As the Institute's first VP for energy and climate, Evelyn Wang '00 is marshaling MIT's expertise to meet the greatest challenge of our age. Professor Evelyn Wang '00 sits beside a compact, portable water-harvesting device that she developed in collaboration with Professor Rohit Karnik of MIT and Krista Walton, then a professor at Georgia Tech. It's designed for portable and emergency use. Water shortages in Southern California made an indelible impression on Evelyn Wang '00 when she was growing up in Los Angeles. "I was quite young, perhaps in first grade," she says. "But I remember we weren't allowed to turn our sprinklers on. And everyone in the neighborhood was given disinfectant tablets for the toilet and encouraged to keep flushing to a minimum. I didn't understand exactly what was happening. But I saw that everyone in the community was affected by the scarcity of this resource."

MIT astronauts aboard the International Space Station--and the MIT researchers who have sent up experiments--have advanced our understanding of science, space, and the universe. This image of the International Space Station and space shuttle Endeavour, flying at an altitude of approximately 350 kilometers, was taken by Expedition 27 crew member Paolo Nespoli from the Soyuz TMA-20 on May 24, 2011. On November 2, 2000, NASA astronaut Bill Shepherd, OCE '78, SM '78, and Russian cosmonauts Sergei Krikalev and Yuri Gidzenko made history as their Soyuz spacecraft docked with the International Space Station. The event marked the start of 25 years of continuous human presence in space aboard the ISS--a prolific period for space research. MIT-trained astronauts, scientists, and engineers have played integral roles in all aspects of the station's design, assembly, operations, and scientific research. One of MIT's most experienced NASA astronauts, Mike Fincke '89, is celebrating that milestone from space.

Encouraged early on by Nobel laureate Enrico Fermi, the "Queen of Carbon" laid the foundation for countless advances in nanotechnology--and mentored countless young scientists along the way. At MIT, Mildred Dresselhaus became a beloved professor who pushed her students to be their very best and provided support in ways big and small. Institute Professor Mildred "Millie" Dresselhaus forever altered our understanding of matter--the physical stuff of the universe that has mass and takes up space. Over 57 years at MIT, Dresselhaus also played a significant role in inspiring people to use this new knowledge to tackle some of the world's greatest challenges, from producing clean energy to curing cancer. Although she became an emerita professor in 2007, Dresselhaus, who taught electrical engineering and physics, remained actively involved in research and all other aspects of MIT life until her death in 2017. She would have been 95 this November.

But her passion is for paper--with no scissors. Today, she's a tessellation expert who teaches, invents new designs, and writes papers on the underlying math. Madonna Yoder '17 photographed in her studio Ross Mantle When Madonna Yoder '17 was eight years old, she learned how to fold a square piece of paper over and over and over again. After about 16 folds, she held a bird in her hands. The first time she pulled the tail of a flapping crane, she says, she realized: . That first piece was an origami classic, folded by kids at summer camp for generations and many people's first foray into the art form.

The AI Industry's Scaling Obsession Is Headed for a Cliff Huge AI infrastructure deals assume that algorithms will keep improving with scale. A new study from MIT suggests the biggest and most computationally intensive AI models may soon offer diminishing returns compared to smaller models. By mapping scaling laws against continued improvements in model efficiency, the researchers found that it could become harder to wring leaps in performance from giant models whereas efficiency gains could make models running on more modest hardware increasingly capable over the next decade. "In the next five to 10 years, things are very likely to start narrowing," says Neil Thompson, a computer scientist and professor at MIT involved in the study. Leaps in efficiency, like those seen with DeepSeek's remarkably low-cost model in January, have already served as a reality check for the AI industry, which is accustomed to burning massive amounts of compute.

