Bridging medical realities in the study of technology and health

A few weeks ago, Amy Moran-Thomas and 20 students in her class 21A.311 (The Social Lives of Medical Objects) were gathered around a glucose meter, a jar of test strips, and various spare medical parts in the MIT Museum seminar room, talking about how to make them work better.

The class had just heard a presentation from the president of the Belize Diabetes Association in Dangriga, Norma Flores, a nurse whose hospital had recently received a huge shipment of insulin that, although durable in theory, seemed to have spoiled in a heat wave. Flores and the students discussed whether scientists could develop temperature-stable insulin and design repairable glucose meters and other technologies for hospitals worldwide.

“Whenever people keep saying they are concerned about an issue, but the medical literature doesn’t describe it yet, there is a key question about what’s happening,” says Moran-Thomas. “Ethnography can help us learn about it.”

For Moran-Thomas, an MIT anthropologist, that class session was a way of connecting people and ideas that are too often overlooked. Flores was a central figure in Moran-Thomas’ 2019 book, “Traveling with Sugar: Chronicles of a Global Epidemic,” about diabetes in Belize and the failures of medical technology designed to treat it. (At the end of class, Flores surprised Moran-Thomas with a framed commendation from the Belize Diabetes Association for their nearly 20 years of work together.)

That approach informs all of Moran-Thomas’ work. Currently she is co-leading a group working on a project called the “Sugar Atlas,” mapping the social and economic dimensions of diabetes in the Caribbean, in tandem with scholars Nicole Charles of the University of Toronto and Tonya Haynes of the University of West Indies. Moran-Thomas has also spent more than a decade following the case of notorious medical experiments that took place in Guatemala in the 1940s, the subject of a recent paper she published with Susan Reverby of Wellesley College.

Closer to home, Moran-Thomas is working on a book about how energy extraction affects chronic conditions and mental health in her native Pennsylvania, at a time of increasing hospital closures. As part of this research, she has been working with MIT seismologist William Frank to develop low-cost sensors that people can use to measure the impact of industrial activity on their home neighborhoods. The research team was recently awarded a grant by the MIT Human Insight Collaborative (MITHIC) for the work. And with another MITHIC grant, Moran-Thomas and several colleagues are working to create a new “Health and Society” educational program at MIT.

“A through line in my work is the question about how to put people at the center of health and medicine,” says Moran-Thomas, an associate professor in MIT’s anthropology program. “Because that’s not how it feels to most people in the world. Care technologies that work for everybody, and health technologies in relation to chronic disease, connect all these different projects.”

The work Moran-Thomas may be best known for occurred in 2020, during the Covid-19 pandemic, when her research recovered an array of neglected clinical studies showing that oximeters functioned differently depending on the skin color of patients. After she published a piece about it in the Boston Review, further hospital studies by physicians who found the essay confirmed a pattern of disproportionately inaccurate readings, leading to subsequent efforts to improve the technology — all steming from her careful, patient-centric approach.

“What anthropology has to offer the world, and other knowledge systems, is the insights of people that might be missing from many accounts, and highlighting these stories that are getting left out,” Moran-Thomas says. “Those are not footnotes, but the central thing to follow. And those histories are also alive in the material world around us.”

Thinking across medical and climate technologies

After growing up in Pennsylvania, Moran-Thomas majored in literature while earning her BA from American University. She decided to pursue ethnographic research as a graduate student, and entered Princeton University’s program in anthropology, earning an MA in 2008 and her PhD in 2012. After postdoc stints at Princeton and Brown University, Moran-Thomas joined the MIT faculty in 2015.

At Princeton, Moran-Thomas’ dissertation research examined the diabetes epidemic in Belize, forming the basis of her first book, “Traveling with Sugar,” whose title is an expression in Belize for living with diabetes. As she chronicles in the book, plantation-era changes that undermined indigenous agriculture, among other things, contributed to a local economy that made diets sugar-heavy, while medical technologies are often unreliable or ill-suited to local conditions. The book also traces breakdowns in care technologies, such as prosthetic limbs (often sought after diabetes-linked amputations), glucose meters, hyperbaric chambers, insulin supply chains, dialysis machines, and pain management technologies.

“Traveling with Sugar” also develops a critique that has become a theme of Moran-Thomas’ work: that society often shifts the blame for illness onto patients while minimizing the larger-scale factors affecting everyday health.

“There can be this focus on exclusively prevention without care, the implicit assumption that patients need to act differently,” Moran-Thomas says. “Blame falls on individuals and families instead of a focus on other questions. Why are these technologies always breaking down? How are they designed, and by whom, for whom? What role is history playing in the present? And how are people trying to remake those structures?”

