MIT Computer Science

• Researchers teach LLMs to solve complex planning challenges

  This new framework leverages a model’s reasoning abilities to create a “smart assistant” that finds the optimal solution to multistep problems.

• For this computer scientist, MIT Open Learning was the start of a life-changing journey

  Ana Trišović, who studies the democratization of AI, reflects on a career path that she began as a student downloading free MIT resources in Serbia.

• A new way to make graphs more accessible to blind and low-vision readers

  The Tactile Vega-Lite system, developed at MIT CSAIL, streamlines the tactile chart design process; could help educators efficiently create these graphics and aid designers in making precise changes.

• Device enables direct communication among multiple quantum processors

  MIT researchers developed a photon-shuttling “interconnect” that can facilitate remote entanglement, a key step toward a practical quantum computer.

• AI tool generates high-quality images faster than state-of-the-art approaches

  Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.

• At the core of problem-solving

  Stuart Levine ’97, director of MIT’s BioMicro Center, keeps departmental researchers at the forefront of systems biology.

• “An AI future that honors dignity for everyone”

  As artificial intelligence develops, we must ask vital questions about ourselves and our society, Ben Vinson III contends in the 2025 Compton Lecture.

• 3D printing approach strings together dynamic objects for you

  “Xstrings” method enables users to produce cable-driven objects, automatically assembling bionic robots, sculptures, and dynamic fashion designs.

• High-performance computing, with much less code

  The Exo 2 programming language enables reusable scheduling libraries external to compilers.

• QS World University Rankings rates MIT No. 1 in 11 subjects for 2025

  The Institute also ranks second in seven subject areas.

• Robotic helper making mistakes? Just nudge it in the right direction

  New research could allow a person to correct a robot’s actions in real-time, using the kind of feedback they’d give another human.

• A leg up for STEM majors

  MIT undergraduates broaden their perspectives and prospects through political science.

• AI system predicts protein fragments that can bind to or inhibit a target

  FragFold, developed by MIT Biology researchers, is a computational method with potential for impact on biological research and therapeutic applications.

• Like human brains, large language models reason about diverse data in a general way

  A new study shows LLMs represent different data types based on their underlying meaning and reason about data in their dominant language.

• AI model deciphers the code in proteins that tells them where to go

  Whitehead Institute and CSAIL researchers created a machine-learning model to predict and generate protein localization, with implications for understanding and remedying disease.

• Gift from Sebastian Man ’79, SM ’80 supports MIT Stephen A. Schwarzman College of Computing building

  Alumnus is the first major donor to support the building since Stephen A. Schwarzman’s foundational gift.

• Bridging philosophy and AI to explore computing ethics

  In a new MIT course co-taught by EECS and philosophy professors, students tackle moral dilemmas of the digital age.

• To keep hardware safe, cut out the code’s clues

  New “Oreo” method from MIT CSAIL researchers removes footprints that reveal where code is stored before a hacker can see them.

• Puzzling out climate change

  Accenture Fellow Shreyaa Raghavan applies machine learning and optimization methods to explore ways to reduce transportation sector emissions.

• Can deep learning transform heart failure prevention?

  A deep neural network called CHAIS may soon replace invasive procedures like catheterization as the new gold standard for monitoring heart health.

• Creating a common language

  New faculty member Kaiming He discusses AI’s role in lowering barriers between scientific fields and fostering collaboration across scientific disciplines.

• Validation technique could help scientists make more accurate forecasts

  MIT researchers developed a new approach for assessing predictions with a spatial dimension, like forecasting weather or mapping air pollution.

• Eleven MIT faculty receive Presidential Early Career Awards

  Faculty members and additional MIT alumni are among 400 scientists and engineers recognized for outstanding leadership potential.

• Introducing the MIT Generative AI Impact Consortium

  The consortium will bring researchers and industry together to focus on impact.

• User-friendly system can help developers build more efficient simulations and AI models

  By automatically generating code that leverages two types of data redundancy, the system saves bandwidth, memory, and computation.

• With generative AI, MIT chemists quickly calculate 3D genomic structures

  A new approach, which takes minutes rather than days, predicts how a specific DNA sequence will arrange itself in the cell nucleus.

• 3 Questions: Modeling adversarial intelligence to exploit AI’s security vulnerabilities

  MIT CSAIL Principal Research Scientist Una-May O’Reilly discusses how she develops agents that reveal AI models’ security weaknesses before hackers do.

• New training approach could help AI agents perform better in uncertain conditions

  Sometimes, it might be better to train a robot in an environment that’s different from the one where it will be deployed.

• Toward video generative models of the molecular world

  Starting with a single frame in a simulation, a new system uses generative AI to emulate the dynamics of molecules, connecting static molecular structures and developing blurry pictures into videos.

• 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.