Any motorist who has ever waited through multiple cycles for a traffic light to turn green knows how annoying signalized intersections can be. But sitting at intersections isn’t just a drag on drivers’ patience — unproductive vehicle idling could contribute as much as 15 percent of the carbon dioxide emissions from U.S. land transportation.

A large-scale modeling study led by MIT researchers reveals that eco-driving measures, which can involve dynamically adjusting vehicle speeds to reduce stopping and excessive acceleration, could significantly reduce those CO2 emissions.

Using a powerful artificial intelligence method called deep reinforcement learning, the researchers conducted an in-depth impact assessment of the factors affecting vehicle emissions in three major U.S. cities.

Their analysis indicates that fully adopting eco-driving measures could cut annual city-wide intersection carbon emissions by 11 to 22 percent, without slowing traffic throughput or affecting vehicle and traffic safety.

Even if only 10 percent of vehicles on the road employ eco-driving, it would result in 25 to 50 percent of the total reduction in CO2 emissions, the researchers found.

In addition, dynamically optimizing speed limits at about 20 percent of intersections provides 70 percent of the total emission benefits. This indicates that eco-driving measures could be implemented gradually while still having measurable, positive impacts on mitigating climate change and improving public health.

“Vehicle-based control strategies like eco-driving can move the needle on climate change reduction. We’ve shown here that modern machine-learning tools, like deep reinforcement learning, can accelerate the kinds of analysis that support sociotechnical decision making. This is just the tip of the iceberg,” says senior author Cathy Wu, the Thomas D. and Virginia W. Cabot Career Development Associate Professor in Civil and Environmental Engineering (CEE) and the Institute for Data, Systems, and Society (IDSS) at MIT, and a member of the Laboratory for Information and Decision Systems (LIDS).

She is joined on the paper by lead author Vindula Jayawardana, an MIT graduate student; as well as MIT graduate students Ao Qu, Cameron Hickert, and Edgar Sanchez; MIT undergraduate Catherine Tang; Baptiste Freydt, a graduate student at ETH Zurich; and Mark Taylor and Blaine Leonard of the Utah Department of Transportation. The research appears in Transportation Research Part C: Emerging Technologies.

A multi-part modeling study

Traffic control measures typically call to mind fixed infrastructure, like stop signs and traffic signals. But as vehicles become more technologically advanced, it presents an opportunity for eco-driving, which is a catch-all term for vehicle-based traffic control measures like the use of dynamic speeds to reduce energy consumption.

In the near term, eco-driving could involve speed guidance in the form of vehicle dashboards or smartphone apps. In the longer term, eco-driving could involve intelligent speed commands that directly control the acceleration of semi-autonomous and fully autonomous vehicles through vehicle-to-infrastructure communication systems.

“Most prior work has focused on how to implement eco-driving. We shifted the frame to consider the question of should we implement eco-driving. If we were to deploy this technology at scale, would it make a difference?” Wu says.

To answer that question, the researchers embarked on a multifaceted modeling study that would take the better part of four years to complete.

They began by identifying 33 factors that influence vehicle emissions, including temperature, road grade, intersection topology, age of the vehicle, traffic demand, vehicle types, driver behavior, traffic signal timing, road geometry, etc.

“One of the biggest challenges was making sure we were diligent and didn’t leave out any major factors,” Wu says.

Then they used data from open street maps, U.S. geological surveys, and other sources to create digital replicas of more than 6,000 signalized intersections in three cities — Atlanta, San Francisco, and Los Angeles — and simulated more than a million traffic scenarios.

The researchers used deep reinforcement learning to optimize each scenario for eco-driving to achieve the maximum emissions benefits.

Reinforcement learning optimizes the vehicles’ driving behavior through trial-and-error interactions with a high-fidelity traffic simulator, rewarding vehicle behaviors that are more energy-efficient while penalizing those that are not.

However, training vehicle behaviors that generalize across diverse intersection traffic scenarios was a major challenge. The researchers observed that some scenarios are more similar to one another than others, such as scenarios with the same number of lanes or the same number of traffic signal phases.

As such, the researchers trained separate reinforcement learning models for different clusters of traffic scenarios, yielding better emission benefits overall.

But even with the help of AI, analyzing citywide traffic at the network level would be so computationally intensive it could take another decade to unravel, Wu says.

Instead, they broke the problem down and solved each eco-driving scenario at the individual intersection level.

“We carefully constrained the impact of eco-driving control at each intersection on neighboring intersections. In this way, we dramatically simplified the problem, which enabled us to perform this analysis at scale, without introducing unknown network effects,” she says.

Significant emissions benefits

When they analyzed the results, the researchers found that full adoption of eco-driving could result in intersection emissions reductions of between 11 and 22 percent.

These benefits differ depending on the layout of a city’s streets. A denser city like San Francisco has less room to implement eco-driving between intersections, offering a possible explanation for reduced emission savings, while Atlanta could see greater benefits given its higher speed limits.

Even if only 10 percent of vehicles employ eco-driving, a city could still realize 25 to 50 percent of the total emissions benefit because of car-following dynamics: Non-eco-driving vehicles would follow controlled eco-driving vehicles as they optimize speed to pass smoothly through intersections, reducing their carbon emissions as well.

In some cases, eco-driving could also increase vehicle throughput by minimizing emissions. However, Wu cautions that increasing throughput could result in more drivers taking to the roads, reducing emissions benefits.

And while their analysis of widely used safety metrics known as surrogate safety measures, such as time to collision, suggest that eco-driving is as safe as human driving, it could cause unexpected behavior in human drivers. More research is needed to fully understand potential safety impacts, Wu says.

Their results also show that eco-driving could provide even greater benefits when combined with alternative transportation decarbonization solutions. For instance, 20 percent eco-driving adoption in San Francisco would cut emission levels by 7 percent, but when combined with the projected adoption of hybrid and electric vehicles, it would cut emissions by 17 percent.

“This is a first attempt to systematically quantify network-wide environmental benefits of eco-driving. This is a great research effort that will serve as a key reference for others to build on in the assessment of eco-driving systems,” says Hesham Rakha, the Samuel L. Pritchard Professor of Engineering at Virginia Tech, who was not involved with this research.

And while the researchers focus on carbon emissions, the benefits are highly correlated with improvements in fuel consumption, energy use, and air quality.

“This is almost a free intervention. We already have smartphones in our cars, and we are rapidly adopting cars with more advanced automation features. For something to scale quickly in practice, it must be relatively simple to implement and shovel-ready. Eco-driving fits that bill,” Wu says.

