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5253 search results found
  • February 16, 1998
    Department of Mechanical Engineering

    Precision Engineering Research Group (PERG)

    Principal Investigator Alexander Slocum

  • May 28, 2010
    Department of Nuclear Science and Engineering

    Quantum Engineering Group (QEG)

    Principal Investigator Paola Cappellaro

  • November 25, 2015
    Department of Electrical Engineering and Computer Science

    EQuS: Engineering Quantum Systems

    Principal Investigator Terry Orlando

  • June 12, 2006
    Anthropology Program

    Developing Diverse Leadership for Engineering

    Principal Investigator Susan Silbey

  • The TILT team pitches at the IDEAS Generator dinner in fall 2019.Photo: Halley Kamerkar
    May 1, 2020 MIT News

    MIT IDEAS celebrates social innovation at the Institute

  • 2024 MIT Seoul Life Science Symposium

    Wed, July 10, 2024 Conference
    Seoul, Korea

    The biopharma industry has been continuously growing and progressing with digital transformation. The Symposium will highlight the Current Status and Promise of mRNA Biotherapeutics and introduce Innovation in mRNA Biotherapeutics Manufacturing at MIT.

    Join us at the 2024 MIT Seoul Life Science Symposium, organized by KoreaBio (Korea Biotechnology Industry Organization) and MIT ILP. The symposium will also bring you closer to the MIT Life Science Innovation Ecosystem and allow you to explore global trends in MIT’s innovative Drug Discovery and Manufacturing research and experiments from leading MIT faculty and MIT-connected startups.

  • February 12, 2007
    Department of Chemical Engineering

    Chemical and Pharmaceutical Engineering (CPE)

    Principal Investigator T Hatton

  • May 2024
    ILP Research Survey

    AI Applications: Social Sciences

    Download PDF Download PDF
  • AI in LIfe Science 2018 - Dimitris Bertsimas

    December 4, 2018Conference Video Duration: 28:23

    Interpretable AI

    This talk introduces a new generation of machine learning methods that provide state of the art performance and are very interpretable, introducing optimal classification (OCT) and regression (ORT) trees for prediction and prescription with and without hyperplanes. This talk shows that (a) Trees are very interpretable, (b) They can be calculated in large scale in practical times, and (c) In a large collection of real world data sets, they give comparable or better performance than random forests or boosted trees. Their prescriptive counterparts have a significant edge on interpretability and comparable or better performance than causal forests. Finally, we show that optimal trees with hyperplanes have at least as much modeling power as (feedforward, convolutional, and recurrent) neural networks and comparable performance in a variety of real world data sets. These results suggest that optimal trees are interpretable, practical to compute in large scale, and provide state of the art performance compared to black box methods.

    2018 MIT AI in Life Sciences and Healthcare Conference
  • September 29, 2016

    Plasma Science -- Lab Astrophysics (LDX)

    Principal Investigator Anne White

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