MIT Computer Science

• Explained: Generative AI’s environmental impact

  Rapid development and deployment of powerful generative AI models comes with environmental consequences, including increased electricity demand and water consumption.

• Algorithms and AI for a better world

  Assistant Professor Manish Raghavan wants computational techniques to help solve societal problems.

• Karl Berggren named faculty head of electrical engineering in EECS

  Professor who develops technologies to push the envelope of what is possible with photonics and electronic devices succeeds Joel Voldman.

• Fast control methods enable record-setting fidelity in superconducting qubit

  The advance holds the promise to reduce error-correction resource overhead.

• New computational chemistry techniques accelerate the prediction of molecules and materials

  With their recently-developed neural network architecture, MIT researchers can wring more information out of electronic structure calculations.

• Q&A: The climate impact of generative AI

  As the use of generative AI continues to grow, Lincoln Laboratory's Vijay Gadepally describes what researchers and consumers can do to help mitigate its environmental impact.

• Teaching AI to communicate sounds like humans do

  Inspired by the human vocal tract, a new AI model can produce and understand vocal imitations of everyday sounds. The method could help build new sonic interfaces for entertainment and education.

• Images that transform through heat

  The Thermochromorph printmaking technique developed by CSAIL researchers allows images to transition into each other through changes in temperature.

• Ecologists find computer vision models’ blind spots in retrieving wildlife images

  Biodiversity researchers tested vision systems on how well they could retrieve relevant nature images. More advanced models performed well on simple queries but struggled with more research-specific prompts.

• MIT affiliates receive 2025 IEEE honors

  Five MIT faculty and staff, along with 19 additional alumni, are honored for electrical engineering and computer science advances.

• Need a research hypothesis? Ask AI.

  MIT engineers developed AI frameworks to identify evidence-driven hypotheses that could advance biologically inspired materials.

• MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures

  With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.

• Study reveals AI chatbots can detect race, but racial bias reduces response empathy

  Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.

• Teaching a robot its limits, to complete open-ended tasks safely

  The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore requests.

• AI in health should be regulated, but don’t forget about the algorithms, researchers say

  In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.

• Researchers reduce bias in AI models while preserving or improving accuracy

  A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.

• Study: Some language reward models exhibit political bias

  Research from the MIT Center for Constructive Communication finds this effect occurs even when reward models are trained on factual data.

• Enabling AI to explain its predictions in plain language

  Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.

• Daniela Rus wins John Scott Award

  MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.

• Citation tool offers a new approach to trustworthy AI-generated content

  Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.

• A new way to create realistic 3D shapes using generative AI

  Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.

• Photonic processor could enable ultrafast AI computations with extreme energy efficiency

  This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.

• Improving health, one machine learning system at a time

  Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.

• MIT researchers develop an efficient way to train more reliable AI agents

  The technique could make AI systems better at complex tasks that involve variability.

• Advancing urban tree monitoring with AI-powered digital twins

  The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.

• Can robots learn from machine dreams?

  MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI's potential for creating robotics training data.

• Four from MIT named 2025 Rhodes Scholars

  Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.

• Graph-based AI model maps the future of innovation

  An AI method developed by Professor Markus Buehler finds hidden links between science and art to suggest novel materials.

• A causal theory for studying the cause-and-effect relationships of genes

  By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.

• A portable light system that can digitize everyday objects

  A new design tool uses UV and RGB lights to change the color and textures of everyday objects. The system could enable surfaces to display dynamic patterns, such as health data and fashion designs.