This work is funded, in part, by Amazon and the Utah Department of Transportation.

The MIT Center for International Studies announced the launch of a new pilot initiative with Angola, to be implemented through its MIT-Africa Program.

The new initiative marks a significant collaboration between MIT-Africa, Sonangol (Angola’s national energy company), and the Instituto Superior Politécnico de Tecnologias e Ciências (ISPTEC). The collaboration was formalized at a signing ceremony on MIT’s campus in June with key stakeholders from all three institutions present, including Diamantino Pedro Azevedo, the Angolan minister of mineral resources, petroleum, and gas, and Sonangol CEO Gaspar Martins.

“This partnership marks a pivotal step in the Angolan government’s commitment to leveraging knowledge as the cornerstone of the country’s economic transformation,” says Azevedo. “By connecting the oil and gas sector with science, innovation, and world-class training, we are equipping future generations to lead Angola into a more technological, sustainable, and globally competitive era.”

The sentiment is shared by the MIT-Africa Program leaders. “This initiative reflects MIT’s deep commitment to fostering meaningful, long-term relationships across the African continent,” says Mai Hassan, faculty director of the MIT-Africa Program. “It supports our mission of advancing knowledge and educating students in ways that are globally informed, and it provides a platform for mutual learning. By working with Angolan partners, we gain new perspectives and opportunities for innovation that benefit both MIT and our collaborators.”

In addition to its new collaboration with MIT-Africa, Sonangol has joined MIT’s Industrial Liaison Program (ILP), breaking new ground as its first corporate member based in sub-Saharan Africa. ILP enables companies worldwide to harness MIT resources to address current challenges and to anticipate future needs. As an ILP member, Sonangol seeks to facilitate collaboration in key sectors such as natural resources and mining, energy, construction, and infrastructure.

The MIT-Africa Program manages a portfolio of research, teaching, and learning initiatives that emphasize two-way value — offering impactful experiences to MIT students and faculty while collaborating closely with institutions and communities across Africa. The new Angola collaboration is aligned with this ethos, and will launch with two core activities during the upcoming academic year:

1. Global Classroom: An MIT course on geo-spatial technologies for environmental monitoring, taught by an MIT faculty member, will be brought directly to the ISPTEC campus, offering Angolan students and MIT participants a collaborative, in-country learning experience.
2. Global Teaching Labs: MIT students will travel to ISPTEC to teach science, technology, engineering, arts, and mathematics subjects on renewable energy technologies, engaging Angolan students through hands-on instruction.

“This is not a traditional development project,” says Ari Jacobovits, managing director of MIT-Africa. “This is about building genuine partnerships rooted in academic rigor, innovation, and shared curiosity. The collaboration has been designed from the ground up with our partners at ISPTEC and Sonangol. We’re coming in with a readiness to learn as much as we teach.”

The pilot marks an important first step in establishing a long-term collaboration with Angola. By investing in collaborative education and innovation, the new initiative aims to spark novel approaches to global challenges and strengthen academic institutions on both sides.

These agreements with MIT-Africa and ILP “not only enhance our innovation and technological capabilities, but also create opportunities for sustainable development and operational excellence,” says Gaspar. “They advance our mission to be a leading force in the African energy sector.”

“The vision behind this initiative is bold,” says Hassan. “It’s about co-creating knowledge and building capacity that lasts.”

Four new faculty members join the School of Architecture and Planning (SA+P) this fall, offering the MIT community creativity, knowledge, and scholarship in multidisciplinary roles.

“These individuals add considerable strength and depth to our faculty,” says Hashim Sarkis, dean of the School of Architecture and Planning. “We are excited for the academic vigor they bring to research and teaching.”

Karrie G. Karahalios ’94, MEng ’95, SM ’97, PhD ’04 joins the MIT Media Lab as a full professor of media arts and sciences. Karahalios is a pioneer in the exploration of social media and of how people communicate in environments that are increasingly mediated by algorithms that, as she has written, “shape the world around us.” Her work combines computing, systems, artificial intelligence, anthropology, sociology, psychology, game theory, design, and infrastructure studies. Karahalios’ work has received numerous honors including the National Science Foundation CAREER Award, Alfred P. Sloan Research Fellowship, SIGMOD Best Paper Award, and recognition as an ACM Distinguished Member.

Pat Pataranutaporn SM ’18, PhD ’20 joins the MIT Media Lab as an assistant professor of media arts and sciences. A visionary technologist, scientist, and designer, Pataranutaporn explores the frontier of human-AI interaction, inventing and investigating AI systems that support human thriving. His research focuses on how personalized AI systems can amplify human cognition, from learning and decision-making to self-development, reflection, and well-being. Pataranutaporn will co-direct the Advancing Humans with AI Program.

Mariana Popescu joins the Department of Architecture as an assistant professor. Popescu is a computational architect and structural designer with a strong interest and experience in innovative ways of approaching the fabrication process and use of materials in construction. Her area of expertise is computational and parametric design, with a focus on digital fabrication and sustainable design. Her extensive involvement in projects related to promoting sustainability has led to a multilateral development of skills, which combine the fields of architecture, engineering, computational design, and digital fabrication. Popescu earned her doctorate at ETH Zurich. She was named a “Pioneer” on the MIT Technology Review global list of “35 innovators under 35” in 2019.

Holly Samuelson joins the Department of Architecture as an associate professor in the Building Technology Program at MIT, teaching architectural technology courses. Her teaching and research focus on issues of building design that impact human and environmental health. Her current projects harness advanced building simulation to investigate issues of greenhouse gas emissions, heat vulnerability, and indoor environmental quality while considering the future of buildings in a changing electricity grid. Samuelson has co-authored over 40 peer-reviewed papers, winning a best paper award from the journal Energy and Building. As a recognized expert in architectural technology, she has been featured in news outlets including The Washington Post, The Boston Globe, the BBC, and The Wall Street Journal. Samuelson earned her doctor of design from Harvard University Graduate School of Design.

Peter Temin PhD ’64, the MIT Elisha Gray II Professor of Economics, emeritus, passed away on Aug. 4. He was 87. 

Temin was a preeminent economic historian whose work spanned a remarkable range of topics, from the British Industrial Revolution and Roman economic history to the causes of the Great Depression and, later in his career, the decline of the American middle class. He also made important contributions to modernizing the field of economic history through his systematic use of economic theory and data analysis.

