BIDS Undergraduate Internships provide a variety of engaging data science research opportunities through UC Berkeley's Undergraduate Research Apprentice Program (URAP). BIDS Data Science Fellows, Faculty and Research Affiliates, and Staff mentors enlist undergraduate students to help design and implement collaborative projects in a variety of research areas, helping students develop skills in research, programming, paper writing, project management, and team building.
Fall 2021 Projects are now posted at the URAP website and eligible undergraduates may apply online August 18-30, 2021.
- Computational imaging and microscopy – Laura Waller, EECS
- Deep Learning in Medical Imaging – Maryam Vareth, BIDS and UCSF
- Deep Learning Methods for High Energy Physics – Ben Nachman, LBNL
- Demo Watch – Saul Perlmutter, BIDS; and Nick Adams, Goodly Labs
- Developing a dashboard for primary care clinicians to support situational awareness – Maryam Vareth and Akram Bayat, BIDS and UCSF
- Public Editor – Saul Perlmutter, BIDS; and Nick Adams, Goodly Labs
- Understanding physical processes and making environmental predictions using LSTM Neural Networks – Laurel Larsen, Geography
- Using Data Science to Improve COVID-19 Response in Developing Countries – Josh Blumenstock, I-School
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Fall 2021 BIDS Undergraduate Internships - Apply August 18-30
August 17, 2021 | BIDS News
Spring 2021 BIDS Undergraduate Internships - Apply January 12-25, 2021
December 15, 2020 | BIDS News
Fall 2020 BIDS Undergraduate Internships - Apply August 19-31, 2020
August 6, 2020 | BIDS News
Spring 2020 Projects
- BIDS Machine Shop - Stéfan van der Walt, BIDS
- Computational imaging and microscopy - Laura Waller, EECS
- Cryptography of the unknown regions of genomes - Ciera Martinez, BIDS
- Data science software stack for julia - David Anthoff, Energy and Resources Group
- Deep Learning in Medical Imaging - Maryam Vareth, BIDS and UCSF
- Enabling future gamma-ray space missions - Andreas Zoglauer, Space Sciences Laboratory
- Garbage In, Garbage Out? Do Machine Learning Research Papers Report Where Training Data Comes From? - Stuart Geiger, BIDS
- Hydrologic forecasting for the East River, CO - Zexuan Xu, LBNL
- Public Editor - Saul Perlmutter, BIDS; and Nick Adams, Goodly Labs
Fall 2019 Projects
- BIDS Machine Shop - Stéfan van der Walt, BIDS
- Computational imaging and microscopy - Laura Waller, EECS
- Cryptography of the unknown regions of genomes - Ciera Martinez, BIDS
- Data science software stack for julia - David Anthoff, Energy and Resources Group
- Deep Learning in Medical Imaging - Maryam Vareth, BIDS and UCSF
- Demo Watch: Discovering Patterns of Peace and Violence between Police and Protesters - Nick Adams, Goodly Labs
- Enabling future gamma-ray space missions - Andreas Zoglauer, Space Sciences Laboratory
- Garbage In, Garbage Out? Do Machine Learning Research Papers Report Where Training Data Comes From? - Stuart Geiger, BIDS
- Hydrologic forecasting for the East River, CO - Zexuan Xu, LBNL
- Liberating Archives: Opening up Public Data for Open Research - Nick Adams, Goodly Labs
- Public Editor - Saul Perlmutter, BIDS; and Nick Adams, Goodly Labs
BIDS undergraduate interns celebrate 2018-2019 milestones
May 20, 2019 | BIDS News
