The seminar is entitled, “Imageomics: Images as the Source of Information about Life” and will be hosted in 231 Hesburgh Library.
Dr. Tanya Berger-Wolf is a Professor of Computer Science Engineering, Electrical and Computer Engineering, and Evolution, Ecology, and Organismal Biology at the Ohio State University, where she is also the Director of the Translational Data Analytics Institute. As a computational ecologist, her research is at the unique intersection of computer science, wildlife biology, and social sciences. She creates computational solutions to address questions such as how environmental factors affect the behavior of social animals (humans included). Berger-Wolf is also a director and co-founder of the conservation software non-profit Wild Me, home of the Wildbook project, which brings together computer vision, crowdsourcing, and conservation. Wildbook has been recently chosen by UNSECO as one of the top AI 100 projects worldwide supporting the UN Sustainable Development Goals. It has been featured in media, including Forbes, The New York Times, CNN, National Geographic, and most recently The Economist.
Berger-Wolf has given hundreds of talks about her work, including at TED/TEDx, UN/UNESCO AI for the Planet, and SXSW EDU. Prior to coming to OSU in January 2020, Berger-Wolf was at the University of Illinois at Chicago. Berger-Wolf holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. She has received numerous awards for her research and mentoring, including University of Illinois Scholar, UIC Distinguished Researcher of the Year, US National Science Foundation CAREER, Association for Women in Science Chicago Innovator, and the UIC Mentor of the Year.
Abstract:
Introducing the new field of imageomics: from images to biological traits using biology-structured machine learning.
Images are the most abundant, readily available source for documenting life on the planet. Coming from natural history collections, laboratory scans, field studies, camera traps, wildlife surveys, autonomous vehicles on the land, water, and in the air, as well as tourists’ cameras, citizen scientists’ platforms, and posts on social media, there are millions of images of living organisms. But their power is yet to be harnessed for science and conservation. Even the traits of organisms cannot be readily extracted from images. The analysis of traits, the integrated products of genes and environment, is critical for biologists to predict effects of environmental change or genetic manipulation and to understand the significance of patterns in the four billion year evolutionary history of life.
I will show how data science and machine learning can turn massive collections of images into high resolution information database about wildlife, enabling scientific inquiry, conservation, and policy decisions. I will share our vision of the new scientific field of imageomics.