Data Science Software Development Core

Liya Ding is a Data Scientist for the DSI and the Center for Living Systems under Margaret Gardel. She received an M.S. degree from the Department of Electronic and Information Engineering at Zhejiang University in 2003 and a Ph.D. degree from the Department of Electrical and Computer Engineering at Ohio State University in 2009. Subsequently, she did postdoctoral training at the Department of Biomedical Informatics at Ohio State University, the Department of Cell Biology at Harvard Medical School, and the Department of Cell Biology at the UT Southwestern Medical Center.

Her professional trajectory includes roles as a Scientist at the Allen Institute for Cell Science, and later as an Associate Professor at the Institute for Brain and Intelligence at Southeast University.

Her interests and fields of research are in computer vision, data science, machine learning, and computational cell biology, with a focus on microscopy image processing and quantitative data analysis.

Alice Duan is a software engineer/data scientist working with grantees of the 11th Hour Project, where she collaborates with community organizations across different sectors to implement projects and build capacity to increase their impact. Before joining the DSI, she worked as a software engineer at the civic tech nonprofit Democracy Works, where her work focused on voter engagement and election administration data. She has a BS in Computer Science from Carnegie Mellon University.

David Jacobson is the Data & Engineering Manager for the Data Science Institute Core Facility team. David specializes in leading machine learning and data science teams to develop practical solutions to complex real-world problems. His work is focused on data science projects for social good, especially related to climate, agriculture, human rights, and global health.

Prior to joining DSI, David has led data science teams in biotech, academia, and software technology, most recently at Meliora Therapeutics, an AI startup for small-molecule cancer drug development, and for the Colorado School of Public Health COVID-19 Modeling Group, where his team developed novel approaches to modeling the spread of COVID-19 and the impact of government policy. He holds a BA in Mathematics from Amherst College.

Jim Pivarski is a data scientist/engineer who has worked in and out of academia. He was trained as a particle physicist with a Ph.D. from Cornell and helped to commission and analyze first data from the CMS experiment at the Large Hadron Collider (LHC) in Geneva. He then worked as a data science consultant at Open Data Group, analyzing data from commercial clients, and then at Princeton, developing analysis software for physicists and promoting better integration with the world beyond academia. Jim is the original author of Awkward Array, a NumFOCUS affiliated project with thousands of users. Now he is analyzing data at U. Chicago’s Data Science Institute for the 11th Hour Project and its philanthropic goals.

Trevor is a Software Engineer II at the DSI. He helps social impact organizations to enhance their operations, research, and communication by utilizing software engineering and data science tools. His work focuses on agriculture, human rights, energy, and marine technology. Trevor also mentors student teams in the Data Science Clinic. Before DSI, Trevor worked as a research assistant at Argonne National Laboratory. Trevor has a BS in Computer Science from MIT.

Nikki Tebaldi is an Open Source Research Software Engineer at the Data Science Institute, where she collaborates with postdoctoral fellows to develop open-source software that advances their research goals. She also contributes to institute-wide projects that develop and maintain open-source tools, data workflows, and infrastructure supporting reproducible and collaborative research.

Before joining the Data Science Institute, Nikki worked at NASA’s Jet Propulsion Laboratory, where she designed and deployed cloud-based scientific data processing workflows and operationalized algorithms to run efficiently at scale.