Machine Learning

Need help with Machine Learning?

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Below are the consultant we have available with Machine Learning and other expertise listed.

Renata Barreto, JD, Ph.D.

Research Fellow
Berkeley Law

Renata is a JD / Ph.D. candidate at Berkeley, where her research focuses on the harms caused by machine learning models on marginalized groups. She is trained in computational social science and has interned at Twitter and Facebook. She enjoys learning both programming and human languages.

Alex Bruefach

Discovery Graduate Fellow
Materials Science and Engineering

Alex is a PhD Candidate in materials science and engineering developing image processing and machine learning techniques for extracting information from electron microscopy datasets. Her primary focus is understanding what information is transferred from various feature representations of images. She has extensive experience collaborating across boundaries and is passionate about brainstorming innovative approaches to challenging data science problems!

Nikita Samarin

Electrical Engineering and Computer Science (EECS)

Nikita Samarin is a doctoral student in Computer Science in the Department of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley advised by Serge Egelman and David Wagner. His research focuses on computer security and privacy from an interdisciplinary perspective, combining approaches from human-computer interaction, behavioral sciences, and legal studies. Samarin is a member of the Berkeley Lab for Usable and Experimental Security (BLUES) and an affiliated graduate researcher at the Center for Long-Term Cybersecurity (CLTC) and the...

Christopher Paciorek, Ph.D.

Research Computing Consultant, Adjunct Professor
Department of Statistics
Research IT

Chris Paciorek is an adjunct professor in the Department of Statistics, as well as the Statistical Computing Consultant in the Department's Statistical Computing Facility (SCF) and in the Econometrics Laboratory (EML) of the Economics Department. He is also a user support consultant for Berkeley Research Computing. He teaches and presents workshops on statistical computing topics, with a focus on R.

Racism Narratives in Medical Literature

Systemic racism is a driving factor in unequal health outcomes, but it is rarely the subject of study in top medical journals (see a 2021 analysis by Krieger et al.). This project, a collaboration between the UC Berkeley D-Lab and the American Medical Association's Center for Health Equity, aims to measure progress in acknowledging, studying, & dismantling racism by creating tools to track racism-related narratives in influential medical research.

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

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!


Python Deep Learning: Parts 1-2

June 7, 2022, 1:00pm
This workshop presents a brief history of Artificial Neural Networks (ANNs) and an explanation of the intuition behind them; a step-by-step reconstruction of a very basic ANN, and then how to use the scikit-learn library to implement an ANN for solving a classification problem.
See event details for participation information.

Python Introduction to Machine Learning: Parts 1-2

May 24, 2022, 1:00pm
This workshop introduces students to scikit-learn, the popular machine learning library in Python, as well as the auto-ML library built on top of scikit-learn, TPOT. The focus will be on scikit-learn syntax and available tools to apply machine learning algorithms to datasets. No theory instruction will be provided.
See event details for participation information.

What is MLOps? An Introduction to the World of Machine Learning Operations

May 10, 2022
More than ever, AI and machine learning (ML) are integral parts of our lives and are tightly coupled with the majority of the products we use on a daily basis. We use AI/ML in almost everything we can think of, from advertising to social media and just going about our daily lives! With the prevalent use of these tools and models, it is essential that, as IT systems and software became a disciplined practice in terms of development, maintainability, and reliability in the early 2000s, ML systems follow a similar trend. The field focused on developing such practices is currently loosely defined under many different titles (e.g., machine learning engineering, applied data science), but is most commonly known as MLOps, or Machine Learning Operations.