“Peter was a dedicated teacher and a wonderful colleague, who could bring economic history to life like few before or since,” says Jonathan Gruber, Ford Professor and chair of the Department of Economics. “As an undergraduate at MIT, I knew Peter as an engaging teacher and UROP [Undergraduate Research Opportunities Program] supervisor. Later, as a faculty member, I knew him as a steady and supportive colleague. A great person to talk to about everything, from research to politics to life at the Cape. Peter was the full package: a great scholar, a great teacher, and a dedicated public goods provider.”

When Temin began his career, the field of economic history was undergoing a reorientation within the profession. Led by giants like Paul Samuelson and Robert Solow, economics had become a more quantitative, mathematically rigorous discipline, and economic historians responded by embracing the new tools of economic theory and data collection. This “new economic history” (today also known as “cliometrics”) revolutionized the field by introducing statistical analysis and mathematical modeling to the study of the past. Temin was a pioneer of this new approach, using econometrics to reexamine key historical events and demonstrate how data analysis could lead to the overturning of long-held assumptions.

A prolific scholar who authored 17 books and edited six, Temin made important contributions to an incredibly diverse set of topics. “As kindly as he was brilliant, Peter was a unique type of academic,” says Harvard University Professor Claudia Goldin, a fellow economic historian and winner of the 2023 Nobel Prize in economic sciences. “He was a macroeconomist and an economic historian who later worked on today’s social problems. In between, he studied antitrust, health care, and the Roman economy.”

Temin’s earliest work focused on American industrial development during the 19th century and honed the signature approach that quickly made him a leading economic historian — combining rigorous economic theory with a deep understanding of historical context to reexamine the past. Temin was known for his extensive analysis of the Great Depression, which often challenged prevailing wisdom. By arguing that factors beyond monetary policy — including the gold standard and a decline in consumer spending — were critical drivers of the crisis, Temin helped recast how economists think about the catastrophe and the role of monetary policy in economic downturns.

As his career progressed, Temin’s work increasingly expanded to include the economic history of other regions and periods. His later work on the Great Depression placed a greater emphasis on the international context of the crisis, and he made significant contributions to our understanding of the drivers of the British Industrial Revolution and the nature of the Roman economy.

“Peter Temin was a giant in the field of economic history, with work touching every aspect of the field and original ideas backed by careful research,” says Daron Acemoglu, Institute Professor and recipient of the 2024 Nobel Prize in economics. “He challenged the modern view of the Industrial Revolution that emphasized technological changes in a few industries, pointing instead to a broader transformation of the British economy. He took on the famous historian of the ancient world, Moses Finley, arguing that slavery notwithstanding, markets in the Roman economy — especially land markets — worked. Peter’s influence and contributions have been long-lasting and will continue to be so.”

Temin was born in Philadelphia in 1937. His parents were activists who emphasized social responsibility, and his older brother, Howard, became a geneticist and virologist who shared the 1975 Nobel Prize in medicine. Temin received his BA from Swarthmore College in 1959 and went on to earn his PhD in Economics from MIT in 1964. He was a junior fellow of Harvard University’s Society of Fellows from 1962 to 1965.

Temin started his career as an assistant professor of industrial history at the MIT Sloan School of Management before being hired by the Department of Economics in 1967. He served as department chair from 1990t o 1993 and held the Elisha Gray II professorship from 1993 to 2009. Temin won a Guggenheim Fellowship in 2001, and served as president of the Economic History Association (1995-96) and the Eastern Economic Association (2001-02).

At MIT, Temin’s scholarly achievements were matched by a deep commitment to engaging students as a teacher and advisor. “As a researcher, Peter was able to zero in on the key questions around a topic and find answers where others had been flailing,” says Christina Romer, chair of the Council of Economic Advisers under President Obama and a former student and advisee. “As a teacher, he managed to draw sleepy students into a rousing discussion that made us think we had figured out the material on our own, when, in fact, he had been masterfully guiding us. And as a mentor, he was unfailingly supportive and generous with both his time and his vast knowledge of economic history. I feel blessed to have been one of his students.”

When he became the economics department head in 1990, Temin prioritized hiring newly-minted PhDs and other junior faculty. This foresight continues to pay dividends — his junior hires included Daron Acemoglu and Abhijit Banerjee, and he launched the recruiting of Bengt Holmström for a senior faculty position. All three went on to win Nobel Prizes and have been pillars of economics research and education at MIT.

Temin remained an active researcher and author after his retirement in 2009. Much of his later work turned toward the contemporary American economy and its deep-seated divisions. In his influential 2017 book, “The Vanishing Middle Class: Prejudice and Power in a Dual Economy,” he argued that the United States had become a “dual economy,” with a prosperous finance, technology, and electronics sector on one hand and, on the other, a low-wage sector characterized by stagnant opportunity.

“There are echoes of Temin’s later writings in current department initiatives, such as the Stone Center on Inequality and Shaping the Future of Work” notes Gruber. “Temin was in many ways ahead of the curve in treating inequality as an issue of central importance for our discipline.”

In “The Vanishing Middle Class,” Temin also explored the role that historical events, particularly the legacy of slavery and its aftermath, played in creating and perpetuating economic divides. He further explored these themes in his last book, “Never Together: The Economic History of a Segregated America,” published in 2022. While Temin was perhaps best known for his work applying modern economic tools to the past, this later work showed that he was no less adept at the inverse: using historical analysis to shed light on modern economic problems.

Temin was active with MIT Hillel throughout his career, and outside the Institute, he enjoyed staying active. He could often be seen walking or biking to MIT, and taking a walk around Jamaica Pond was a favorite activity in his last few months of life. Peter and his late wife Charlotte were also avid travelers and art collectors. He was a wonderful husband, father, and grandfather, who was deeply devoted to his family.

Temin is lovingly remembered by his daughter Elizabeth “Liz” Temin and three grandsons, Colin and Zachary Gibbons and Elijah Mendez. He was preceded in death by his wife, Charlotte Temin, a psychologist and educator, and his daughter, Melanie Temin Mendez.

Artificial intelligence is changing the way businesses store and access their data. That’s because traditional data storage systems were designed to handle simple commands from a handful of users at once, whereas today, AI systems with millions of agents need to continuously access and process large amounts of data in parallel. Traditional data storage systems now have layers of complexity, which slows AI systems down because data must pass through multiple tiers before reaching the graphical processing units (GPUs) that are the brain cells of AI.

