R

Farnam Mohebi

Data Science Fellow
Haas School of Business

I am a PhD student at the Haas School of Business, University of California, Berkeley, and a researcher in the Department of Radiation Oncology at the University of California, San Francisco, having previously earned my MD and MPH degrees. My research focuses on the intersection of professionals and emerging technologies, drawing from the fields of medical sociology, organizational theory, and science and technology studies. I am particularly fascinated by the evolving relationship between physicians and artificial intelligence, the phenomenon of physician influencers, and the social...

Mingyu Yuan

Data Science for Social Justice Senior Fellow 2024
Linguistics

I am a Ph.D. candidate in Linguistics, with a focus on phonetics and phonology, specifically speech production in neuro-atypical populations. I use methods from Natural Language Processing in my day-to-day research.

Hellina Hailu Nigatu

Data Science for Social Justice Senior Fellow 2024
Electrical Engineering and Computer Science (EECS)

I am a PhD student at UC Berkeley in the EECS department co-advised by Prof. Sarah Chasins and Prof. John Canny. My research interest broadly lies in the intersection of AI and HCI, with a focus on making usable AI tools accessible to end users.

I am currently looking into making NLP tools usable and accessible for low-resourced languages. I am also interested in the impact of AI on society, specifically in how it affects Global Majority countries and communities. Outside of research, I like to read books, make and drink traditional Ethiopian coffee, knit,...

Violet Davis

Data Science for Social Justice Senior Fellow 2024
MIDS

I am a Masters student studying Data Science with the School of Information. My research involves computational social science projects focused on social justice and equity.

Sahiba Chopra

Data Science Fellow 2024
Haas

I'm a PhD student in the Management and Organizations (Macro) group at Berkeley Haas. I have a diverse professional background, primarily as a data scientist across numerous industries, including fintech, cleantech, and media. I hold a BA in Economics from the University of Maryland, an MS in Applied Economics from the University of San Francisco, and an MS in Business Administration from UC Berkeley.

My research focuses on the intersection of inequality, technology, and the labor market. I am particularly interested in understanding how to reduce inequality in...

Maddie Taylor

Data Science for Social Justice Fellow 2024
Environmental Science, Policy and Management

Maddie is a third year Ph.D. Student in Environmental Science, Policy, & Management. Their research brings together disability studies and environmental justice to explore how disabled and chronically ill communities are affected by climate change.

They graduated from Barnard College in 2017, and their experiences navigating ableism while working in the environmental field led them to their current research interest at UC Berkeley. In their free time they enjoy knitting, playing music, and exploring the Bay Area with their dog, Winnie.

R Fundamentals: Parts 1-4

October 3, 2022, 4:00pm
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

R Data Visualization

February 16, 2023, 10:00am
This workshop will provide an introduction to graphics in R with ggplot2. Participants will learn how to construct, customize, and export a variety of plot types in order to visualize relationships in data. We will also explore the basic grammar of graphics, including the aesthetics and geometry layers, adding statistics, transforming scales, and coloring or panelling by groups. You will learn how to make histograms, boxplots, scatterplots, lineplots, and heatmaps as well as how to make compound figures.

R Fundamentals: Parts 1-4

December 4, 2023, 10:00am
This workshop is a four-part introductory series that will teach you R from scratch with clear introductions, concise examples, and support documents. You will learn how to download and install the open-sourced R Studio software, understand data and basic manipulations, import and subset data, explore and visualize data, and understand the basics of automation in the form of loops and functions. After completion of this workshop you will have a foundational understanding to create, organize, and utilize workflows for your personal research.

R Introduction to Deep Learning: Parts 1-2

March 29, 2022, 10:00am
This workshop introduces the basic concepts of Deep Learning — the training and performance evaluation of large neural networks, especially for image classification, natural language processing, and time-series data. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels.