@MITLIDS
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MIT LIDS retweeted
Thinking about adding machine learning to your engineering toolkit? Start by hearing from the person who teaches it.
On Wednesday, September 9 at 12pm ET, MIT professor Youssef Marzouk will walk through MIT xPRO's machine learning, modeling, and simulation: engineering problem-solving in the age of Ai. You'll learn what to expect from the program and get to ask the course team directly.
Register now: bit.ly/3UD0lxE
🗳️ What is AI telling voters about elections?
MIT’s new LLM Election Observatory, from LIDS PI @charapod and collaborators, is tracking how major AI models answer questions about candidates and issues — and how those answers evolve throughout the midterms.
bit.ly/4dwYpx7
When it comes to AI-assisted medical diagnosis, one size may not fit all.
A new study from @MarzyehGhassemi and team found that AI explanations affect clinicians and non-experts differently—highlighting the need to design AI support around user expertise.
bit.ly/4gv0jR7
MIT LIDS retweeted
📣 Planning to submit our #NeurIPS2026 workshop? we are using OpenReview this year, & account creation can take up to 2 weeks. We recommend creating your OpenReview account by Aug 15 to ensure you can submit on time for the Aug 29 deadline.
👉Learn more: climatechange.ai/events/neur…
MIT LIDS retweeted
Hiring a postdoc at @MIT 🚨
Compositional design, applied category theory, optimization → autonomy stacks, control architectures, multi-agent systems, agentic AI, mobility & energy networks.
Rolling review, first pass Sept 15 👉
MIT LIDS retweeted
The 2026 volume of the Annual Review of Economics is now online. Take a look at the most read article, "Large Language Models: An Applied Econometric Framework" by @jensottoludwig, @m_sendhil, and @asheshrambachan
bit.ly/4wI35an
MIT LIDS retweeted
MIT Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power. news.mit.edu/2026/new-chip-c…
Inspired by pinecones, tree bark, and seedpods, MIT researchers developed a mathematical framework for designing manufacturable, adaptive materials that mimic complex behaviors found in nature — with applications from robotics to aerospace. bit.ly/4zr0VOK
MIT LIDS retweeted
Deep learning runs on math, but we've never had a formal language for architectures!
Now we do. Our new work is out on @TmlrOrg , led by @Vincent Abbott 🎉
arxiv.org/pdf/2604.07242
Using reason, again and again
A profile explores the work of LIDS PI Brian Hedden, whose research connects ethics and computing. From "Time-Slice Rationality" to MIT's SERC initiative, Hedden is helping shape a more thoughtful future for AI and computing. bit.ly/4wadCuy
MIT LIDS retweeted
Ashia Wilson, a professor in @MITEECS, is a recipient of the 2026 Junior Bose Award. The award is given annually to an outstanding contributor to education.
📸: Conor McArdle
engineering.mit.edu/news/ash…
MIT LIDS PI Dimitri Bertsekas, whose groundbreaking work shaped optimization, control, reinforcement learning, and AI, has passed away at 83. A prolific author, educator, and mentor, his impact will continue through generations of students and researchers. bit.ly/3Rg1fip
Following questions where they lead. A new profile highlights Bailey Flanigan's interdisciplinary journey—from medicine and economics to computer science and political science. Today, she develops computational tools to strengthen democratic participation: bit.ly/452IlyR
ALT A portrait of Bailey Flanigan
A faster way to sample from #AI diffusion models from @MITLIDS @FanChen0111, PI @rakhlin, with @MIT_CSAIL PI @KonstDaskalakis and Sinho Chewi. Read the paper that won the Outstanding Paper Award at #icml2026 @icmlconf
MIT researchers developed a faster way to sample from AI diffusion models.
They propose a new algorithm reaching the same level of accuracy in exponentially fewer steps, suggesting a path to much more efficient image generation & other diffusion-based AI systems. It recently won Outstanding Paper at ICML: bit.ly/450rpbZ
MIT LIDS retweeted
🚢🌊 Our latest paper introduces ORCA (Observation-informed Real-time Correction with Attention), a transformer for correcting numerical weather prediction (NWP) over the ocean.
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