Dr. Tanya Berger-Wolf is the Director of the Translational Data Analytics Institute and a Professor of Computer Science Engineering, Electrical and Computer Engineering, as well as Evolution, Ecology, and Organismal Biology at the Ohio State University. Recently she has been awarded US National Science Foundation grant to establish a new field of study: Imageomics. As a computational ecologist, her research is at the unique intersection of computer science, wildlife biology, and social sciences.
Berger-Wolf is a member of the US National Academies Board on Life Sciences and CNRS International Scientific Advisory Board, Artificial Intelligence for Science, Science for Artificial Intelligence (AISSA) Centre. She served on the Global Partnership on AI (GPAI) working group on AI and Biodiversity, WWF working group on AI Collaboration to End Wildlife Trafficking, AAAS-FBI Big Data in the Life Sciences and National Security Working Group, and the organizing committee of the National Academies First U.S.-Africa Frontiers of Science, Engineering, and Medicine Symposium, among many others.
Berger-Wolf is also a director and co-founder of the AI for wildlife conservation software non-profit Wild Me, home of the Wildbook project, has been 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, National Geographic, The New York Times, CNN, and The Economist.
Prior to coming to OSU in January 2020, Berger-Wolf was at the University of Illinois at Chicago. She holds a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign. She is widely published and is a sought-after invited speaker. Berger-Wolf has received numerous awards for her research and mentoring, including the highest honor of 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.
Data science, computational ecology, imageomics, AI for good, AI for conservation, AI and biodiversity, network analysis, applied machine learning