Those issues are highlighted in Moran-Thomas’ ongoing project, “Sugar Atlas: Counter-Mapping Diabetes from the Caribbean,” which is backed by a two-year Digital Justice Seed Grant from the American Council of Learned Societies. Whereas international organizations tend to lump North America and the Caribbean together when tracking diabetes, this project zooms in on specific aspects of the disease and its historical and structural contributors in the Caribbean, such as the distance people must travel to buy vegetables, their proximity to insulin supplies, and the ways climate change is affecting sea life and fishing practices.

“We’re trying to create a community platform offering a different vision of these conditions,” Moran-Thomas says of the effort to map otherwise unrecorded aspects of the global diabetes epidemic, while tracing mutual aid networks and people’s “arts of care” in the present.

Better design for common devices

Following her research in Belize, where glucose meters were prone to breaking, Moran-Thomas began taking a more active focus on the design of medical technology. At MIT, she began co-teaching a course with tech innovator Jose Gomez-Marquez, 21A.311 (The Social Lives of Medical Objects). The idea was to get students to think about device design that could lead to more durable, fixable, and equitable products.

In turn, Moran-Thomas’ interest in devices led her to question the pulse oximeter readings she started seeing first-hand during the Covid-19 pandemic. Pulse oximeters measure oxygen saturation levels in patients and are a part of even routine appointment check-ins. They work visually, casting beams of light to measure the color of hemoglobin, which varies depending on how much oxygen it contains. 

After firsthand encounters with the sensors led to more research, Moran-Thomas learned that some medical professionals had lingering, unanswered questions about pulse oximeters and they way they were calibrated. After she published her essay in the Boston Review, arguing for more data collection, medical researchers examined the issue more closely, finding that patients with darker skin were about three times more likely to have erroneous blood-oxygen readings than patients with lighter skin. Ultimately, an FDA panel recommended changes to the devices.

“A lot of my work has been learning about health and medicine technologies from the perspectives of patients, families, and nurses, rather than beginning with engineers and doctors,” Moran-Thomas says. “Those two projects, about blood sugar and blood oxygen, were about the shortcomings of those devices and how they could be improved. Those are perspectives I can highlight in hopes others will pick up on them and make other kinds of designs and policies possible.”

Moran-Thomas’ interest in device design has continued with her current book project, about the chronic health effects of energy production in Pennsylvania. She has worked with MIT seismologist William Frank, of the Department of Earth, Atmospheric and Planetary Sciences, to construct an inexpensive meter people can use to measure shaking in their homes caused by industrial activities. (After colleagues in western Pennsylvania reached out with seismic concerns, Moran-Thomas first got the idea to contact Frank after reading about his work in MIT News, incidentally).

The effort is also inspired by guidance from community leaders based at the Center for Coalfield Justice in western Pennsylvania. The research team has received a MITHIC Connectivity grant for their project, “Seismic Collaboratory: Rural Health, Missing Science, and Communicating the Chronic Impacts of Extraction.”

“I’ve met people who have been told by their doctors they must have vertigo, while they thought the walls of their house were really shaking,” Moran-Thomas says. “In a case like that, the device you need is not in the clinic, it’s a monitor at home.”

The book, overall, will examine the effects of energy production on chronic disease and mental health issues in Pennsylvania, something exacerbated by more hospitals being shuttered in the state.

Moran-Thomas is simultaneously working with several co-investigators to create the “Health and Society” educational program at MIT, including Katharina Ribbeck, Erica James, Aleshia Carlsen-Bryan, and Dina Asfaha. Their work was recently awarded an Education Innovation Seed Grant from MITHIC.

From small devices to large-scale changes in health care systems, from the U.S. to other regions, Moran-Thomas remains focused on a core set of issues about how to improve and broaden health care — and lessen the need for it in the first place.

“Thinking across scales is something that’s really useful about anthropology,” Moran-Thomas says. “Even one medical device is a tiny piece of a bigger infrastructure. In order to study that technology or device or sensor, you have to understand the bigger infrastructure it’s attached to, and that there are people involved in all parts of it.” 

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Weekends@MIT offers connection through varied activities

Weekends at MIT are often a time for students to catch up on sleep or finish p-sets, lab work, and other school assignments. But for more than two decades, through a student-driven initiative supported by the Division of Student Life (DSL), students have been able to find welcoming activities designed to build community on Friday and Saturday nights through Weekends@MIT. All events are open to both graduate and undergraduate students.

At the heart of Weekends@MIT is a leadership team within the Wellbeing Ambassadors program. Ten leadership team members plan and host a variety of events from 9 to 11 p.m. in the MIT Wellbeing Lab, transforming the space into a hub for connection and creativity. While DSL staff provide advising, logistical support, and funding, event ideas come from students. Club members are committed to facilitating student social activities, all while increasing health awareness.