Cloudian, co-founded by Michael Tso ’93, SM ’93 and Hiroshi Ohta, is helping storage keep up with the AI revolution. The company has developed a scalable storage system for businesses that helps data flow seamlessly between storage and AI models. The system reduces complexity by applying parallel computing to data storage, consolidating AI functions and data onto a single parallel-processing platform that stores, retrieves, and processes scalable datasets, with direct, high-speed transfers between storage and GPUs and CPUs.

Cloudian’s integrated storage-computing platform simplifies the process of building commercial-scale AI tools and gives businesses a storage foundation that can keep up with the rise of AI.

“One of the things people miss about AI is that it’s all about the data,” Tso says. “You can’t get a 10 percent improvement in AI performance with 10 percent more data or even 10 times more data — you need 1,000 times more data. Being able to store that data in a way that’s easy to manage, and in such a way that you can embed computations into it so you can run operations while the data is coming in without moving the data — that’s where this industry is going.”

From MIT to industry

As an undergraduate at MIT in the 1990s, Tso was introduced by Professor William Dally to parallel computing — a type of computation in which many calculations occur simultaneously. Tso also worked on parallel computing with Associate Professor Greg Papadopoulos.

“It was an incredible time because most schools had one super-computing project going on — MIT had four,” Tso recalls.

As a graduate student, Tso worked with MIT senior research scientist David Clark, a computing pioneer who contributed to the internet’s early architecture, particularly the transmission control protocol (TCP) that delivers data between systems.

“As a graduate student at MIT, I worked on disconnected and intermittent networking operations for large scale distributed systems,” Tso says. “It’s funny — 30 years on, that’s what I’m still doing today.”

Following his graduation, Tso worked at Intel’s Architecture Lab, where he invented data synchronization algorithms used by Blackberry. He also created specifications for Nokia that ignited the ringtone download industry. He then joined Inktomi, a startup co-founded by Eric Brewer SM ’92, PhD ’94 that pioneered search and web content distribution technologies.

In 2001, Tso started Gemini Mobile Technologies with Joseph Norton ’93, SM ’93 and others. The company went on to build the world’s largest mobile messaging systems to handle the massive data growth from camera phones. Then, in the late 2000s, cloud computing became a powerful way for businesses to rent virtual servers as they grew their operations. Tso noticed the amount of data being collected was growing far faster than the speed of networking, so he decided to pivot the company.

“Data is being created in a lot of different places, and that data has its own gravity: It’s going to cost you money and time to move it,” Tso explains. “That means the end state is a distributed cloud that reaches out to edge devices and servers. You have to bring the cloud to the data, not the data to the cloud.”

Tso officially launched Cloudian out of Gemini Mobile Technologies in 2012, with a new emphasis on helping customers with scalable, distributed, cloud-compatible data storage.

“What we didn’t see when we first started the company was that AI was going to be the ultimate use case for data on the edge,” Tso says.

Although Tso’s research at MIT began more than two decades ago, he sees strong connections between what he worked on and the industry today.

“It’s like my whole life is playing back because David Clark and I were dealing with disconnected and intermittently connected networks, which are part of every edge use case today, and Professor Dally was working on very fast, scalable interconnects,” Tso says, noting that Dally is now the senior vice president and chief scientist at the leading AI company NVIDIA. “Now, when you look at the modern NVIDIA chip architecture and the way they do interchip communication, it’s got Dally’s work all over it. With Professor Papadopoulos, I worked on accelerate application software with parallel computing hardware without having to rewrite the applications, and that’s exactly the problem we are trying to solve with NVIDIA. Coincidentally, all the stuff I was doing at MIT is playing out.”

Today Cloudian’s platform uses an object storage architecture in which all kinds of data —documents, videos, sensor data — are stored as a unique object with metadata. Object storage can manage massive datasets in a flat file stucture, making it ideal for unstructured data and AI systems, but it traditionally hasn’t been able to send data directly to AI models without the data first being copied into a computer’s memory system, creating latency and energy bottlenecks for businesses.

In July, Cloudian announced that it has extended its object storage system with a vector database that stores data in a form which is immediately usable by AI models. As the data are ingested, Cloudian is computing in real-time the vector form of that data to power AI tools like recommender engines, search, and AI assistants. Cloudian also announced a partnership with NVIDIA that allows its storage system to work directly with the AI company’s GPUs. Cloudian says the new system enables even faster AI operations and reduces computing costs.

“NVIDIA contacted us about a year and a half ago because GPUs are useful only with data that keeps them busy,” Tso says. “Now that people are realizing it’s easier to move the AI to the data than it is to move huge datasets. Our storage systems embed a lot of AI functions, so we’re able to pre- and post-process data for AI near where we collect and store the data.”

AI-first storage

Cloudian is helping about 1,000 companies around the world get more value out of their data, including large manufacturers, financial service providers, health care organizations, and government agencies.

Cloudian’s storage platform is helping one large automaker, for instance, use AI to determine when each of its manufacturing robots need to be serviced. Cloudian is also working with the National Library of Medicine to store research articles and patents, and the National Cancer Database to store DNA sequences of tumors — rich datasets that AI models could process to help research develop new treatments or gain new insights.

“GPUs have been an incredible enabler,” Tso says. “Moore’s Law doubles the amount of compute every two years, but GPUs are able to parallelize operations on chips, so you can network GPUs together and shatter Moore’s Law. That scale is pushing AI to new levels of intelligence, but the only way to make GPUs work hard is to feed them data at the same speed that they compute — and the only way to do that is to get rid of all the layers between them and your data.”

If you’ve urgently ordered a package from Amazon — and exhaled when it arrived on your doorstep hours later — you likely have three graduates of the MIT Leaders for Global Operations (LGO) program to thank: John Tagawa SM ’99; Diego Méndez de la Luz MNG ’04, MBA ’11, SM ’11; or Chuck Cummings MBA ’11, SM ’11.

Each holds critical roles within the company. Tagawa oversees Amazon’s North American operations. Méndez de la Luz heads up operations in Mexico. Cummings leads customer fulfillment throughout Canada. They also mentor LGO students and recent graduates throughout the organization and credit LGO’s singular blend of operational and leadership strength for their success as Amazon grows.

John Tagawa

Tagawa came to Amazon — now the world’s largest online retailer — through an LGO alumni connection in 2008, joining the organization during rapid expansion. He led fulfillment centers on the West Coast and went on to oversee operations in India, South America, and in Europe, with a focus on safety, speed, and efficiency.

Today, he’s a resource for other LGO graduates at Amazon, applauding the program’s uniquely multidimensional focus on tech, engineering, and leadership, all of which are key pillars as the organization continues to grow.

