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3296 search results found
  • August 5, 2021 MIT News

    Gerald Wogan, professor emeritus of biological engineering, chemistry, and toxicology, dies at 91

  • October 13, 2021 MIT News

    Thriving Stars: An initiative to improve gender representation in electrical engineering and computer science

  • August 13, 2021 MIT News

    Katie Galloway receives NIH award to develop foundational tools for cellular engineering

  • 2024 MIT R&D Conference: Track 6 - Quantum 2.0 - Compiling Machine Intelligence onto (Quantum) Optoelectronic Systems

    November 19, 2024Conference Video Duration: 19:7
    Compiling Machine Intelligence onto (Quantum) Optoelectronic Systems
    Dirk Englund
    Associate Professor, MIT Department of Electrical Engineering and Computer Science

    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.

  • January 22, 2019

    An Energy-Efficient Reconfigurable DTLS Cryptographic Engine for End-to-End Security in IoT Applications

  • 2024 MIT R&D Conference: Track 5 - AI - Efficient Multi-modal LLM on the Edge

    November 19, 2024Conference Video Duration: 20:30
    Efficient Multi-modal LLM on the Edge
    Song Han
    Associate Professor, MIT Electrical Engineering & Computer Science Department
    This talk presents efficient multi-modal LLM innovations with algorithm and system co-design. I’ll first present VILA, a visual language model deployable on the edge. It is capable of visual in-context learning, multi-image reasoning, video captioning and video QA. Followed by SmoothQuant and AWQ for LLM quantization, which enables VILA deployable on edge devices, bringing new capabilities for mobile vision applications. Second, I’ll present StreamingLLM, a KV cache optimization technique for long conversation and QUEST, leveraging sparsity for KV cache compression.
  • RD-11.15-16.2022-Panel-Fact-Fiction

    November 16, 2022Conference Video Duration: 35:55

    Brian Anthony
    Faculty Lead, Industry Immersion Program in Mechanical Engineering
    Co-Director, MIT Clinical Research Center
    Associate Director, MIT.nano
    Duane Boning
    Clarence J. LeBel Professor in Electrical Engineering, Department of Electrical Engineering and Computer Science
    Erik v Head of Product & EcosystemTulip
    Steven Moskowitz
    Director of Digital Transformation, Entegris

  • 2024 MIT Digital Technology and Strategy Conference: Scaling Digital Production

    September 17, 2024Conference Video Duration: 38:27
    Scaling Digital Production
  • 2024 MIT R&D Conference: Track 4 - Healthcare - Machine-Learning-Guided Quality Control of CAR-T Therapy Product Using Microfluidic Biophysical Cytometry

    November 19, 2024Conference Video Duration: 18:44
    Machine-Learning-Guided Quality Control of CAR-T Therapy Product Using Microfluidic Biophysical Cytometry
    Jongyoon Han
    Professor of Electrical Engineering, MIT Electrical Engineering & Computer Science Department
    Professor, MIT Biological Engineering

    Chimeric Antigen Receptor (CAR) T cell therapy has revolutionized cancer care, yet its manufacturing remains challenging due to variability in quality and efficacy. In this talk we introduce a novel microfluidic, label-free cellular biophysical profiling assay that rapidly assesses the functional phenotypes of CAR T cells. Our assay leverages biophysical features such as cell size and deformability to directly correlate with critical functional attributes, including the CD4:CD8 ratio, effector and central memory subtypes, and killing potency. Validated through extensive longitudinal studies across multiple CAR T batches from different donors and culture platforms, this method requires fewer than 10,000 cells and completes profiling within 10 minutes. The assay provides an efficient means to predict CAR T cell quality at critical manufacturing stages, thereby potentially reducing batch failure rates and enhancing therapeutic consistency.

  • 2017 Japan

    2017 MIT Japan Conference

    Thu, December 7, 2017 Conference
    Tokyo

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