The explosion of digital information offers an unprecedented opportunity to study the dynamics that shape human understanding, investigation and certainty. Our researchers are developing new Big Data, machine learning and crowd-sourcing approaches and techniques to:
Efficiently harvest and share knowledge and judgment from experts, people, articles, preprints, software, patient records, artifacts, videos and sensors
Learn how knowledge is made, used, certified and forgotten
Represent, recombine and generate knowledge in powerful ways
Improve knowledge generation, representation, management and innovation practices…
Knowledge Lab seeks to leverage insights into the dynamics of knowledge creation and advances in large-scale computation to reimagine the scientific process of the future by identifying gaps in the global knowledge landscape, areas of rich potential for breakthroughs, and automating discovery through the generation of novel, potentially high impact hypotheses.
By developing and blending a rich set of computational tools and techniques with novel scientific methods, Knowledge Lab is uncovering deep insights into the fundamental processes by which knowledge is conceived, validated, shared, and reinforced, and capitalizing on these insights to accelerate scientific progress by conceiving of and implementing revolutionary computational approaches to reading, reasoning, and hypothesis design that transcend the capacity of individual researchers and traditional teams.
Knowledge Lab’s research is currently being deployed to help national labs and Universities better understand and leverage their intellectual assets and outputs; help funding agencies to better understand and predict which research areas are the most promising; uncover and eliminate biases that hamper the scientific review process and the productive recombination of knowledge; understand the social and economic dynamics that underlie cities as engines of innovation; and, most recently, Knowledge Lab has mobilized these processes to revolutionize cancer research through Big Data and intelligent machines.
Knowledge Lab knits these research agendas together by operating a research center at the core of the University of Chicago’s Computation Institute. Knowledge Lab functions with a centralized, core research group of ten post-doctoral scholars, research programmers, and developers. In addition, Knowledge Lab leads a decentralized network of 40+ field leading scientists, mathematicians, and engineers and scholars from the world’s top institutions including: Harvard, Stanford, the Santa Fe Institute, Princeton, Cornell, UCLA, and Argonne National Laboratory. Work produced by the lab and associated network is regularly published in high impact journals including Science, Nature, and PNAS.
Knowledge Lab collaborates with corporations that are serious about taking on and solving hard problems in multimodal data integration, analysis, and prediction, as well as scaling and capitalizing on insights into social, scientific, and innovative processes. These collaborations take on a working group structure where corporate sponsors actively participate in funded research that is relevant to both the sponsor and the Lab.
Knowledge Lab has field leading expertise in:
• Mathematical and statistical modeling and simulation (e.g. predictive analytics)
• Complex, dynamic network analysis (e.g. social network analysis)
• Automated hypothesis generation (e.g. automated descovery)
• Multimodel data integration and analysis (e.g. intelligence)
• Natural language processing (e.g. text mining)
• Machine learning (e.g. learning from data)