“Today, we have hundreds of fulfillment centers worldwide, and Amazon has grown its transportation and last-mile delivery network in an effort to ensure greater resilience and speed in getting products to customers,” he explains.

Tagawa says that LGO’s unique dual-degree program provided a singular blueprint for success as an operations leader and an engineer.

“The technology and engineering education that I received at MIT plays directly into my day-to-day role. We’re constantly thinking about how to infuse technology and innovate at scale to improve outcomes for our employees and customers. That ranges from introducing robotics to our fulfillment centers to using AI to determine how much inventory we should buy and where we should place it to introducing technology on the shop floor to help our frontline leaders. Those components of my LGO education were critical,” he says. 

After receiving his undergraduate degree at the University of Washington, Tagawa pursued engineering and operations roles. But it wasn’t until LGO that he realized how important the fusion of business, operations, and leadership competencies was.

“What drew me to LGO was being able to study business and finance, coupled with an engineering and leadership education. I hadn’t realized how powerful bringing all three of those disciplines together could be,” he reflects. “Amazon’s efficacy relies on how great our leaders are, and a big part of my role is to develop, coach, and build a great leadership team. The foundation of my ability to do that is based on what I learned at MIT about becoming a lifelong learner.”

Tagawa recalls his own classes with Donald Davis, the late chair and CEO of The Stanley Works. Davis was one of LGO’s first lecturers, sharing case studies from his time on the front lines. Davis imparted the concepts of servant-leadership and diversity, which shaped Tagawa’s outlook at Amazon.

“I get energized by the leadership principles at Amazon. We strive to be Earth's best employer​​ and being customer-obsessed. It’s energizing to lead large-scale organizations whose sole mission is to improve the lives of our employees and customers, with a strong focus on developing great leaders. Who could ask for something better than that?” he asks.

Diego Méndez de la Luz

This blend of leadership acumen and engineering dynamism also jump-started the career of Méndez de la Luz, now Amazon’s country director of Mexico operations. LGO’s leadership focus was crucial in preparing him for his Amazon role, where he oversees the vast majority of Amazon’s 10,000 employees in Mexico — those who work in operations — across 40 facilities throughout his home country.

At MIT, he took classes with notable professors, whom he credits with broadening his intellectual and professional horizons. 

“I was a good student throughout my education, but only after joining LGO did I learn what I consider to be foundational concepts and skills,” says Méndez de la Luz, who also started his career in engineering. “I learned about inventory management, business law, accounting, and about how to have important conversations in the workplace — things I never learned as an engineer. LGO was tremendously useful.”

Méndez de la Luz joined Amazon shortly after LGO, working his way up from frontline management roles at fulfillment centers throughout the United States. Today, he oversees the end-to-end network of imports, fulfillment, transportation, and customer delivery.

At Amazon, he believes he’s making a real difference in his native country. With Amazon’s scale comes the responsibility to improve both the planet and local communities, he says. Amazon engages with communities through volunteer programs, literacy efforts, and partnerships with shelters.

Today, Méndez de la Luz says that he’s working in his “dream job — exactly what I went to MIT for,” in a community he loves.

“My role at Amazon is a great source of pride. When I was growing up, I wanted to be the president of Mexico. I still want to make a difference for people in our society. Here, I have the ability to come back to my home country to create good jobs. Having the ability to do that has been a surprise to me — but a very positive development that I just value so much,” he says. “I want people to feel excited that they’re going to come to work and see their friends and colleagues do well.”

Chuck Cummings

This collaborative atmosphere propelled Cummings to pursue a post-MIT career at Amazon after years as a mechanical engineer. He discovered a hospitable workplace that valued growth: He began as an operations management intern, and today he leads the customer fulfillment business in Canada, which includes the country’s fulfillment centers. It’s a big job made better by his LGO expertise, where he always strives for co-worker and customer satisfaction.

“I sought out LGO because I’ve always loved the shop floor,” he says. “I continue to get excited about: How do we offer faster speeds to Canadian customers? How do we keep lowering our cost structure so that we can continue to invest and offer new benefits for our customers? At the same time, how do I build the absolute best working environment for all of my employees?”

Last year, Cummings’ team launched an Amazon robotics fulfillment center in Calgary, Alberta. This was a significant enhancement for Canadian customers; now, Calgary shoppers have more inventory much closer to home, with delivery speeds to match. Cummings also helped to bring Amazon’s storage and distribution network to a new facility in Vancouver, British Columbia, which will enable nearby fulfillment centers to respond to a wider selection of customer orders at the fastest-possible delivery speeds.

These were substantial endeavors, which he felt comfortable undertaking thanks to his classes at MIT. His experience was so meaningful that Cummings now serves as Amazon’s co-school captain for LGO, where he recruits the next generation of LGO graduates for internships and full-time roles. Cummings has now worked with more than 25 LGO graduates, and he says they’re easy to pick out of a crowd.

“You can give them very ambiguous, complex problems, and they can dive into the data and come out with an amazing solution. But what makes LGO students even more special is, at the same time, they have strong communication skills. They have a lot of emotional intelligence. It’s a combination of business leadership with extreme technical understanding,” he says.

Both Tagawa and Méndez de la Luz interact frequently with LGO students, too. They agree that, while Amazon’s technology is always unfolding, its leadership qualities remain constant — and match perfectly with LGO’s reputation for creating dynamic, empathetic professionals who also prize technical skill.

“Whereas technology has grown and changed by leaps and bounds, leadership principles carry on for decades,” Tagawa says. “The infusion of the engineering, business, and leadership components at LGO are second to none.”

A new strategy for strengthening polymer materials could lead to more durable plastics and cut down on plastic waste, according to researchers at MIT and Duke University.

Using machine learning, the researchers identified crosslinker molecules that can be added to polymer materials, allowing them to withstand more force before tearing. These crosslinkers belong to a class of molecules known as mechanophores, which change their shape or other properties in response to mechanical force.

“These molecules can be useful for making polymers that would be stronger in response to force. You apply some stress to them, and rather than cracking or breaking, you instead see something that has higher resilience,” says Heather Kulik, the Lammot du Pont Professor of Chemical Engineering at MIT, who is also a professor of chemistry and the senior author of the study.

The crosslinkers that the researchers identified in this study are iron-containing compounds known as ferrocenes, which until now had not been broadly explored for their potential as mechanophores. Experimentally evaluating a single mechanophore can take weeks, but the researchers showed that they could use a machine-learning model to dramatically speed up this process.

