By appointment only

Consulting Areas: Bash or Command Line, Cluster Analysis, Data Sources, Data Visualization, Digital Humanities, Excel, Git or GitHub, Javascript, LaTeX, Machine Learning, Natural Language Processing (NLP), Python, Regression Analysis, RStudio, SQL, Text Analysis

Quick-tip: the fastest way to speak to a consultant is to first ...

Vy Ngo Thai

Consulting Drop-In Hours: By appointment only

Consulting Areas: Python, SQL, Javascript, HTML / CSS, APIs, Data Visualization, Databases and SQL, Digital Humanities, Web Scraping, Software Development, Git or GitHub, Tableau

Quick-tip: the fastest way to speak to a consultant is to first submit a request...

Digital Humanities Working Group Meetup

March 17, 2023, 12:00pm
The UC Berkeley Digital Humanities Working Group is a research community founded to facilitate interdisciplinary conversations in the digital humanities and cultural analytics. Our gatherings are participant driven and provide a place for sharing research ideas (including brainstorming new ideas and receiving feedback from others), learning about the intersection of computational methods and humanistic inquiry, and connecting with others working in this space at Berkeley.

Digital Humanities Working Group (April 2nd, 2024)

April 2, 2024, 12:30pm
The UC Berkeley Digital Humanities Working Group is a research community founded to facilitate interdisciplinary conversations in the digital humanities and cultural analytics. Our gatherings are participant driven and provide a place for sharing research ideas (including brainstorming new ideas and receiving feedback from others), learning about the intersection of computational methods and humanistic inquiry, and connecting with others working in this space at Berkeley.

Digital Humanities Working Group (April 2024)

April 30, 2024, 12:30pm
The UC Berkeley Digital Humanities Working Group is a research community founded to facilitate interdisciplinary conversations in the digital humanities and cultural analytics. Our gatherings are participant driven and provide a place for sharing research ideas (including brainstorming new ideas and receiving feedback from others), learning about the intersection of computational methods and humanistic inquiry, and connecting with others working in this space at Berkeley.

Forecasting Social Outcomes with Deep Neural Networks

October 7, 2025
by Paige Park. Our capacity to accurately predict social outcomes is increasing. Deep neural networks and artificial intelligence are crucial technologies pushing this progress along. As these tools reshape how social prediction is done, social scientists should feel comfortable engaging with them and meaningfully contributing to the conversation. But many social scientists are still unfamiliar with and sometimes even skeptical of deep learning. This tutorial is designed to help close that knowledge gap. We’ll walk step-by-step through training a simple neural network for a social prediction task: forecasting population-level mortality rates.

Bee Lehman, Ph.D.

Literatures and Digital Humanities Librarian
UC Berkeley Library

Bee Lehman is a specialist in Information Literacy. They earned their MLIS from Simmons University in 2007 and their Ph.D. in History from UNC at Chapel Hill in 2017. They specialize in European migration, digital humanities, and travel literature.

Weiying Li

Data Science & AI Fellow 2025-2026
Berkeley Graduate School of Education

Weiying is a Ph.D. candidate in Learning Sciences and Human Development at the UC Berkeley School of Education, with a Designated Emphasis in New Media. Her research focuses on designing and evaluating AI dialogs that support students in learning complex science concepts and engaging with social justice topics in science, such as food access. She uses mixed methods to investigate how iterative prompt design, developed in collaboration with teachers, can deepen students’ knowledge integration. Her work contributes to the development of responsible and adaptive AI tools for...

Decision-Making Under Pressure during My PhD: Lessons from whale songs and ocean noise

May 6, 2025
by Jaewon Saw. This blog post shares a story from a field experiment using Distributed Acoustic Sensing (DAS) to detect whale vocalizations in Monterey Bay. Most of the data got overwhelmed by noise from boat engines, wave motion, and cable instability. On the final day, a spur-of-the-moment decision to add loops to the fiber optic cable dramatically improved signal quality.

Predicting the Future: Harnessing the Power of Probabilistic Judgements Through Forecasting Tournaments

April 29, 2025
by Christian Caballero. From the threat of nuclear war to rogue superintelligent AI to future pandemics and climate catastrophes, the world faces risks that are both urgent and deeply uncertain. These risks are where traditional data-driven models fall short—there’s often no historical precedent, no baseline data, and no clear way to simulate a future world. In cases like this, how can we anticipate the future? Forecasting tournaments offer one answer, harnessing the wisdom of crowds to generate probabilistic estimates of uncertain future events. By incentivizing accuracy through structured competition and deliberation, these tournaments have produced aggregate predictions of future events that outperform well-calibrated statistical models and teams of experts. As they continue to develop and expand into more domains, they also raise urgent questions about bias, access, and whose knowledge gets to shape our collective sensemaking of the future.