MIT projects selected for funding under US Department of Energy’s Genesis Mission
Initial research projects advance national priorities across natural resources, manufacturing, nuclear physics, and more.
More from the blog
3 Questions: What is the best path forward for AI in academia?
MIT Statistics and Data Science Center Director Alexander (Sasha) Rakhlin shares important considerations for departments and institutions.
Using AI to mitigate the growing environmental threat of data centers
By rethinking how large cloud computing systems operate, Associate Professor Christina Delimitrou seeks to make data centers more energy efficient.
Discovering the value of humanistic inquiry
Students in MIT’s Concourse program delve deeply into the human condition, debate challenging questions, and learn to develop judgment about issues that can’t be quantified.
Chris Bourg named vice provost and Barbara K. Ostrom (1978) Director of the MIT Libraries
As director, Bourg has focused on digital access, open and equitable scholarly publishing, and expanded support for data-intensive research.
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Lean Internal Startups for Software Product Innovation in Large Companies: Enablers and Inhibitors
This study investigates how Lean internal startup facilitates software product innovation in large companies and identifies its enablers and inhibitors, and shows the potential of the method-in-action framework to investigate the Lean startup approach in non-startup context.
AI-powered Code Review with LLMs: Early Results
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Enhanced Sampling in the Age of Machine Learning: Algorithms and Applications
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
System for systematic literature review using multiple AI agents: Concept and an empirical evaluation
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.