Student-led activities

Student ownership is intentional, says Robyn Priest, an assistant dean in the Division of Student Life. “All the ideas for activities come from the students. Leaders brainstorm themes, vote on their favorite concepts, and spearhead events in small teams. The only criterion is that it be substance-free. The students involved are dedicated, and the time commitment can be significant, so they are paid. But our students consistently step up, motivated by the opportunity to create experiences for their peers.”

Past events have included craft nights with boba tea, yoga, trivia competitions, bracelet-making workshops, waffle nights with customizable toppings, and even Spooky Skate, a Halloween costume ice-skating event hosted by the club in the Z Center.

Priest notes that just this past fall semester, more than 2,000 students attended the Friday night events, with many programs designed as drop-in experiences so students can participate around their busy schedules.

“I joined Weekends@MIT because I really liked the idea of helping organize activities on campus that promoted well-being for students and provided them with chill events that they can attend to build community and feel good on Friday nights,” says junior Emily Crespin Guerra.

Senior Ruting Hung adds, “I wanted to become more involved in promoting wellness on campus. Since then, I've found that it has also served as a way for me to recharge after a long week.”

Expanding Saturday events

Saturdays bring additional variety through collaborations with student clubs and groups. Organizations can apply for funding — typically several hundred dollars — to host events between 9 and 11 p.m. that are open to all students.

Undergraduate and graduate organizations, cultural groups, and hobby-based clubs have all contributed to programming. The partnerships also introduce new audiences to the Wellbeing Lab, helping the space become a familiar and welcoming destination across campus communities.

Connecting the campus through communication

Another key component of Weekends@MIT is a weekly newsletter distributed to thousands of students. The newsletter highlights upcoming programs in the Wellbeing Lab, along with other campus events that align with the initiative’s goals of connection and community without alcohol.

First-year student Vivian Dinh notes, “I love how the events provide a fun escape from the stress of classes and problem sets. The Wellbeing Lab is such a nice facility on campus for students to relax and enjoy themselves.”

A long tradition, evolving for the future

The current initiative builds on a long history of student-led weekend programming that began more than 20 years ago. Over time, the effort has evolved — from early safety campaigns to today’s comprehensive model focused on well-being, belonging, and social connection — but the core idea remains the same: students creating healthy spaces for other students.

Looking ahead, Weekends@MIT aims to continue expanding collaborations and exploring new ways to bring communities together on weekends. Additional events for this semester include: pupusas; blitz chess tournament with the Chess Club; craft night; movies and waffles; mocktails and latte art; a Bob Ross paint night, and much more.

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MIT and Hasso Plattner Institute establish collaborative hub for AI and creativity

The following is a joint announcement from the MIT School of Architecture and Planning, MIT Schwarzman College of Computing, Hasso Plattner Institute, and Hasso Plattner Foundation.

The MIT Morningside Academy for Design (MAD), MIT Schwarzman College of Computing, Hasso Plattner Institute (HPI), and Hasso Plattner Foundation celebrated the launch of the MIT and HPI AI and Creativity Hub (MHACH) at a signing ceremony this week. This 10-year initiative aims to deepen ties between computing and design as advances in artificial intelligence are reshaping how ideas are conceived and shared.

Funded by the Hasso Plattner Foundation, MIT and HPI will work together to foster collaborative interdisciplinary research and support a portfolio of educational programs, fellowships, and faculty engagement focused on AI and creativity, expanding scholarly inquiry into AI applications across disciplines, industries, and societal challenges. The collaboration begins with an inaugural two-day workshop March 19-20 at MIT, bringing together faculty, students, and researchers to set early priorities.

“As we hear from our faculty, as the Information Age gives way to an era of imagination, we expect a new emphasis on human creativity,” reflects MIT President Sally Kornbluth. “Through this collaboration, MIT and HPI are creating a shared space where students and faculty will come together across disciplines to explore new ideas, experiment with emerging tools, and invent new frontiers at the intersection of human creativity and AI.”

“The best minds need the right environment to do their most creative work,” says Rouven Westphal, from the Hasso Plattner Foundation. “When HPI and MIT come together across disciplines and borders, they create exactly that. The Hasso Plattner Foundation is committed to supporting this collaboration for the long term, building on Hasso Plattner’s vision of uniting technological excellence with human-centered design and creativity.”

Deepening collaboration at the intersection of technology, creativity, and societal impact

Building on the success of the Hasso Plattner Institute-MIT Research Program on Designing for Sustainability, established in 2022 between MIT MAD and HPI, the new MHACH hub represents a commitment to deepen collaboration at the intersection of technology, creativity, and societal impact.

“MIT and HPI share a common commitment to turning scientific excellence into real-world impact. Through this collaboration, we will create an environment where students and researchers from both sides of the Atlantic can work together, experiment across disciplines, and learn from one another — at a time when artificial intelligence is set to profoundly shape our lives. We are convinced that this collaboration will generate ideas with impact far beyond both institutions and inspire international cooperation and innovation,” says Professor Tobias Friedrich, dean and managing director of the Hasso Plattner Institute.

