MIT Computer Science

• A human-centered approach to data visualization

  Balancing automation and agency, Associate Professor Arvind Satyanarayan develops interactive data visualizations that amplify human creativity and cognition.

• A greener way to 3D print stronger stuff

  MIT CSAIL researchers developed SustainaPrint, a system that reinforces only the weakest zones of eco-friendly 3D prints, achieving strong results with less plastic.

• A new generative AI approach to predicting chemical reactions

  System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.

• 3 Questions: The pros and cons of synthetic data in AI

  Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.

• MIT researchers develop AI tool to improve flu vaccine strain selection

  VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.

• Learning from punishment

  A new computational model makes sense of the cognitive processes humans use to evaluate punishment.

• A new model predicts how molecules will dissolve in different solvents

  Solubility predictions could make it easier to design and synthesize new drugs, while minimizing the use of more hazardous solvents.

• A shape-changing antenna for more versatile sensing and communication

  You can adjust the frequency range of this durable, inexpensive antenna by squeezing or stretching its structure.

• Eco-driving measures could significantly reduce vehicle emissions

  New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.

• Helping data storage keep up with the AI revolution

  Storage systems from Cloudian, co-founded by an MIT alumnus, are helping businesses feed data-hungry AI models and agents at scale.

• AI helps chemists develop tougher plastics

  Researchers created polymers that are more resistant to tearing by incorporating stress-responsive molecules identified by a machine-learning model.

• MIT tool visualizes and edits “physically impossible” objects

  By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.

• New algorithms enable efficient machine learning with symmetric data

  This new approach could lead to enhanced AI models for drug and materials discovery.

• New system dramatically speeds the search for polymer materials

  The platform identifies, mixes, and tests up to 700 new polymer blends a day for applications like protein stabilization, battery electrolytes, or drug-delivery materials.

• Robot, know thyself: New vision-based system teaches machines to understand their bodies

  Neural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.

• A new way to edit or generate images

  MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.

• The unique, mathematical shortcuts language models use to predict dynamic scenarios

  Language models follow changing situations using clever arithmetic, instead of sequential tracking. By controlling when these approaches are used, engineers could improve the systems’ capabilities.

• This “smart coach” helps LLMs switch between text and code

  The CodeSteer system could boost large language models’ accuracy when solving complex problems, such as scheduling shipments in a supply chain.

• Can AI really code? Study maps the roadblocks to autonomous software engineering

  A team of researchers has mapped the challenges of AI in software development, and outlined a research agenda to move the field forward.

• How to more efficiently study complex treatment interactions

  A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.

• Simulation-based pipeline tailors training data for dexterous robots

  The PhysicsGen system, developed by MIT researchers, helps robots handle items in homes and factories by tailoring training data to a particular machine.

• AI shapes autonomous underwater “gliders”

  An AI pipeline developed by CSAIL researchers enables unique hydrodynamic designs for bodyboard-sized vehicles that glide underwater and could help scientists gather marine data.

• At MIT, musicians make new tools for new tunes

  From the classroom to expanding research opportunities, students at MIT Music Technology use design to push the frontier of digital instruments and software for human expression and empowerment.

• Study could lead to LLMs that are better at complex reasoning

  Researchers developed a way to make large language models more adaptable to challenging tasks like strategic planning or process optimization.

• Robotic probe quickly measures key properties of new materials

  Developed to analyze new semiconductors, the system could streamline the development of more powerful solar panels.

• MIT and Mass General Hospital researchers find disparities in organ acceptance

  In an analysis of over 160,000 transplant candidates, researchers found that race is linked to how likely an organ offer is to be accepted on behalf of a patient.

• New imaging technique reconstructs the shapes of hidden objects

  By leveraging reflections from wireless signals like Wi-Fi, the system could allow robots to find and manipulate items that are blocked from view.

• Using generative AI to help robots jump higher and land safely

  MIT CSAIL researchers combined GenAI and a physics simulation engine to refine robot designs. The result: a machine that out-jumped a robot designed by humans.

• LLMs factor in unrelated information when recommending medical treatments

  Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.

• Researchers present bold ideas for AI at MIT Generative AI Impact Consortium kickoff event

  Presentations targeted high-impact intersections of AI and other areas, such as health care, business, and education.