This collection of MIT Theses in DSpace contains selected theses and dissertations from all MIT departments. Please note that this is NOT a complete collection of MIT theses. To search all MIT theses, use MIT Libraries' catalog.

MIT's DSpace contains more than 58,000 theses completed at MIT dating as far back as the mid 1800's. Theses in this collection have been scanned by the MIT Libraries or submitted in electronic format by thesis authors. Since 2004 all new Masters and Ph.D. theses are scanned and added to this collection after degrees are awarded.

MIT Theses are openly available to all readers. Please share how this access affects or benefits you. Your story matters.

Contact

If you have questions about MIT theses in DSpace, mit-theses@mit.edu. See also Access & Availability Questions or About MIT Theses in DSpace.

If you are a recent MIT graduate, your thesis will be added to DSpace within 3-6 months after your graduation date. Please email mit-theses@mit.edu with any questions.

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MIT Theses may be protected by copyright. Please refer to the MIT Libraries Permissions Policy for permission information. Note that the copyright holder for most MIT theses is identified on the title page of the thesis.

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Recent Submissions

  • Additive Manufacturing of Electrical Machines and Electronic Devices 

    Cañada Pérez-Sala, Jorge (Massachusetts Institute of Technology, 2025-05)
    Recent advancements in the additive manufacture of electronics and electrical machines have led to successful demonstrations of 3D-printed passive (e.g., resistors, capacitors, inductors) and active (e.g., transistors) ...
  • A Unified Theory of Representation Learning: How Hidden Relationships Power Algorithms that can Learn without Labels 

    Hamilton, Mark T. (Massachusetts Institute of Technology, 2025-05)
    How does the human mind make sense of raw information without being taught how to see or hear? This thesis presents a unifying theory that describes how algorithms can learn and discover structure in complex systems, like ...
  • Score Estimation for Generative Modeling 

    Jayashankar, Tejas Kumar (Massachusetts Institute of Technology, 2025-05)
    Recent advances in score-based (diffusion) generative models have achieved state-of-the-art sample quality across standard benchmarks. Building on the remarkable property of these models in estimating scores, this thesis ...

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