“HPI and MIT exist at the nexus of technology and creativity. Expanding this dynamic relationship will generate new paths for the infusion of AI, design, and creativity, enabling students, faculty, and researchers to dream and discover novel solutions, moving more quickly than ever from idea to implementation. MAD was established to connect thinkers across and beyond the Institute, and this new era of collaboration with HPI advances that mission on a global scale,” comments Hashim Sarkis, dean of the MIT School of Architecture and Planning and the Elizabeth and James Killian (1926) Professor.

Academic leadership from MIT and HPI will jointly shape the hub’s research and teaching agenda. Based in Potsdam, Germany, HPI is a center of excellence for digital engineering advancing research, education, and societal transfer in IT systems engineering, data engineering, cybersecurity, entrepreneurship, and digital health. Through its globally recognized HPI d-school and pioneering work in design thinking methodology, HPI brings a distinctive perspective on human-centered innovation to the collaboration, alongside a strong record in AI and data science research and technology transfer.

Expanding research and education on AI and creativity

The efforts of this multifaceted initiative are intended to foster a dynamic academic community spanning MIT and HPI, anchored by Hasso Plattner–named professorships and graduate fellowships whose recipients will be actively engaged in the hub. The long-term framework is designed to provide continuity for faculty appointments, doctoral training, and cross-campus research.

The agreement also includes the development of classes and educational programs in areas of shared AI focus, along with expanded experiential opportunities through AI-focused workshops, hackathons, and summer exchanges. A steering committee composed of representatives from the MIT School of Architecture and Planning, MIT Schwarzman College of Computing, and Hasso Plattner Institute will facilitate the shared governance of MHACH.

“Creativity has always been about extending human capability. At its core, this collaboration asks what it truly means to create something new. The question isn’t whether AI diminishes creativity, but how new forms of intelligence can deepen and enrich that process. Our goal is to explore that intersection with rigor and build a cross-disciplinary scholarly and research community that shapes how AI supports the creation of new ideas and knowledge,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Ellis Warren (1894) Professor of Electrical Engineering and Computer Science.

This collaboration is made possible by the Hasso Plattner Foundation’s long-term philanthropic commitment to institutions that connect technological innovation with design thinking and education. The Hasso Plattner Foundation has played a central role in establishing and supporting institutions such as the Hasso Plattner Institute and international design thinking programs that bridge disciplines and geographies.

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Preserving Keres

Growing up in the village of Kewa — located between Santa Fe and Albuquerque in New Mexico — William Pacheco, a member of the Santo Domingo Pueblo, learned the value of his language, its history, and the traditions it carries.

“We speak Keres, a language isolate found in seven villages and communities in central New Mexico,” he says. “It’s an endangered language with fewer than 10,000 speakers.” The Pueblos’ conception of ‘language,’ according to Pacheco, evokes the idea that speaking “comes from deep within.”

Pacheco is a graduate student in the MIT Indigenous Languages Initiative, a special master’s program in linguistics for members of communities whose languages are threatened. The two-year program provides its graduates with the linguistic knowledge to help them keep their communities’ languages alive. The initiative also offers expanded opportunities for students and faculty to become involved in Indigenous and endangered languages, working with both native speaker linguists in the master’s program and outside groups, ideas that appealed to him.

“There’s some complexity to our language that defies traditional instruction,” says Pacheco, who will complete his studies this spring. “I want to develop the linguistic tools I need to improve my understanding of its construction and how best to teach and preserve it.” Pacheco is keenly aware of cultural differences in how language transmission occurs. Language, he believes, evolves over time and is best learned experientially; the Western model of language learning prioritizes immediacy and test-taking.

A variety of factors complicate efforts to preserve and potentially teach Keres. Each of the villages where it’s spoken has its own distinct dialect. These dialects are mutually intelligible to various degrees based on where they’re being spoken. Additionally, the last three decades have seen a significant increase in English usage by young Pueblos, which further endangers Keres’ existence.

Furthermore, Keres isn’t a written language. For centuries, the Pueblo have relied on daily use within their homes and communities to maintain its vitality. “The community doesn’t want it written,” Pacheco says. 

Contact with the wider world has previously imperiled Indigenous ideas, an outcome Pacheco wants to avoid. “We believe [Keres] is a form of intellectual property, a tradition and artifact that is best served by empowering our people to preserve it,” he says.

From the Southwest to MIT

While he’s now passionate about linguistics, languages weren’t Pacheco’s first choice when considering an educational path. “I always admired [MIT alumnus and Nobel laureate] Richard Feynman,” he recalls. “I wanted to study physics.”

After earning an undergraduate degree from the University of New Mexico, Pacheco, who’d been working as a K-12 educator, began efforts to preserve Keres, increasing the language’s vitality and preserving its usefulness for, and value to, future generations. He sought permission and certification from the tribe to teach the language at the Santa Fe Indian School, an off-reservation boarding school. He soon discovered that a traditional Western approach to language learning wouldn’t suffice.

