MIT Schwarzman College of Computing

🧬 AI for a fuller picture of the cell A new #AI framework identifies which cellular signals are unique to a measurement method — and which are shared across modalities. The approach helps researchers better understand cell states and disease mechanisms. bit.ly/4ufvuV0

Reducing emissions — and parking frustration 🚗♻️ MIT LIDS researchers developed a parking-aware navigation system that guides drivers to lots balancing proximity + availability. The result? Up to 35 minutes saved and more accurate travel time estimates. bit.ly/3P5QYne

Can personalization make LLMs too agreeable? Research from @MITLIDS Shomik Jain, Charlotte Park, and PI Ashia Wilson finds that long-term conversations can cause LLMs to mirror users’ views — potentially reducing accuracy & reinforcing echo chambers. More: bit.ly/3OqbC1b

MIT research finds platforms ranking the latest #LLMs can be surprisingly fragile — removing just a handful of crowdsourced votes can flip which model tops the leaderboard. The work underscores a need for more rigorous strategies to evaluate model rankings bit.ly/4csuBC8

Brian Hedden PhD ’12, @MITphilosophy professor holding an MIT Schwarzman College of Computing shared position with @MITEECS, has been appointed co-associate dean of the Social and Ethical Responsibilities of Computing (SERC). news.mit.edu/2026/brian-hed…

Where the ocean and atmosphere communicate 🌊🌬️ In a recent profile, LIDS PI @abigailbodner explores how ocean turbulence shapes climate patterns—and how math, Earth science, and AI come together in her research. Read the profile in MIT Spectrum: bit.ly/4qWJv8d

A new MIT interdisciplinary course, 6.S044/24.S00 (AI and Rationality), asks what it really means for an AI to be “rational.” Blending computer science and philosophy, students explore rational agency, decision-making, and how we define intelligence. news.mit.edu/2026/philosoph…

Whose job is it to tell what's real--and what's AI slop? @WGBH hosts a discussion with EECS professor David @karger: wgbh.org/news/local/202…

A new cross-disciplinary course, 6.S044/24.S00 (AI and Rationality), challenges students to explore an artificial system's potential to be rational. Watch senior Amanda Paredes Rioboo discuss the course. @MITEECS @MIT_SCC @MITphilosophy

Moving beyond overly aggregated #ML metrics. New research from LIDS postdoc Olawale Salaudeen, PI Marzyeh Ghassemi, and collaborators reveals how commonly used, highly aggregated ML metrics can hide serious failures — and offers a method to correct them. bit.ly/4k5LMLE

Common Ground offers classes that blend computing with other fields students are studying — so you learn to use computational tools in your major, not in isolation. Look for the “.C” in course numbers to spot them. Browse spring term classes at: computing.mit.edu/common-ground-…

AI will transform every sector of human society in fundamental ways. Is there a case to be made for how it can be a force for good? Tell us what you think the future holds. Over $20K in prizes will be awarded. Open to MIT students only. Submit by 2/8/2026. computing.mit.edu/FutureComputin…

#AI is often criticized for its growing energy use—but it can also help modernize the power grid. In a 3Q with MIT News, LIDS PI Priya Donti explains how AI can boost grid efficiency, improve resilience to extreme weather, and enable more renewable energy. bit.ly/4pGrwkM

MIT researchers built an algorithm that turns any 3D design into a flat grid that deploys itself with a single pull—no motors or assembly. Inspired by kirigami, it could enable foldable robots, medical devices, shelters, and space structures. news.mit.edu/2025/one-strin…

New prognostic tool could help clinicians identify high-risk lymphoma patients earlier—and tailor treatment to improve survival. MIT LIDS researchers show how early relapse strongly predicts outcomes using Synthetic Survival Controls. Published in Blood. bit.ly/45zomYY

Expand your horizons during MIT's January Independent Activities Period. Take part in bootcamps to build hands-on computational skills and engage in a series of workshops exploring key topics in computing and artificial intelligence. computing.mit.edu/IAP2026

A smarter way for LLMs to reason 🧠 MIT LIDS researchers developed a method that lets LLMs dynamically adjust how much computation they use based on a question’s difficulty—boosting efficiency without sacrificing performance. bit.ly/4p00Knf

Researchers from MIT and elsewhere developed an AI-driven robotic assembly system that can turn text or voice prompts into real physical objects like chairs and shelves. news.mit.edu/2025/robot-mak…

📊 Smooth sailing for spatial data. Researchers at MIT LIDS have developed a new method that improves the reliability of statistical estimates when data vary across space. 🔗 Read more: bit.ly/4pBXpMp 1/3

Learning the wrong lessons. MIT LIDS researchers found that LLMs sometimes copy sentence patterns instead of reasoning — leading to nonsensical answers. A question with the structure of “Where is Paris located?” can trigger “France,” even for gibberish. bit.ly/48Kgbdo