Natural Language Processing (NLP)

D-Lab & Graduate Division create inclusive data science summer program

August 9, 2023
by Vanessa Navarro Rodriguez. UC Berkeley's Social Sciences D-Lab and Graduate Division created the Data Science for Social Justice Program to address underrepresentation in data science. The program teaches diverse students critical data analysis and its applications in addressing societal injustices. The 8-week free summer course for admitted University of California students focuses on Python programming, Natural Language Processing, and value-informed data practices. It aims to empower students from underrepresented backgrounds and to bridge STEM with social justice. This blog post elaborates on the program's creation and features one of the DSSJ students, Robin López, and his reasons for participating.

Unlock the Joy and Power of Reading in Language Learning

August 21, 2023
by Bowen Wang-Kildegaard. I share my story of how reading for pleasure transformed my English speaking and writing skills. This experience inspired my passion to promote the joy and power of reading to all language learners. Using natural language processing techniques, I dive into the Language Learning subreddit, revealing a trend: Learners are often highly anxious about output practices, but are generally positive about input methods like reading and listening. I then distill complex language learning theories into actionable language learning tips, emphasizing the value of extensive reading for pleasure, pointing to potential methods like using ChatGPT for customization of reading materials, and advocating for joy in the learning journey.

My Summer Exploring Data Science for Social Justice: Learnings, Tensions & Recommendations

September 5, 2023
by Genevieve Smith. This summer I joined the D-Lab hosted Data Science for Social Justice workshop at UC Berkeley diving into Python – including TF-IDF, sentiment analysis, word embeddings, and more – with a lens towards leveraging data science for social justice. My team explored a Reddit channel on abortion and used computational analysis to answer key questions related to abortion access from before versus after Roe vs. Wade was overturned. Computational social science is incredibly powerful, but I continue to grapple with tensions particularly as it relates to employing machine learning and large language in international research, and end with key recommendations for CSS practitioners.

Daniel Lobo

Computational Social Science Fellow
Sociology

Daniel Lobo is a PhD student in the Department of Sociology with an emphasis in Political Economy at UC Berkeley. He is broadly interested in how culture, or the unspoken “rules of the game,” reproduces inequality within a system of racial capitalism. At the individual level, he is interested in documenting and measuring the extent to which cultural capital and social capital enable or constrain opportunities for intergenerational mobility. At the organizational level, he is interested in documenting and measuring the extent to which culturally-based selection and promotion processes...

Peter Amerkhanian

Graduate Student Researcher (GSR), Instructor
Goldman School of Public Policy (GSPP)

I’m a D-Lab GSR and a graduate student in The Goldman School’s Master of Public Policy/The I School’s Graduate Certificate in Applied Data Science. I have 5 years of experience working on data problems in government and nonprofits. I’m interested in social policy, program evaluation, and computational methods. Python is my principal language, but I’ve developed experience using and teaching a variety of other tools, including R, Excel, Tableau, and JavaScript. I deeply enjoy teaching data science methods and am excited to be a part of the D-Lab.

Aniket Kesari, Ph.D.

Former D-Lab Postdoc and Senior Data Science Fellow
Berkeley Law

Aniket Kesari was a postdoc and data science fellow at D-Lab. He is currently a research fellow at NYU’s Information Law Institute, and will join the faculty of Fordham Law School in 2023. His research focuses on law and data science, with particular interests in privacy, cybersecurity, and consumer protection.

Featured D-Lab Blog Post: Introducing “A Three-Step Guide to Training Computational Social Science Ph.D. Students for...

Bo Yun Park, Ph.D.

Postdoc
D-Lab

I am a Postdoctoral Scholar in the D-Lab at the University of California, Berkeley. My research lies at the intersection of political, cultural, and transnational sociology. I am particularly interested in dynamics of social inclusion and exclusion, social change, technology, and digital politics. My dissertation investigated how political strategists in France and the United States craft narratives of political leadership for presidential candidates in the digital age. I received my Ph.D. in Sociology at Harvard University, where I was affiliated with the Institute for Quantitative Social...

Abhishek Roy

IUSE Undergraduate Advisory Board
Economics
Data Science

I'm Abhishek Roy and I'm double majoring in Economics and Data Science. I've been a part of D-Lab's IUSE project since Spring 2020 and have truly found an organization that is not only passionate about Data Science but also strives to expand its reach equitably to all communities. I am involved in Research and Project Management roles in various departments and labs at Berkeley and I'm an Editor at the Berkeley Economic Review. I love diving into anything at the intersection of Data Science, Economics, Business, and Computational Social Science. Whenever I'm free, I love writing...

Erin Manalo-Pedro

Research Fellow
Community Health Sciences (UCLA)

Erin Manalo-Pedro is a Ph.D. student in the Department of Community Health Sciences at the UCLA Fielding School of Public Health with a minor in education. She focuses her racial health equity research on curriculum, the health workforce, and political interventions for communities of color. Drawing from Public Health Critical Race Praxis and Pinayism, she aims to use methods, like natural language processing and counter storytelling, to document the subtleties of structural racism and resistance from marginalized groups.

To guide her interdisciplinary approach, Erin leverages
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Swetha Pola

Research Fellow
School of Information

Swetha (she/her) is a 5th Year Master of Information and Data Science student at the School of Information, with experience in Cognitive Science, Psychology research, and product management. Her research interests include building ethical, transparent AI and the impacts of technologies (specifically, mass media, surveillance, and algorithms of bias) on longitudinal behavioral health. She is happy to help with questions on Python, R, SQL, machine learning, neural networks, statistical analysis, and research design!

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