“Students weren’t taking the course to be scholars of the language; they wanted to learn it to build community and create opportunities to connect with elders,” Pacheco says. It was students’ advocacy, he notes, that led to the Keres learning initiative. While designing the course, however, he found gaps in his knowledge that led him to consider graduate study. 

“There are fascinating idiosyncrasies in Keres, including, for example, verb morphology — the ways in which verbs and verb sounds change,” he notes. “I wasn’t sure about how to teach them.” He sought to improve his understanding and ability by earning a master’s degree in learning design, innovation, and technology from Harvard University. While completing his studies there, he had another burst of inspiration.

“I thought a background in linguistics would prove useful,” he says. “An advisor told me about the Indigenous Languages Initiative at MIT and recommended I apply.” Pacheco knew of Professor Emeritus Noam Chomsky’s pioneering work in generative linguistics at the Institute and sought to learn more about the field’s potential to help him become a better, more effective educator and linguist. 

Upon arriving at MIT in 2024, Pacheco found himself embraced by faculty and students alike. “[MIT linguists] Adam Albright and Norvin Richards have been wonderfully supportive mentors, offering enthusiasm and expertise” he says. “I’ve benefited from MIT’s approach to linguistics and its use of scientific inquiry as a tool to explore language.” Engaging with other students working to preserve languages at risk of extinction continues to drive his work.

“MIT continually encourages us to use its resources, to collaborate, and to help one another find solutions to our unique challenges,” he says. “Networking, gathering good ideas, and having access to professors and students from a variety of disciplines is incredibly valuable.” 

MIT’s scholars, Pacheco says, are experienced with Indigenous language learning, education, and pedagogy.

Developing an organized approach to Keres research and instruction

While gratified that his work created opportunities for him to preserve and teach Keres, Pacheco marvels at his path to the Institute and its impact on his life. “It was my language, not my interest in physics, which led me to Harvard and MIT,” he says. “How did I end up at these places?”

An advantage of language and linguistics education at MIT is the rigor with which it explores language acquisition modeling and allows for alternatives to established systems. Pacheco is after new ideas for Keres language learning and education, working to develop an effective course based on generative linguistics that both preserves the Pueblos’ approach to community and offers an educational model students are likely to embrace. He’s already had opportunities to test novel theories and practices as an educator back home. 

“I was teaching students to use Keres as a programming tool,” he says. “We modeled a robot as a member of the community navigating a maze, and students would have to teach it to accept commands in Keres.” 

Pacheco also wants to explore community-centered language issues. He wants to standardize the development and education of community linguists, creating a cohort of scholars trained to use the tools he designs that are deeply invested in Keres’ preservation and instruction.

“We want to drive inquiries into Keres and how it’s taught,” he says, “while also centering Indigenous knowledge systems and expanding access to linguistics study for Indigenous scholars.”

Pacheco believes there’s value in exposing scholars and communities to the cultural and ideological exchanges he’s enjoyed between the sciences, humanities, Indigenous ideas, and experiences. “Indigenous scholars exist at MIT,” he says. “We’re here, and the Institute’s support helps preserve languages like Keres as important communal and cultural artifacts.” 

Pacheco is grateful for the opportunities his research at MIT have afforded him. While his education as a linguist and scholar continues, Pacheco’s community, culture, and support for Keres language learning remain top priorities.

“I want to amplify the impact in tribal language policy and Indigenous-centered education,” he says. “Language, its study, and its transmission is both science and art.”

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Improving cartilage repair through cell therapy

Researchers have developed a new method for monitoring iron flux — the movement and rate at which cells take in, store, use and release iron — in stem cells known as mesenchymal stromal cells (MSCs). The system can provide insights within a minute about a cell’s ability to grow cartilage tissue for cartilage repair. 

The breakthrough offers a promising pathway toward more consistent and efficient manufacturing of high‑quality MSCs for regenerative therapies to treat joint diseases such as osteoarthritis, chronic joint degeneration conditions, and cartilage injuries.

The work was led by researchers from the Critical Analytics for Manufacturing Personalized-Medicine (CAMP) group within the Singapore-MIT Alliance for Research and Technology (SMART), and was supported by the SMART Antimicrobial Resistance (AMR) research group, in collaboration with MIT and the National University of Singapore (NUS).

A paper describing the work, “Cellular iron flux measurement by micromagnetic resonance relaxometry as a critical quality attribute of mesenchymal stromal cells,” was published in February in the journal Stem Cells Translational Medicine.

Regenerative therapies hold significant promise for patients with the potential to repair damaged tissues rather than simply manage symptoms. However, one of the biggest challenges in bringing these therapies to patients lies in the unpredictable quality of the MSC’s chondrogenic potential — a cell’s ability to develop and form cartilage tissue — during the in vitro manufacturing process.

