Abstract: Scientific publications often describe in a methods section all the steps involved in obtaining a new finding. Studies have shown that those sections are highly incomplete and ambiguous, and making the transparency and reproducibility effort out of the reach of readers. This is particularly challenging in computational modeling, since many real-world problems require integrating diverse models from different disciplines and unfortunately remain one-of efforts. We have been examining computational models from the lens of transparency and reproducibility. We have gathered requirements on the information that scientists need, analyzed sources of information beyond papers and including code repositories and community boards, and started to use text extraction techniques for model metadata to create structured model catalogs with rich knowledge about scientific models. Many challenges remain, including extracting abstractions, code sequences and workflows, and training/calibration procedures for models. If we succeed, this work will open the door to AI systems that can automate important aspects of science and accelerate discoveries.

Bio: Dr. Yolanda Gil is Director of New Initiatives in AI and Data Science in USC’s Viterbi School of Engineering, and Research Professor in Computer Science and in Spatial Sciences. She is also Director of Data Science programs and of the USC Center for Knowledge-Powered Interdisciplinary Data Science. She received her M.S. and Ph. D. degrees in Computer Science from Carnegie Mellon University, with a focus on artificial intelligence. Dr. Gil collaborates with scientists in several domains on developing AI scientists. She is a Fellow of the Association for Computing Machinery (ACM), the Association for the Advancement of Science (AAAS), and the Institute of Electrical and Electronics Engineers (IEEE). She is also Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), and served as its 24th President.