MIT postdoc Ilia Kevlishvili is the lead author of the open-access paper, which appeared Friday in ACS Central Science. Other authors include Jafer Vakil, a Duke graduate student; David Kastner and Xiao Huang, both MIT graduate students; and Stephen Craig, a professor of chemistry at Duke.

The weakest link

Mechanophores are molecules that respond to force in unique ways, typically by changing their color, structure, or other properties. In the new study, the MIT and Duke team wanted to investigate whether they could be used to help make polymers more resilient to damage.

The new work builds on a 2023 study from Craig and Jeremiah Johnson, the A. Thomas Guertin Professor of Chemistry at MIT, and their colleagues. In that work, the researchers found that, surprisingly, incorporating weak crosslinkers into a polymer network can make the overall material stronger. When materials with these weak crosslinkers are stretched to the breaking point, any cracks propagating through the material try to avoid the stronger bonds and go through the weaker bonds instead. This means the crack has to break more bonds than it would if all of the bonds were the same strength.

To find new ways to exploit that phenomenon, Craig and Kulik joined forces to try to identify mechanophores that could be used as weak crosslinkers.

“We had this new mechanistic insight and opportunity, but it came with a big challenge: Of all possible compositions of matter, how do we zero in on the ones with the greatest potential?” Craig says. “Full credit to Heather and Ilia for both identifying this challenge and devising an approach to meet it.”

Discovering and characterizing mechanophores is a difficult task that requires either time-consuming experiments or computationally intense simulations of molecular interactions. Most of the known mechanophores are organic compounds, such as cyclobutane, which was used as a crosslinker in the 2023 study.

In the new study, the researchers wanted to focus on molecules known as ferrocenes, which are believed to hold potential as mechanophores. Ferrocenes are organometallic compounds that have an iron atom sandwiched between two carbon-containing rings. Those rings can have different chemical groups added to them, which alter their chemical and mechanical properties.

Many ferrocenes are used as pharmaceuticals or catalysts, and a handful are known to be good mechanophores, but most have not been evaluated for that use. Experimental tests on a single potential mechanophore can take several weeks, and computational simulations, while faster, still take a couple of days. Evaluating thousands of candidates using these strategies is a daunting task.

Realizing that a machine-learning approach could dramatically speed up the characterization of these molecules, the MIT and Duke team decided to use a neural network to identify ferrocenes that could be promising mechanophores.

They began with information from a database known as the Cambridge Structural Database, which contains the structures of 5,000 different ferrocenes that have already been synthesized.

“We knew that we didn’t have to worry about the question of synthesizability, at least from the perspective of the mechanophore itself. This allowed us to pick a really large space to explore with a lot of chemical diversity, that also would be synthetically realizable,” Kevlishvili says.

First, the researchers performed computational simulations for about 400 of these compounds, allowing them to calculate how much force is necessary to pull atoms apart within each molecule. For this application, they were looking for molecules that would break apart quickly, as these weak links could make polymer materials more resistant to tearing.

Then they used this data, along with information on the structure of each compound, to train a machine-learning model. This model was able to predict the force needed to activate the mechanophore, which in turn influences resistance to tearing, for the remaining 4,500 compounds in the database, plus an additional 7,000 compounds that are similar to those in the database but have some atoms rearranged.

The researchers discovered two main features that seemed likely to increase tear resistance. One was interactions between the chemical groups that are attached to the ferrocene rings. Additionally, the presence of large, bulky molecules attached to both rings of the ferrocene made the molecule more likely to break apart in response to applied forces.

While the first of these features was not surprising, the second trait was not something a chemist would have predicted beforehand, and could not have been detected without AI, the researchers say. “This was something truly surprising,” Kulik says.

Tougher plastics

Once the researchers identified about 100 promising candidates, Craig’s lab at Duke synthesized a polymer material incorporating one of them, known as m-TMS-Fc. Within the material, m-TMS-Fc acts as a crosslinker, connecting the polymer strands that make up polyacrylate, a type of plastic.

By applying force to each polymer until it tore, the researchers found that the weak m-TMS-Fc linker produced a strong, tear-resistant polymer. This polymer turned out to be about four times tougher than polymers made with standard ferrocene as the crosslinker.

“That really has big implications because if we think of all the plastics that we use and all the plastic waste accumulation, if you make materials tougher, that means their lifetime will be longer. They will be usable for a longer period of time, which could reduce plastic production in the long term,” Kevlishvili says.

The researchers now hope to use their machine-learning approach to identify mechanophores with other desirable properties, such as the ability to change color or become catalytically active in response to force. Such materials could be used as stress sensors or switchable catalysts, and they could also be useful for biomedical applications such as drug delivery.

In those studies, the researchers plan to focus on ferrocenes and other metal-containing mechanophores that have already been synthesized but whose properties are not fully understood.

“Transition metal mechanophores are relatively underexplored, and they’re probably a little bit more challenging to make,” Kulik says. “This computational workflow can be broadly used to enlarge the space of mechanophores that people have studied.”

The research was funded by the National Science Foundation Center for the Chemistry of Molecularly Optimized Networks (MONET).

M.C. Escher’s artwork is a gateway into a world of depth-defying optical illusions, featuring “impossible objects” that break the laws of physics with convoluted geometries. What you perceive his illustrations to be depends on your point of view — for example, a person seemingly walking upstairs may be heading down the steps if you tilt your head sideways. 

Computer graphics scientists and designers can recreate these illusions in 3D, but only by bending or cutting a real shape and positioning it at a particular angle. This workaround has downsides, though: Changing the smoothness or lighting of the structure will expose that it isn’t actually an optical illusion, which also means you can’t accurately solve geometry problems on it.

Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a unique approach to represent “impossible” objects in a more versatile way. Their “Meschers” tool converts images and 3D models into 2.5-dimensional structures, creating Escher-like depictions of things like windows, buildings, and even donuts. The approach helps users relight, smooth out, and study unique geometries while preserving their optical illusion.

This tool could assist geometry researchers with calculating the distance between two points on a curved impossible surface (“geodesics”) and simulating how heat dissipates over it (“heat diffusion”). It could also help artists and computer graphics scientists create physics-breaking designs in multiple dimensions.

Lead author and MIT PhD student Ana Dodik aims to design computer graphics tools that aren’t limited to replicating reality, enabling artists to express their intent independently of whether a shape can be realized in the physical world. “Using Meschers, we’ve unlocked a new class of shapes for artists to work with on the computer,” she says. “They could also help perception scientists understand the point at which an object truly becomes impossible.”