Even when grown under controlled laboratory conditions, MSCs are prone to losing some of their potential and ability to form cartilage tissue, leading to inconsistent cartilage repair outcomes due to the varying quality of MSC batches. Existing tests that evaluate the quality of MSCs’ cartilage‑forming potential are destructive in nature, which causes irreversible damage to the cells being tested and renders them unusable for further therapeutic or manufacturing purposes.

In addition, the tests require a prolonged — up to 21-day — period for cells to grow. This slows decision‑making, extends production timelines, and can hinder the timely translation of MSC-based therapies into clinical use and delay treatment for patients. As MSCs can lose chondrogenic potential during this process, early assessment is essential for manufacturers to determine whether a batch should be continued or discontinued. Hence, there is a need for a reliable and rapid method to predict MSCs’ chondrogenic potential during the cell manufacturing process.

The new developement represents a rapid, non-destructive method to monitor iron flux in MSCs by measuring iron changes in spent media — residual components in the culture medium after cell growth. Using an inexpensive benchtop micromagnetic resonance relaxometry (µMRR) device, the approach enables real‑time monitoring of cellular iron changes without damaging the cells. The inexpensive µMRR device can be easily integrated into existing laboratories and manufacturing workflows, enabling routine, real‑time quality monitoring without significant infrastructure or cost barriers.

Iron homeostasis is a critical process that maintains normal levels of iron for cell function, maintaining the balance between providing sufficient iron for essential processes, while preventing toxic accumulation. The study found that iron homeostasis is highly correlated with the MSC’s chondrogenic potential, where significant iron uptake and accumulation will reduce the cell’s ability to form cartilage. The researchers also found that supplementing the cell growth process with ascorbic acid (AA) helps regulate iron homeostasis by limiting iron flux, thereby improving the MSC’s chondrogenic potential.

Using this novel method, spent media are collected as samples and treated with AA. The µMRR device is then used to track and provide real-time insights into small iron concentration changes within the spent media. These iron concentration changes reflect how MSCs take up and release iron and can provide an early indicator of whether a batch is likely to succeed in forming good cartilage.

These findings allow manufacturers to not only monitor MSCs quality for cartilage repair in real-time, but also to assess when, and to what extent, interventions such as AA supplementation are likely to be beneficial - supporting efficient manufacturing of more effective and consistent MSC‑based therapies.

“One of the key challenges in cartilage regeneration is the inability to reliably predict whether MSCs will retain their chondrogenic potential during manufacturing. Our study addresses this by introducing a rapid, non-destructive method to monitor iron flux dynamics as a novel critical quality attribute (CQA) of MSCs' chondrogenic capacity. This approach enables early identification of suboptimal cell batches during culture, enhancing quality control efficiency, reducing manufacturing costs, and accelerating clinical translation,” says Yanmeng Yang, CAMP postdoc and first author of the paper.

“Our research sheds light on a fundamental biological process that, until now, has been extremely difficult to measure. By monitoring iron flux in real-time without destroying the cells, we can gain actionable insights into a cell batch’s chondrogenic potential, which allows for early decision-making during the manufacturing process. The findings support µMRR‑based iron monitoring as an effective quality control strategy for MSC-based therapy manufacturing, paving the way for more consistent and clinically viable regenerative medicine for cartilage regeneration,” says MIT Professor Jongyoon Han, co-head CAMP PI, AMP PI, and corresponding author of the paper.

This method represents a promising step toward improving manufacturing consistency and functional characterisation of MSC-based cellular products. Beyond advancing cell therapy manufacturing, it contributes to the scientific industry studying iron biology by providing real-time iron flux measurements that were previously unavailable. The research also advances clinical translation of high-quality cell therapies for cartilage regeneration, bringing these closer to patients with joint degeneration conditions and cartilage injuries.

Building on these findings, the researchers plan to carry out future preclinical and clinical studies to expand this approach beyond quality control in manufacturing, with the aim of establishing µMRR as a validated method for the clinical translation of MSC-based therapies in patients for cartilage repair.

The research, conducted at SMART, was supported by the National Research Foundation Singapore under its Campus for Research Excellence and Technological Enterprise (CREATE) program.

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Generative AI improves a wireless vision system that sees through obstructions

MIT researchers have spent more than a decade studying techniques that enable robots to find and manipulate hidden objects by “seeing” through obstacles. Their methods utilize surface-penetrating wireless signals that reflect off concealed items.

Now, the researchers are leveraging generative artificial intelligence models to overcome a longstanding bottleneck that limited the precision of prior approaches. The result is a new method that produces more accurate shape reconstructions, which could improve a robot’s ability to reliably grasp and manipulate objects that are blocked from view.

