MIT Computer Science

• Researchers discover a shortcoming that makes LLMs less reliable

  Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.

• MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

  BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.

• The cost of thinking

  MIT neuroscientists find a surprising parallel in the ways humans and new AI models solve complex problems.

• Bigger datasets aren’t always better

  MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.

• Understanding the nuances of human-like intelligence

  Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.

• Leading quantum at an inflection point

  The MIT Quantum Initiative is taking shape, leveraging quantum breakthroughs to drive the future of scientific and technological progress.

• Charting the future of AI, from safer answers to faster thinking

  MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.

• MIT researchers propose a new model for legible, modular software

  The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.

• Teaching robots to map large environments

  A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.

• 3 Questions: How AI is helping us monitor and support vulnerable ecosystems

  MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.

• A faster problem-solving tool that guarantees feasibility

  The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.

• The brain power behind sustainable AI

  PhD student Miranda Schwacke explores how computing inspired by the human brain can fuel energy-efficient artificial intelligence.

• Charts can be social artifacts that communicate more than just data

  Researchers find that design elements of data visualizations influence viewers’ assumptions about the source of the information and its trustworthiness.

• Creating AI that matters

  How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.

• Solar energy startup Active Surfaces wins inaugural PITCH.nano competition

  Twelve START.nano companies competed for the grand prize of nanoBucks to be used at MIT.nano’s facilities.

• New software designs eco-friendly clothing that can reassemble into new items

  To reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.

• Method teaches generative AI models to locate personalized objects

  After being trained with this technique, vision-language models can better identify a unique item in a new scene.

• MIT Schwarzman College of Computing and MBZUAI launch international collaboration to shape the future of AI

  The MIT–MBZUAI Collaborative Research Program will unite faculty and students from both institutions to advance AI and accelerate its use in pressing scientific and societal challenges.

• Using generative AI to diversify virtual training grounds for robots

  New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.

• Laurent Demanet appointed co-director of MIT Center for Computational Science and Engineering

  Applied mathematics professor will join fellow co-director Nicolas Hadjiconstantinou in leading the cross-cutting center.

• Fighting for the health of the planet with AI

  Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.

• AI maps how a new antibiotic targets gut bacteria

  MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.

• System lets people personalize online social spaces while staying connected with others

  By enabling users to easily create social apps that serve communities’ needs, the Graffiti framework aims to promote healthier online interactions.

• Responding to the climate impact of generative AI

  Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.

• New AI system could accelerate clinical research

  By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.

• MIT engineers develop a magnetic transistor for more energy-efficient electronics

  A new device concept opens the door to compact, high-performance transistors with built-in memory.

• MIT named No. 2 university by U.S. News for 2025-26

  Undergraduate engineering, computer science, and business programs are all No. 1.

• MIT affiliates win AI for Math grants to accelerate mathematical discovery

  Department of Mathematics researchers David Roe and Andrew Sutherland seek to advance automated theorem proving; four additional MIT alumni also awarded.

• What does the future hold for generative AI?

  At the inaugural MIT Generative AI Impact Consortium Symposium, researchers and business leaders discussed potential advancements centered on this powerful technology.

• A new community for computational science and engineering

  The stand-alone PhD program is building connections and preparing students to make a difference.