WiDS Worldwide and WiDS Berkeley 2022

Women in Data Science (WiDS)


March 7, 2022 to March 9, 2022
6:00am to 6:00pm
Virtual Participation

WiDS Worldwide 2022
Monday, March 7, 2022
Virtual Participation – Register Now

WiDS Berkeley 2022
Monday, March 7-9, 2022
Virtual Participation – Register Now

Women in Data Science (WiDS) aims to inspire and educate data scientists worldwide, regardless of gender, and to support women in the field. WiDS Worldwide 2022 on March 7 will feature outstanding women contributors in the field of data science and related areas. Women in Data Science (WiDS) is spearheaded by the Stanford Institute for Computational and Mathematical Computing (ICME) ​and Stanford Data Science. For more information, contact widsconference@stanford.edu.

BIDS' Deb Agarwal and Ciera Martinez will be featured speakers for WiDS Berkeley 2022, a livestreamed program highlighting distinguished scholars and practitioners from across the Bay Area and the world. These events will cover a wide range of technology and application areas, and all genders are welcome and encouraged to attend. This year's WiDS Berkeley event is being sponsored by UC Berkeley's School of Information; the Division of Computing, Data Science and Society; One IT; D-Lab; Research IT; Research, Teaching, and Learning; the UC Berkeley Library; and the Lawrence Berkeley National Laboratory. For more information, contact the WiDS Berkeley planning team: wids@berkeley.edu.


Deb Agarwal

Senior Scientist and Scientific Data Division (SciData) Director, LBNL

Deb Agarwal is a Senior Scientist and the Director of the Scientific Data (SciData) Division at Lawrence Berkeley National Laboratory. Dr. Agarwal's research focuses on scientific tools which enable sharing of scientific experiments, advanced networking infrastructure to support sharing of scientific data, data analysis support infrastructure for eco-science, and cybersecurity infrastructure to secure collaborative environments. Dr. Agarwal is a Research Affiliate at the Berkeley Institute for Data Science and an Inria International Chair, where she co-leads the DALHIS (Data Analysis on Large-scale Heterogeneous Infrastructures for Science) Inria Associate team. Dr. Agarwal also leads teams developing data server infrastructure to significantly enhance data browsing and analysis capabilities and enable eco-science synthesis at the watershed-scale to understand hydrologic and conservation questions and at the global-scale to understand carbon flux. Some of the projects Dr. Agarwal is working on include: Enviromental Systems Science Digital Infrastructure for a Virtual Ecosystem (ESS-DIVE), Watershed Function SFA, AmeriFlux Management Project, FLUXNET, NGEE Tropics, International Soil Carbon Network. Dr. Agarwal received her Ph.D. in electrical and computer engineering from University of California, Santa Barbara and a B.S. in Mechanical Engineering from Purdue University.

Ciera Martinez

Biology and Environmental Sciences Lead

BIDS Biology and Environmental Sciences Lead Ciera Martinez focuses on data intensive research projects that aim to understand how life on this planet evolves in reaction to the environment and climate – especially projects involving large and complex datasets.  A long-time open science advocate, Ciera has been involved with and continues to be interested in working on training for open data, education, publishing, and software, including developing community standards for data management practices.  As a 2019 Mozilla Open Science Fellow, she connected her love of data and museums and worked on projects aimed at understanding and increasing the usability of biodiversity and natural history museum data.  She received her PhD in Plant Biology from UC Davis, researching the genetic mechanisms regulating plant architecture.  She then went on to become a NSF Postdoctoral Fellow at UC Berkeley in the Molecular and Cellular Biology Department, studying genome evolution.  She was also a BIDS postdoctoral Data Science Fellow for 3 years, working on undergraduate research practices, data science training, community development, and best practices for data science, diversity and inclusion, and computational research.