Scaling Science: Optimizing Scientific Investment with Machine Learning and Network Science
发布时间 :2019-09-26  阅读次数 :3007




报告人:James Woodward Weis






James W. Weis is a research affiliate of the MIT Media Lab and a doctoral candidate in the MIT Computational and Systems Biology program. James has published peer-reviewed work in areas as diverse as synthetic biology, computational chemistry, machine learning, and technology transfer, been featured by news outlets from the USA to Brazil, and has given numerous invited lectures internationally. James was also the founder of Nest.Bio Labs, a Founding Partner at Nest.Bio Ventures, Founder of the MIT Alumni Life Science Angels of Boston, Founding President of the MIT Biotech Group, and a quantitative trader in New York City.



From Göbekli Tepe to the World Wide Web, the story of civilization is the story of collaboration—the goal-oriented organization of resources. However, while the quantity, speed, complexity, and economic importance of the scientific enterprise have grown exponentially in recent decades, our core resource allocation frameworks have failed to scale accordingly. The application of modern machine learning methods on the history of science could close this gap—and thus dramatically improve the efficiency of the hundreds of billions of dollars that are deployed to support research and development annually. In this talk, James will discuss projects that he is pioneering within the MIT Media Lab. By collecting, structuring, and computing on over 5 billion data points, we can gain insight into the features that predict highly-impactful technologies, propose ideas for collaborations, and—via the application of portfolio theory—suggest impact-optimized funding strategies.