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5215 search results found
  • August 2024
    ILP Research Survey

    AI Applications: Biology, Diagnostics, Therapeutics, Medical Monitoring & Treatment

    Download PDF Download PDF
  • July 8, 2009
    Department of Electrical Engineering and Computer Science

    MIT Statistics and Data Science Center

    Principal Investigator Devavrat Shah

  • December 5, 2017
    Department of Electrical Engineering and Computer Science

    HCI Engineering Group

    Principal Investigator Stefanie Mueller

  • J. Christopher Love - 2019 Life Science Conference

    December 10, 2019Conference Video Duration: 31:36

    Fast and Modular Manufacturing Systems for Biopharmaceuticals and Vaccines

    Recombinant biopharmaceuticals and vaccines represent a significant class of therapeutics and preventions. While the industry has established efficient platformed processes for the production of monoclonal antibodies at multi-ton scales, the improved precision of therapeutic indications and expanding molecular designs (such as bispecific antibodies, nanobodies, and others) add new challenges for the timely and cost-effective production of emerging therapeutic concepts. This talk will present an integrated approach to biomanufacturing that combines automated end-to-end production and purification along with a fast and engineering-friendly alternative host to enable a flexible platform for next-generation manufacturing. Examples in both biopharmaceuticals and vaccines will be presented.
     
    2019 MIT Increased Productivity in the Biopharmaceutical Industry Conference
  • 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

  • 99% Air - Nano-Engineering the Materials of the Future: Carlos M. Portela

    January 24, 2025Conference Video Duration: 42:47
    99% Air: Nano-Engineering the Materials of the Future
    Carlos M. Portela
    Robert N. Noyce Career Development Assistant Professor, MIT Department of Mechanical Engineering

    Architected materials—i.e., materials whose three-dimensional (3D) micro- or nanostructure has been engineered to attain a specific purpose—are ubiquitous in nature and have enabled properties that are unachievable by all other existing materials. Their concept relies on maximizing performance while requiring a minimal amount of material. Several human-made 3D architected materials have been reported to enable novel mechanical properties such as high stiffness-to-weight ratios or extreme resilience, especially when nanoscale features present. However, most architected materials have relied on advanced additive manufacturing techniques that are not yet scalable and yield small sample sizes. Additionally, most of these nano- and micro-architected materials have only been studied in controlled laboratory conditions, while our understanding of their performance in real-world applications requires attention.

    In this talk, we will explain the concept of architected materials, providing various examples that we routinely fabricate and test in our laboratory at MIT, and we will discuss how nanoscale features significantly enhance their performance. We will also discuss ongoing research directions that will not only allow us to scale-up their fabrication, but also understand how they perform in realistic conditions outside the laboratory—towards contributing to more efficient material solutions in industry and beyond.

  • Publication date: October 1, 2010
    Books
    Felice C. Frankel and George M. Whitesides

    No Small Matter: Science on the Nanoscale

  • November 1, 2020

    School of Engineering Communication Lab

    Principal Investigator Diana Chien

  • Publication date: August 5, 2011
    Books
    Stefan Scholtes, Prof. Richard L de Neufville

    Flexibility in Engineering Design

  • AI in LIfe Science 2018 - Richard Braatz

    December 4, 2018Conference Video Duration: 28:22

    Robust Data Analytics in Biopharmaceutical Manufacturing

    Although process data analytics is a valuable tool for improving the manufacturing of biologic drugs, selection of the best method requires a substantial level of expertise. This talk describes a robust and automated approach for process data analytics tool selection that allows the user to focus on goals rather than methods. The approach first applies tools to automatically interrogate the data to ascertain its characteristics, e.g., nonlinearity, correlation, dynamics. This information is then used to select a best-in-class process data analytics tool. The approach is demonstrated for industrial data for the manufacturing of a monoclonal antibody.

    2018 MIT AI in Life Sciences and Healthcare Conference

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