This new technique builds a partial reconstruction of a hidden object from reflected wireless signals and fills in the missing parts of its shape using a specially trained generative AI model.

The researchers also introduced an expanded system that uses generative AI to accurately reconstruct an entire room, including all the furniture. The system utilizes wireless signals sent from one stationary radar, which reflect off humans moving in the space.  

This overcomes one key challenge of many existing methods, which require a wireless sensor to be mounted on a mobile robot to scan the environment. And unlike some popular camera-based techniques, their method preserves the privacy of people in the environment.

These innovations could enable warehouse robots to verify packed items before shipping, eliminating waste from product returns. They could also allow smart home robots to understand someone’s location in a room, improving the safety and efficiency of human-robot interaction.

“What we’ve done now is develop generative AI models that help us understand wireless reflections. This opens up a lot of interesting new applications, but technically it is also a qualitative leap in capabilities, from being able to fill in gaps we were not able to see before to being able to interpret reflections and reconstruct entire scenes,” says Fadel Adib, associate professor in the Department of Electrical Engineering and Computer Science, director of the Signal Kinetics group in the MIT Media Lab, and senior author of two papers on these techniques. “We are using AI to finally unlock wireless vision.”

Adib is joined on the first paper by lead author and research assistant Laura Dodds; as well as research assistants Maisy Lam, Waleed Akbar, and Yibo Cheng; and on the second paper by lead author and former postdoc Kaichen Zhou; Dodds; and research assistant Sayed Saad Afzal. Both papers will be presented at the IEEE Conference on Computer Vision and Pattern Recognition.

Surmounting specularity

The Adib Group previously demonstrated the use of millimeter wave (mmWave) signals to create accurate reconstructions of 3D objects that are hidden from view, like a lost wallet buried under a pile.

These waves, which are the same type of signals used in Wi-Fi, can pass through common obstructions like drywall, plastic, and cardboard, and reflect off hidden objects.

But mmWaves usually reflect in a specular manner, which means a wave reflects in a single direction after striking a surface. So large portions of the surface will reflect signals away from the mmWave sensor, making those areas effectively invisible.

“When we want to reconstruct an object, we are only able to see the top surface and we can’t see any of the bottom or sides,” Dodds explains.

The researchers previously used principles from physics to interpret reflected signals, but this limits the accuracy of the reconstructed 3D shape.

In the new papers, they overcame that limitation by using a generative AI model to fill in parts that are missing from a partial reconstruction.

“But the challenge then becomes: How do you train these models to fill in these gaps?” Adib says.

Usually, researchers use extremely large datasets to train a generative AI model, which is one reason models like Claude and Llama exhibit such impressive performance. But no mmWave datasets are large enough for training.

Instead, the researchers adapted the images in large computer vision datasets to mimic the properties in mmWave reflections.

“We were simulating the property of specularity and the noise we get from these reflections so we can apply existing datasets to our domain. It would have taken years for us to collect enough new data to do this,” Lam says.

The researchers embed the physics of mmWave reflections directly into these adapted data, creating a synthetic dataset they use to teach a generative AI model to perform plausible shape reconstructions.

The complete system, called Wave-Former, proposes a set of potential object surfaces based on mmWave reflections, feeds them to the generative AI model to complete the shape, and then refines the surfaces until it achieves a full reconstruction.

Wave-Former was able to generate faithful reconstructions of about 70 everyday objects, such as cans, boxes, utensils, and fruit, boosting accuracy by nearly 20 percent over state-of-the-art baselines. The objects were hidden behind or under cardboard, wood, drywall, plastic, and fabric.

Seeing “ghosts”

The team used this same approach to build an expanded system that fully reconstructs entire indoor scenes by leveraging mmWave reflections off humans moving in a room.

Human motion generates multipath reflections. Some mmWaves reflect off the human, then reflect again off a wall or object, and then arrive back at the sensor, Dodds explains.

These secondary reflections create so-called “ghost signals,” which are reflected copies of the original signal that change location as a human moves. These ghost signals are usually discarded as noise, but they also hold information about the layout of the room.

“By analyzing how these reflections change over time, we can start to get a coarse understanding of the environment around us. But trying to directly interpret these signals is going to be limited in accuracy and resolution.” Dodds says.

They used a similar training method to teach a generative AI model to interpret those coarse scene reconstructions and understand the behavior of multipath mmWave reflections. This model fills in the gaps, refining the initial reconstruction until it completes the scene.

They tested their scene reconstruction system, called RISE, using more than 100 human trajectories captured by a single mmWave radar. On average, RISE generated reconstructions that were about twice as precise than existing techniques.

In the future, the researchers want to improve the granularity and detail in their reconstructions. They also want to build large foundation models for wireless signals, like the foundation models GPT, Claude, and Gemini for language and vision, which could open new applications.

This work is supported, in part, by the National Science Foundation (NSF), the MIT Media Lab, and Amazon.

