MIT Schwarzman College of Computing

Rachel Sava, a PhD student in @mit_hst, explores transformative improvements and dystopian risks of neural technology in her winning piece for the fourth annual Envisioning the Future of Computing Prize. news.mit.edu/2026/toward-fu…

Could AI help you find your lost keys? @MITLIDS researchers developed Describe Anything Anywhere At Any Moment (DAAAM) a long-term memory framework that enables robots to remember objects & answer questions about their surroundings using natural language. bit.ly/4vAv780

Helping tiny robots navigate tight spaces RLE & LIDS researchers developed a new low-power chip that helps small robots to rapidly generate 3D maps, avoid obstacles, and navigate complex environments like HVAC systems for inspection and gas-leak detection. bit.ly/3QZVEfO/CBNBF6

The “power of three” may be key to understanding human preferences. LIDS @gabrfarina, Sobhan Mohammadpour, and collaborators show that two-way comparisons can't reveal preference correlations—but rankings of three alternatives can. A major advance in RUMs: bit.ly/4oxAhPu

The MIT Ethics of Computing Research Symposium - hosted by SERC - gathered experts and researchers working at the heart of ethical and social impact in technology. news.mit.edu/2026/crucial-h… @MITPoliSci @MIT_SCC

Among all of the possible chemical compounds, it’s estimated that between 1020 and 1060 may hold potential as small-molecule drugs. Evaluating each of those compounds experimentally would be far too time-consuming for chemists. So, in recent years, MIT Associate Professor Connor Coley PhD ’19 has been using artificial intelligence to help identify compounds that could make good drug candidates. news.mit.edu/2026/building-…

Games people — and machines — play 🎲 LIDS PI @gabrfarina combines concepts from game theory with such tools as machine learning, optimization, and statistics to advance theoretical and algorithmic foundations for decision-making. Read a profile: bit.ly/4nm4X5G

MIT and IBM will expand our collaboration: The MIT-IBM Computing Research Lab will seek to leverage hybrid quantum and classical computing, with advanced algorithms and AI, to solve challenging problems in fundamentally new ways. news.mit.edu/2026/mit-ibm-c…

Before autonomous vehicles scale, the science behind them needs an upgrade. Researchers from @MITLIDS, @MIT_CEE, @MITdusp, and collaborators find mobility-on-demand studies often lack transparency and reproducibility—key for real-world decisions. More: bit.ly/4cCrAig

Researchers at @MIT_CSAIL developed an intelligent system for balancing the tasks of storage devices inside a data center, which can extend the longevity of storage hardware and help a data center operate more efficiently. news.mit.edu/2026/helping-d…

Evaluating the ethics of autonomous systems A new framework from @MITLIDS, @MITAeroAstro & @Saab––SEED-SET—can pinpoint when #AI decision-support systems make unfair recommendations in complex settings like power grids. More: bit.ly/41dEFbr

For the 3rd year, Expanding Horizons in Computing brought together MIT faculty and guests to explore key questions in computing & AI — from human intelligence to coding to the future of education. Recordings from the workshops are now available to watch🎥 bit.ly/2026ExpandingH…

April 30: The MIT Ethics of Computing Research Symposium will feature MIT faculty presentations, a keynote by Jon Kleinberg (Cornell), and panels on AI alignment and the future of education with participants from OpenAI and Google DeepMind. Register: bit.ly/SERCSymposium2…

Call for applications: Join us this summer for a weeklong workshop to gain hands-on experience in modern concepts of AI, their applications to modeling, and strategies for classroom implementation. Open to faculty at institutions nationwide. Learn more: computing.mit.edu/ai-educators-p…

Smarter traffic control for warehouse robots 🤖 @MITLIDS + Symbotic researchers developed an #AI system that learns how congestion forms and decides which robots should go first—rerouting them in advance to avoid bottlenecks. More: bit.ly/4lWdawE

Spotting overconfident #LLMs New work from LIDS Kimia Hamidieh, Marzyeh Ghassemi, and collaborators introduces a more reliable uncertainty metric to flag when models are confident but wrong. This could help users better judge when to trust #AI outputs. bit.ly/4sobRZN

🧬 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