Tree search has become as a representative framework for test-time reasoning with large language models (LLMs), exemplified by methods such as Tree-of-Thought and Monte Carlo Tree Search that explore multiple reasoning paths. However, it remains difficult to provide instant and reliable quantitative assessments of intermediate reasoning step quality, and extensive path exploration is computationally costly. To address this, we propose Mutual Information Tree Search (MITS), a novel framework that guides reasoning with information-theoretic principles. MITS introduces an effective scoring function based on pointwise mutual information (PMI), which enables step-wise evaluation of reasoning paths and search tree expansion via beam search without expensive look-ahead simulations, achieving superior reasoning performances while maintaining computational efficiency. The framework is complemented by an entropy-based dynamic sampling strategy that adaptively allocates computational resources to uncertain reasoning steps where exploration is most beneficial. For final prediction, MITS employs a weighted voting scheme that combines PMI scores with prediction consensus. Complex multi-step reasoning remains a fundamental challenge for Large Language Models (LLMs), particularly in tasks that require logical deduction, mathematical computation, or systematic problem-solving (Y ang et al., 2025a; Zhu et al., 2024; Yi et al., 2024). While Chain-of-Thought (CoT) prompting (Wei et al., 2022; Kojima et al., 2022) has emerged as a powerful technique to enhance reasoning by decomposing problems into intermediate steps, it typically generates a single reasoning path, which may lead to incorrect solutions due to error accumulation or the selection of suboptimal reasoning strategies. This limitation becomes particularly pronounced in complex reasoning tasks where multiple valid approaches exist, but only specific paths lead to correct answers.

In an era of increasing societal fragmentation, political polarization, and erosion of public trust in institutions, representative deliberative assemblies are emerging as a promising democratic forum for developing effective policy outcomes on complex global issues. Despite theoretical attention, there remains limited empirical work that systematically traces how specific ideas evolve, are prioritized, or are discarded during deliberation to form policy recommendations. Addressing these gaps, this work poses two central questions: (1) How might we trace the evolution and distillation of ideas into concrete recommendations within deliberative assemblies? (2) How does the deliberative process shape delegate perspectives and influence voting dynamics over the course of the assembly? To address these questions, we develop LLM-based methodologies for empirically analyzing transcripts from a tech-enhanced in-person deliberative assembly. The framework identifies and visualizes the space of expressed suggestions. We also empirically reconstruct each delegate's evolving perspective throughout the assembly. Our methods contribute novel empirical insights into deliberative processes and demonstrate how LLMs can surface high-resolution dynamics otherwise invisible in traditional assembly outputs.

General-domain large multimodal models (LMMs) have achieved significant advances in various image-text tasks. However, their performance in the Intelligent Traffic Surveillance (ITS) domain remains limited due to the absence of dedicated multimodal datasets. To address this gap, we introduce MITS (Multimodal Intelligent Traffic Surveillance), the first large-scale multimodal benchmark dataset specifically designed for ITS. MITS includes 170,400 independently collected real-world ITS images sourced from traffic surveillance cameras, annotated with eight main categories and 24 subcategories of ITS-specific objects and events under diverse environmental conditions. Additionally, through a systematic data generation pipeline, we generate high-quality image captions and 5 million instruction-following visual question-answer pairs, addressing five critical ITS tasks: object and event recognition, object counting, object localization, background analysis, and event reasoning. To demonstrate MITS's effectiveness, we fine-tune mainstream LMMs on this dataset, enabling the development of ITS-specific applications. Experimental results show that MITS significantly improves LMM performance in ITS applications, increasing LLaVA-1.5's performance from 0.494 to 0.905 (+83.2%), LLaVA-1.6's from 0.678 to 0.921 (+35.8%), Qwen2-VL's from 0.584 to 0.926 (+58.6%), and Qwen2.5-VL's from 0.732 to 0.930 (+27.0%). We release the dataset, code, and models as open-source, providing high-value resources to advance both ITS and LMM research.

"One of the dreams I had as a kid was about the first day of school, and being able to build and be creative, and it was the happiest day of my life. And at MIT, I felt like that dream became reality," says Ballesteros. Growing up in the suburban town of Spring, Texas, just outside of Houston, Erik Ballesteros couldn't help but be drawn in by the possibilities for humans in space. It was the early 2000s, and NASA's space shuttle program was the main transport for astronauts to the International Space Station (ISS). Ballesteros' hometown was less than an hour from Johnson Space Center (JSC), where NASA's mission control center and astronaut training facility are based.