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A better method for identifying overconfident large language models

Large language models (LLMs) can generate credible but inaccurate responses, so researchers have developed uncertainty quantification methods to check the reliability of predictions. One popular method involves submitting the same prompt multiple times to see if the model generates the same answer.

But this method measures self-confidence, and even the most impressive LLM might be confidently wrong. Overconfidence can mislead users about the accuracy of a prediction, which might result in devastating consequences in high-stakes settings like health care or finance.   

To address this shortcoming, MIT researchers introduced a new method for measuring a different type of uncertainty that more reliably identifies confident but incorrect LLM responses.

Their method involves comparing a target model’s response to responses from a group of similar LLMs. They found that measuring cross-model disagreement more accurately captures this type of uncertainty than traditional approaches.

They combined their approach with a measure of LLM self-consistency to create a total uncertainty metric, and evaluated it on 10 realistic tasks, such as question-answering and math reasoning. This total uncertainty metric consistently outperformed other measures and was better at identifying unreliable predictions.

“Self-consistency is being used in a lot of different approaches for uncertainty quantification, but if your estimate of uncertainty only relies on a single model’s outcome, it is not necessarily trustable. We went back to the beginning to understand the limitations of current approaches and used those as a starting point to design a complementary method that can empirically improve the results,” says Kimia Hamidieh, an electrical engineering and computer science (EECS) graduate student at MIT and lead author of a paper on this technique.

She is joined on the paper by Veronika Thost, a research scientist at the MIT-IBM Watson AI Lab; Walter Gerych, a former MIT postdoc who is now an assistant professor at Worcester Polytechnic Institute; Mikhail Yurochkin, a staff research scientist at the MIT-IBM Watson AI Lab; and senior author Marzyeh Ghassemi, an associate professor in EECS and a member of the Institute of Medical Engineering Sciences and the Laboratory for Information and Decision Systems.

Understanding overconfidence

Many popular methods for uncertainty quantification involve asking a model for a confidence score or testing the consistency of its responses to the same prompt. These methods estimate aleatoric uncertainty, or how internally confident a model is in its own prediction.

However, LLMs can be confident when they are completely wrong. Research has shown that epistemic uncertainty, or uncertainty about whether one is using the right model, can be a better way to assess true uncertainty when a model is overconfident.

The MIT researchers estimate epistemic uncertainty by measuring disagreement across a similar group of LLMs.    

“If I ask ChatGPT the same question multiple times and it gives me the same answer over and over again, that doesn’t mean the answer is necessarily correct. If I switch to Claude or Gemini and ask them the same question, and I get a different answer, that is going to give me a sense of the epistemic uncertainty,” Hamidieh explains.

Epistemic uncertainty attempts to capture how far a target model diverges from the ideal model for that task. But since it is impossible to build an ideal model, researchers use surrogates or approximations that often rely on faulty assumptions.

To improve uncertainty quantification, the MIT researchers needed a more accurate way to estimate epistemic uncertainty.

An ensemble approach

The method they developed involves measuring the divergence between the target model and a small ensemble of models with similar size and architecture. They found that comparing semantic similarity, or how closely the meanings of the responses match, could provide a better estimate of epistemic uncertainty.

To achieve the most accurate estimate, the researchers needed a set of LLMs that covered diverse responses, weren’t too similar to the target model, and were weighted based on credibility.

“We found that the easiest way to satisfy all these properties is to take models that are trained by different companies. We tried many different approaches that were more complex, but this very simple approach ended up working best,” Hamidieh says.

Once they had developed this method for estimating epistemic uncertainty, they combined it with a standard approach that measures aleatoric uncertainty. This total uncertainty metric (TU) offered the most accurate reflection of whether a model’s confidence level is trustworthy.

“Uncertainty depends on the uncertainty of the given prompt as well as how close our model is to the optimal model. This is why summing up these two uncertainty metrics is going to give us the best estimate,” Hamidieh says.

TU could more effectively identify situations where an LLM is hallucinating, since epistemic uncertainty can flag confidently wrong outputs that aleatoric uncertainty might miss. It could also enable researchers to reinforce an LLM’s confidently correct answers during training, which may improve performance.

They tested TU using multiple LLMs on 10 common tasks, such as question-answering, summarization, translation, and math reasoning. Their method more effectively identified unreliable predictions than either measure on its own.

Measuring total uncertainty often required fewer queries than calculating aleatoric uncertainty, which could reduce computational costs and save energy.

Their experiments also revealed that epistemic uncertainty is most effective on tasks with a unique correct answer, like factual question-answering, but may underperform on more open-ended tasks.

In the future, the researchers could adapt their technique to improve its performance on open-ended queries. They may also build on this work by exploring other forms of aleatoric uncertainty.

This work is funded, in part, by the MIT-IBM Watson AI Lab.

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