Dodik and her colleagues will present their paper at the SIGGRAPH conference in August.

Making impossible objects possible

Impossible objects can’t be fully replicated in 3D. Their constituent parts often look plausible, but these parts don’t glue together properly when assembled in 3D. But what can be computationally imitated, as the CSAIL researchers found out, is the process of how we perceive these shapes.

Take the Penrose Triangle, for instance. The object as a whole is physically impossible because the depths don’t “add up,” but we can recognize real-world 3D shapes (like its three L-shaped corners) within it. These smaller regions can be realized in 3D — a property called “local consistency” — but when we try to assemble them together, they don’t form a globally consistent shape.

The Meschers approach models’ locally consistent regions without forcing them to be globally consistent, piecing together an Escher-esque structure. Behind the scenes, Meschers represents impossible objects as if we know their x and y coordinates in the image, as well as differences in z coordinates (depth) between neighboring pixels; the tool uses these differences in depth to reason about impossible objects indirectly.

The many uses of Meschers

In addition to rendering impossible objects, Meschers can subdivide their structures into smaller shapes for more precise geometry calculations and smoothing operations. This process enabled the researchers to reduce visual imperfections of impossible shapes, such as a red heart outline they thinned out.

The researchers also tested their tool on an “impossibagel,” where a bagel is shaded in a physically impossible way. Meschers helped Dodik and her colleagues simulate heat diffusion and calculate geodesic distances between different points of the model.

“Imagine you’re an ant traversing this bagel, and you want to know how long it’ll take you to get across, for example,” says Dodik. “In the same way, our tool could help mathematicians analyze the underlying geometry of impossible shapes up close, much like how we study real-world ones.”

Much like a magician, the tool can create optical illusions out of otherwise practical objects, making it easier for computer graphics artists to create impossible objects. It can also use “inverse rendering” tools to convert drawings and images of impossible objects into high-dimensional designs. 

“Meschers demonstrates how computer graphics tools don’t have to be constrained by the rules of physical reality,” says senior author Justin Solomon, associate professor of electrical engineering and computer science and leader of the CSAIL Geometric Data Processing Group. “Incredibly, artists using Meschers can reason about shapes that we will never find in the real world.”

Meschers can also aid computer graphics artists with tweaking the shading of their creations, while still preserving an optical illusion. This versatility would allow creatives to change the lighting of their art to depict a wider variety of scenes (like a sunrise or sunset) — as Meschers demonstrated by relighting a model of a dog on a skateboard.

Despite its versatility, Meschers is just the start for Dodik and her colleagues. The team is considering designing an interface to make the tool easier to use while building more elaborate scenes. They’re also working with perception scientists to see how the computer graphics tool can be used more broadly.

Dodik and Solomon wrote the paper with CSAIL affiliates Isabella Yu ’24, SM ’25; PhD student Kartik Chandra SM ’23; MIT professors Jonathan Ragan-Kelley and Joshua Tenenbaum; and MIT Assistant Professor Vincent Sitzmann. 

Their work was supported, in part, by the MIT Presidential Fellowship, the Mathworks Fellowship, the Hertz Foundation, the U.S. National Science Foundation, the Schmidt Sciences AI2050 fellowship, MIT Quest for Intelligence, the U.S. Army Research Office, U.S. Air Force Office of Scientific Research, SystemsThatLearn@CSAIL initiative, Google, the MIT–IBM Watson AI Laboratory, from the Toyota–CSAIL Joint Research Center, Adobe Systems, the Singapore Defence Science and Technology Agency, and the U.S. Intelligence Advanced Research Projects Activity.

Youssef Marzouk ’97, SM ’99, PhD ’04, the Breene M. Kerr (1951) Professor in the Department of Aeronautics and Astronautics (AeroAstro) at MIT, has been appointed associate dean of the MIT Schwarzman College of Computing, effective July 1.

Marzouk, who has served as co-director of the Center for Computational Science and Engineering (CCSE) since 2018, will work in his new role to foster a stronger community among bilingual computing faculty across MIT. A key aspect of this work will be providing additional structure and support for faculty members who have been hired into shared positions in departments and the college.

Shared faculty at MIT represent a new generation of scholars whose research and teaching integrate the forefront of computing and another discipline (positions that were initially envisioned as “bridge faculty” in the 2019 Provost’s Task Force reports). Since 2021, the MIT Schwarzman College of Computing has been steadily growing this cohort. In collaboration with 24 departments across the Institute, 20 faculty have been hired in shared positions: three in the School of Architecture and Planning; four in the School of Engineering; seven in the School of Humanities, Arts, and Social Sciences; four in the School of Science; and two in the MIT Sloan School of Management.

“Youssef’s experience leading cross-cutting efforts in research and education in CCSE is of direct relevance to the broader goal of bringing MIT’s computing bilinguals together in meaningful ways. His insights and collaborative spirit position him to make a lasting impact in this role. We are delighted to welcome him to this new leadership position in the college,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren Professor of Electrical Engineering and Computer Science.

“I’m excited that Youssef has agreed to take on this important role in the college. His thoughtful approach and nuanced understanding of MIT’s academic landscape make him ideally suited to support our shared faculty community. I look forward to working closely with him,” says Asu Ozdaglar, deputy dean of the MIT Schwarzman College of Computing, head of the Department of Electrical Engineering and Computer Science (EECS), and the MathWorks Professor of EECS.

Marzouk’s research interests lie at the intersection of computational mathematics, statistical inference, and physical modeling. He and his students develop and analyze new methodologies for uncertainty quantification, Bayesian computation, and machine learning in complex physical systems. His recent work has centered on algorithms for data assimilation and inverse problems; high-dimensional learning and surrogate modeling; optimal experimental design; and transportation of measure as a tool for statistical inference and generative modeling. He is strongly motivated by the interplay between theory, methods, and diverse applications, and has collaborated with other researchers at MIT on topics ranging from materials science to fusion energy to the geosciences.

In 2018, he was appointed co-director of CCSE with Nicolas Hadjiconstantinou, the Quentin Berg Professor of Mechanical Engineering. An interdisciplinary research and education center dedicated to advancing innovative computational methods and applications, CCSE became one of the academic units of the MIT Schwarzman College of Computing when it formally launched in 2020.

