Michael P. Brenner

About

Michael P. Brenner is a Catalyst Professor of Applied Mathematics, Applied Physics, and Physics at Harvard University, affiliated with the Harvard John A. Paulson School of Engineering and Applied Sciences. He serves as the Area Chair for Applied Mathematics and is a Kavli Scholar at the Kavli Institute for Bionano Science & Technology. His research areas include applied mathematics, fluid mechanics, modeling physical and biological phenomena and systems, artificial intelligence, science and engineering for climate technology, applied physics, soft matter science, bioengineering, biomechanics, computer science, computational and data science, electrical engineering, environmental science and engineering, materials science, and mechanical engineering. Brenner's work involves applying computational frameworks, physics-based machine learning algorithms, and modeling techniques to biomolecular design, cellular organization, and other complex physical and biological systems, contributing to advancements in scientific research and engineering.

Research topics

• Computer Science
• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• Psychology
• Communication
• Linguistics
• Econometrics
• Mathematics
• Speech recognition

Selected publications

• Shared computational principles for language processing in humans and deep language models

  Nature Neuroscience · 2022 · 442 citations

  Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models generate appropriate linguistic responses in a given context. In the current study, nine participants listened to a 30-min podcast while their brain responses were recorded using electrocorticography (ECoG). We provide empirical evidence that the human brain and autoregressiv…

  PublisherOA PDFDOI

• The United States COVID-19 Forecast Hub dataset

  Scientific Data · 2022 · 126 citations

  Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hosp…

  DOI

• Thinking ahead: spontaneous prediction in context as a keystone of language in humans and machines

  bioRxiv (Cold Spring Harbor Laboratory) · 2020 · 52 citations

  Abstract Departing from traditional linguistic models, advances in deep learning have resulted in a new type of predictive (autoregressive) deep language models (DLMs). Using a self-supervised next-word prediction task, these models are trained to generate appropriate linguistic responses in a given context. We provide empirical evidence that the human brain and autoregressive DLMs share three fundamental computational principles as they process natural language: 1) both are engaged in continuou…

  PublisherOA PDFDOI

• Mapping the ionosphere with millions of phones

  Nature · 2024-11-13 · 32 citations

  articleOpen access

  Abstract The ionosphere is a layer of weakly ionized plasma bathed in Earth’s geomagnetic field extending about 50–1,500 kilometres above Earth 1 . The ionospheric total electron content varies in response to Earth’s space environment, interfering with Global Satellite Navigation System (GNSS) signals, resulting in one of the largest sources of error for position, navigation and timing services 2 . Networks of high-quality ground-based GNSS stations provide maps of ionospheric total electron con…

  PublisherOA PDFDOI

• A unified acoustic-to-speech-to-language embedding space captures the neural basis of natural language processing in everyday conversations

  Nature Human Behaviour · 2025-03-07 · 28 citations

  articleOpen access

  This study introduces a unified computational framework connecting acoustic, speech and word-level linguistic structures to study the neural basis of everyday conversations in the human brain. We used electrocorticography to record neural signals across 100 h of speech production and comprehension as participants engaged in open-ended real-life conversations. We extracted low-level acoustic, mid-level speech and contextual word embeddings from a multimodal speech-to-text model (Whisper). We deve…

  PublisherOA PDFDOI

Recent grants

• DMREF: Self Assembly with DNA-Labeled Colloidal Particles and DNA Nanostructures

  NSF · $1.5M · 2014–2019

• Research and Education in Physical Mathematics

  NSF · $288k · 2006–2010

• Research and Education in Physical Mathematics

  NSF · $399k · 2014–2019

Frequent coauthors

• Carl P. Goodrich

  Institute of Science and Technology Austria

  35 shared

• David A. Weitz

  Harvard University

  32 shared

• Alain Pumir

  École Normale Supérieure de Lyon

  31 shared

• Detlef Lohse

  Max Planck University of Twente Center for Complex Fluid Dynamics

  28 shared

• Lucy J. Colwell

  University of Cambridge

  27 shared

• Vinothan Manoharan

  Harvard University

  25 shared

• Bobbi Aubrey

  Princeton University

  23 shared

• Ofer Kimchi

  Princeton University

  22 shared

Labs

• Michael P. Brenner LabPI

Similar researchers at Harvard University

• Federico Capassoh-143
• Katia Bertoldih-82
• Michael J. Azizh-72