Trusting any data set or analysis requires a leap of faith. Beyond an acceptance of margins of error and biases, all data-driven decisions necessitate a will to believe. When it comes to data that impacts or justifies institutional decisions, this belief must exist not only in the institution's ability to be honest and rigorous with data, but in the very authority of data itself to tell us something meaningful about the world. In an era of “alternative facts” and fear-based advocacy, we must contend with this; but it may also sometimes be a symptom of data tunnel vision. How can we be better at designing the conditions for people to develop faith in our (and their) ability to do good things with data? And how can purposefully-deployed inefficiencies improve the resilience of human systems?
Principal Investigator Karl Berggren
Manolis Kellis Professor, MIT Department of Electrical Engineering and Computer Science
Antoine Allanore
The world of quantum mechanics holds enormous potential to address unsolved problems in communications, computation, precision measurements, and machine learning/AI. Dr. Englund's QP-Group at MIT pursues experimental and theoretical research towards machine learning hardware and critical quantum technologies (computing, networking, sensing) by precision control of photons and atomic systems, combining techniques from atomic physics, optoelectronics, and modern semiconductor devices. In this talk, Dr. Englund will share some of the latest research conducted by his group at MIT and their potential applications.
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Assistant
Brian Anthony | Associate Director, MIT.nano Moungi Bawendi Lester Wolfe Professor of Chemistry MIT Department of Chemistry Juejun (JJ) Hu Associate Professor, Department of Materials Science & Engineering Daniel Moyer Postdoctoral Associate, MIT Computer Science & Artificial Intelligence Lab