CCSE has grown significantly under Marzouk and Hadjiconstantinou’s leadership. Most recently, they spearheaded the design and launch of the center’s new standalone PhD program in computational science and engineering, which will welcome its second cohort in September. Collectively, CCSE’s standalone and interdisciplinary PhD programs currently enroll more than 70 graduate students.

Marzouk is also a principal investigator in the MIT Laboratory for Information and Decision Systems, and a core member of MIT’s Statistics and Data Science Center.

Among his many honors and awards, he was named a fellow of the Society for Industrial and Applied Mathematics (SIAM) in 2025. He was elected associate fellow of the American Institute of Aeronautics and Astronautics (AIAA) in 2018 and received the National Academy of Engineering Frontiers of Engineering Award in 2012, the MIT Junior Bose Award for Teaching Excellence in 2012, and the DOE Early Career Research Award in 2010. His recent external engagement includes service on multiple journal editorial boards; co-chairing major SIAM conferences and elected service on various SIAM committees; leadership of scientific advisory boards, including that of the Institute for Computational and Experimental Research in Mathematics (ICERM); and organizing many other international programs and workshops.

At MIT, in addition to co-directing CCSE, Marzouk has served as both graduate and undergraduate officer of the Department of AeroAstro. He also leads the MIT Center for the Exascale Simulation of Materials in Extreme Environments, an interdisciplinary computing effort sponsored by the U.S. Department of Energy’s Predictive Science Academic Alliance program.

Marzouk received his bachelor’s, master’s, and doctoral degrees from MIT. He spent four years at Sandia National Laboratories, as a Truman Fellow and a member of the technical staff, before joining the MIT faculty in 2009.

In the push to shrink and enhance technologies that control light, MIT researchers have unveiled a new platform that pushes the limits of modern optics through nanophotonics, the manipulation of light on the nanoscale, or billionths of a meter.

The result is a class of ultracompact optical devices that are not only smaller and more efficient than existing technologies, but also dynamically tunable, or switchable, from one optical mode to another. Until now, this has been an elusive combination in nanophotonics.

The work is reported in the July 8 issue of Nature Photonics.

“This work marks a significant step toward a future in which nanophotonic devices are not only compact and efficient, but also reprogrammable and adaptive, capable of dynamically responding to external inputs. The  marriage of emerging quantum materials and established nanophotonics architectures will surely bring advances to both fields,” says Riccardo Comin, MIT’s Class of 1947 Career Development Associate Professor of Physics and leader of the work. Comin is also affiliated with MIT’s Materials Research Laboratory and Research Laboratory of Electronics (RLE).

Comin’s colleagues on the work are Ahmet Kemal Demir, an MIT graduate student in physics; Luca Nessi, a former MIT postdoc who is now a postdoc at Politecnico di Milano; Sachin Vaidya, a postdoc in RLE; Connor A. Occhialini PhD ’24, who is now a postdoc at Columbia University; and Marin Soljačić, the Cecil and Ida Green Professor of Physics at MIT.

Demir and Nessi are co-first authors of the Nature Photonics paper.

Toward new nanophotonic materials

Nanophotonics has traditionally relied on materials like silicon, silicon nitride, or titanium dioxide. These are the building blocks of devices that guide and confine light using structures such as waveguides, resonators, and photonic crystals. The latter are periodic arrangements of materials that control how light propagates, much like how a semiconductor crystal affects electron motion.

While highly effective, these materials are constrained by two major limitations. The first involves their refractive indices. These are a measure of how strongly a material interacts with light; the higher the refractive index, the more the material “grabs” or interacts with the light, bending it more sharply and slowing it down more. The refractive indices of silicon and other traditional nanophotonic materials are often modest, which limits how tightly light can be confined and how small optical devices can be made.

A second major limitation of traditional nanophotonic materials: once a structure is fabricated, its optical behavior is essentially fixed. There is usually no way to significantly reconfigure how it responds to light without physically altering it. “Tunability is essential for many next-gen photonics applications, enabling adaptive imaging, precision sensing, reconfigurable light sources, and trainable optical neural networks,” says Vaidya.

Introducing chromium sulfide bromide

These are the longstanding challenges that chromium sulfide bromide (CrSBr) is poised to solve. CrSBr is a layered quantum material with a rare combination of magnetic order and strong optical response. Central to its unique optical properties are excitons: quasiparticles formed when a material absorbs light and an electron is excited, leaving behind a positively charged “hole.” The electron and hole remain bound together by electrostatic attraction, forming a sort of neutral particle that can strongly interact with light.

In CrSBr, excitons dominate the optical response and are highly sensitive to magnetic fields, which means they can be manipulated using external controls.

Because of these excitons, CrSBr exhibits an exceptionally large refractive index that allows researchers to sculpt the material to fabricate optical structures like photonic crystals that are up to an order of magnitude thinner than those made from traditional materials. “We can make optical structures as thin as 6 nanometers, or just seven layers of atoms stacked on top of each other,” says Demir.

And crucially, by applying a modest magnetic field, the MIT researchers were able to continuously and reversibly switch the optical mode. In other words, they demonstrated the ability to dynamically change how light flows through the nanostructure, all without any moving parts or changes in temperature. “This degree of control is enabled by a giant, magnetically induced shift in the refractive index, far beyond what is typically achievable in established photonic materials,” says Demir.

In fact, the interaction between light and excitons in CrSBr is so strong that it leads to the formation of polaritons, hybrid light-matter particles that inherit properties from both components. These polaritons enable new forms of photonic behavior, such as enhanced nonlinearities and new regimes of quantum light transport. And unlike conventional systems that require external optical cavities to reach this regime, CrSBr supports polaritons intrinsically.

While this demonstration uses standalone CrSBr flakes, the material can also be integrated into existing photonic platforms, such as integrated photonic circuits. This makes CrSBr immediately relevant to real-world applications, where it can serve as a tunable layer or component in otherwise passive devices.

The MIT results were achieved at very cold temperatures of up to 132 kelvins (-222 degrees Fahrenheit). Although this is below room temperature, there are compelling use cases, such as quantum simulation, nonlinear optics, and reconfigurable polaritonic platforms, where the unparalleled tunability of CrSBr could justify operation in cryogenic environments.

In other words, says Demir, “CrSBr is so unique with respect to other common materials that even going down to cryogenic temperatures will be worth the trouble, hopefully.”

That said, the team is also exploring related materials with higher magnetic ordering temperatures to enable similar functionality at more accessible conditions.

This work was supported by the U.S. Department of Energy, the U.S. Army Research Office, and a MathWorks Science Fellowship. The work was performed in part at MIT.nano.