machine learning and the life sciences: beyond data analysis · ryan adams machine learning and the...

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Ryan Adams MACHINE LEARNING and the LIFE SCIENCES: BEYOND DATA ANALYSIS presented by the Data Sciences Platform, the Stat Math Reading Club, and the office of the Chair of the Faculty Assistant Professor, SEAS, Harvard University Group leader, Harvard Intelligent Probabilistic Systems Head of Research, Twitter Cortex Co-host, Talking Machines www.thetalkingmachines.com Machine learning is about understanding and building computational processes for adapting to data and experience – an ability that for most of natural history has only existed in living organisms. It’s also a valuable source of new tools for analyzing biological data. But does its relevance extend beyond data analysis? Adams will discuss two approaches that push the boundary: automated design of biologically relevant systems (e.g., organic molecules, DNA sequences, biomimetic robots), and the use of biomolecules to implement machine learning algorithms without the need for digital models of computation. Nov. 12, 1 PM Auditorium, Broad Institute 415 Main St., Cambridge (Kendall Square) Eliza Grinnell, Harvard SEAS

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Page 1: MACHINE LEARNING and the LIFE SCIENCES: BEYOND DATA ANALYSIS · Ryan Adams MACHINE LEARNING and the LIFE SCIENCES: BEYOND DATA ANALYSIS presented by the Data Sciences Platform, the

Ryan Adams

MACHINE LEARNING and the LIFE SCIENCES: BEYOND DATA ANALYSIS

presented by the Data Sciences Platform, the Stat Math Reading Club, and the

office of the Chair of the Faculty

Assistant Professor, SEAS, Harvard University

Group leader, Harvard Intelligent Probabilistic Systems

Head of Research, Twitter Cortex Co-host, Talking Machines www.thetalkingmachines.com

Machine learning is about understanding

and building computational processes for

adapting to data and experience – an ability

that for most of natural history has only

existed in living organisms. It’s also a

valuable source of new tools for analyzing

biological data. But does its relevance

extend beyond data analysis? Adams will

discuss two approaches that push the

boundary: automated design of biologically

relevant systems (e.g., organic molecules,

DNA sequences, biomimetic robots), and

the use of biomolecules to implement

machine learning algorithms without the

need for digital models of computation.

Nov. 12, 1 PM Auditorium, Broad Institute

415 Main St., Cambridge (Kendall Square)

Eliza Grinnell, Harvard SEAS