Join us for the annual BIDS Data Science Faire! This year’s Data Science Faire will feature a variety of presentations, lightning talks, posters and demonstrations from researchers across campus. Participants will learn about exciting data-intensive research and open source projects, and connect with UC Berkeley data scientists representing a wide variety research domains and methodological areas. Our Keynote Address will be presented by Rachel Thomas of fast.ai. Our industry partners, Siemens and State Street, will also be available at the faire to showcase their efforts in data science and discuss career opportunities at their organizations.
AGENDA
1:30 PM -- Event Begins!
1:50 PM -- Welcome Remarks
2:00-2:30 PM -- Keynote Address
Rachel Thomas - Accessible AI: Expanding the Reach of Intelligent Solutions
3:00-3:10 PM -- Lightning talks from BIDS Fellows
Kellie Ottoboni - Facilitating the Implementation of Post-Election Risk-Limiting Audits
Zexuan Xu - Data-driven Predictive Model with Machine Learning Techniques for Hydrologic Forecasting from Watershed to Global
Andreas Zoglauer - Enhancing the data analysis pipeline of the COSI space telescope with machine learning
3:30-3:40 PM -- Lightning talks from BIDS Fellows
Maryana Alegro - Deep learning for billion-pixel digital pathology analysis: application in mapping Tau protein in the human brain
Garret Christensen - Data Sharing & Citations: Causal Evidence from Economics and Political Science
Nick Adams - Forget AI: We Need Collective Intelligence
4:00-4:10 PM -- Lightning talks from BIDS Fellows
Deborah Sunter - City-Integrated Renewable Energy for a Sustainable Future
Henry Pinkard - Dynamic, whole organ imaging using sample adaptive two-photon microscopy
Orianna DeMasi - Using Data to Train Helpline Counselors
Stuart Geiger - Developing Desirable and Sustainable Career Paths for Academic Data Scientists
4:30-4:40 PM -- Lightning talks from BIDS Fellows
Marla Stuart - From X to 1: Processing Data for Reproducibility
Soeren Kuenzel - Transfer Learning for Deep Causal Inference
Joh Schöneberg - Big Data Image Analysis in Advanced Lattice Light-Sheet Microscopy
5:00 PM -- Event Concludes
With questions, please contact bids@berkeley.edu.
Speaker(s)
Rachel Thomas
fast.ai
Rachel Thomas was selected by Forbes as one of 20 Incredible Women in AI, earned her math PhD at Duke, and was an early engineer at Uber. She is co-founder of fast.ai, which created the “Practical Deep Learning for Coders” course that over 100,000 students have taken. Rachel is a popular writer and keynote speaker. Her writing has been read by over half a million people; has been translated into Chinese, Spanish, Korean, & Portuguese; and has made the front page of Hacker News 7x. She is on twitter @math_rachel and her website is here.