Machine-learning models can speed up the discovery of new materials by making predictions and suggesting experiments. But most models today only consider a few specific types of data or variables. Compare that with human scientists, who work in a collaborative environment and consider experimental results, the broader scientific literature, imaging and structural analysis, personal experience or intuition, and input from colleagues and peer reviewers.

Now, MIT researchers have developed a method for optimizing materials recipes and planning experiments that incorporates information from diverse sources like insights from the literature, chemical compositions, microstructural images, and more. The approach is part of a new platform, named Copilot for Real-world Experimental Scientists (CRESt), that also uses robotic equipment for high-throughput materials testing, the results of which are fed back into large multimodal models to further optimize materials recipes.

Human researchers can converse with the system in natural language, with no coding required, and the system makes its own observations and hypotheses along the way. Cameras and visual language models also allow the system to monitor experiments, detect issues, and suggest corrections.

“In the field of AI for science, the key is designing new experiments,” says Ju Li, School of Engineering Carl Richard Soderberg Professor of Power Engineering. “We use multimodal feedback — for example information from previous literature on how palladium behaved in fuel cells at this temperature, and human feedback — to complement experimental data and design new experiments. We also use robots to synthesize and characterize the material’s structure and to test performance.”

The system is described in a paper published in Nature. The researchers used CRESt to explore more than 900 chemistries and conduct 3,500 electrochemical tests, leading to the discovery of a catalyst material that delivered record power density in a fuel cell that runs on formate salt to produce electricity.

Joining Li on the paper as first authors are PhD student Zhen Zhang, Zhichu Ren PhD ’24, PhD student Chia-Wei Hsu, and postdoc Weibin Chen. Their coauthors are MIT Assistant Professor Iwnetim Abate; Associate Professor Pulkit Agrawal; JR East Professor of Engineering Yang Shao-Horn; MIT.nano researcher Aubrey Penn; Zhang-Wei Hong PhD ’25, Hongbin Xu PhD ’25; Daniel Zheng PhD ’25; MIT graduate students Shuhan Miao and Hugh Smith; MIT postdocs Yimeng Huang, Weiyin Chen, Yungsheng Tian, Yifan Gao, and Yaoshen Niu; former MIT postdoc Sipei Li; and collaborators including Chi-Feng Lee, Yu-Cheng Shao, Hsiao-Tsu Wang, and Ying-Rui Lu.

A smarter system

Materials science experiments can be time-consuming and expensive. They require researchers to carefully design workflows, make new material, and run a series of tests and analysis to understand what happened. Those results are then used to decide how to improve the material.

To improve the process, some researchers have turned to a machine-learning strategy known as active learning to make efficient use of previous experimental data points and explore or exploit those data. When paired with a statistical technique known as Bayesian optimization (BO), active learning has helped researchers identify new materials for things like batteries and advanced semiconductors.

“Bayesian optimization is like Netflix recommending the next movie to watch based on your viewing history, except instead it recommends the next experiment to do,” Li explains. “But basic Bayesian optimization is too simplistic. It uses a boxed-in design space, so if I say I’m going to use platinum, palladium, and iron, it only changes the ratio of those elements in this small space. But real materials have a lot more dependencies, and BO often gets lost.”

Most active learning approaches also rely on single data streams that don’t capture everything that goes on in an experiment. To equip computational systems with more human-like knowledge, while still taking advantage of the speed and control of automated systems, Li and his collaborators built CRESt.

CRESt’s robotic equipment includes a liquid-handling robot, a carbothermal shock system to rapidly synthesize materials, an automated electrochemical workstation for testing, characterization equipment including automated electron microscopy and optical microscopy, and auxiliary devices such as pumps and gas valves, which can also be remotely controlled.  Many processing parameters can also be tuned.

With the user interface, researchers can chat with CRESt and tell it to use active learning to find promising materials recipes for different projects. CRESt can include up to 20 precursor molecules and substrates into its recipe. To guide material designs, CRESt’s models search through scientific papers for descriptions of elements or precursor molecules that might be useful. When human researchers tell CRESt to pursue new recipes, it kicks off a robotic symphony of sample preparation, characterization, and testing. The researcher can also ask CRESt to perform image analysis from scanning electron microscopy imaging, X-ray diffraction, and other sources.

Information from those processes is used to train the active learning models, which use both literature knowledge and current experimental results to suggest further experiments and accelerate materials discovery.

“For each recipe we use previous literature text or databases, and it creates these huge representations of every recipe based on the previous knowledge base before even doing the experiment,” says Li. “We perform principal component analysis in this knowledge embedding space to get a reduced search space that captures most of the performance variability. Then we use Bayesian optimization in this reduced space to design the new experiment. After the new experiment, we feed newly acquired multimodal experimental data and human feedback into a large language model to augment the knowledgebase and redefine the reduced search space, which gives us a big boost in active learning efficiency.”

Materials science experiments can also face reproducibility challenges. To address the problem, CRESt monitors its experiments with cameras, looking for potential problems and suggesting solutions via text and voice to human researchers.

The researchers used CRESt to develop an electrode material for an advanced type of high-density fuel cell known as a direct formate fuel cell. After exploring more than 900 chemistries over three months, CRESt discovered a catalyst material made from eight elements that achieved a 9.3-fold improvement in power density per dollar over pure palladium, an expensive precious metal. In further tests, CRESTs material was used to deliver a record power density to a working direct formate fuel cell even though the cell contained just one-fourth of the precious metals of previous devices.

The results show the potential for CRESt to find solutions to real-world energy problems that have plagued the materials science and engineering community for decades.

“A significant challenge for fuel-cell catalysts is the use of precious metal,” says Zhang. “For fuel cells, researchers have used various precious metals like palladium and platinum. We used a multielement catalyst that also incorporates many other cheap elements to create the optimal coordination environment for catalytic activity and resistance to poisoning species such as carbon monoxide and adsorbed hydrogen atom. People have been searching low-cost options for many years. This system greatly accelerated our search for these catalysts.”

A helpful assistant

Early on, poor reproducibility emerged as a major problem that limited the researchers’ ability to perform their new active learning technique on experimental datasets. Material properties can be influenced by the way the precursors are mixed and processed, and any number of problems can subtly alter experimental conditions, requiring careful inspection to correct.

To partially automate the process, the researchers coupled computer vision and vision language models with domain knowledge from the scientific literature, which allowed the system to hypothesize sources of irreproducibility and propose solutions. For example, the models can notice when there’s a millimeter-sized deviation in a sample’s shape or when a pipette moves something out of place. The researchers incorporated some of the model’s suggestions, leading to improved consistency, suggesting the models already make good experimental assistants.

The researchers noted that humans still performed most of the debugging in their experiments.

“CREST is an assistant, not a replacement, for human researchers,” Li says. “Human researchers are still indispensable. In fact, we use natural language so the system can explain what it is doing and present observations and hypotheses. But this is a step toward more flexible, self-driving labs.”

This site turns your URL into something sketchy-looking.

For example, www.schneier.com becomes
https://cheap-bitcoin.online/firewall-snatcher/cipher-injector/phishing_sniffer_tool.html?form=inject&host=spoof&id=bb1bc121&parameter=inject&payload=%28function%28%29%7B+return+%27+hi+%27.trim%28%29%3B+%7D%29%28%29%3B&port=spoof.

Found on Boing Boing.

Clean energy is the "trend of our time," Chinese President Xi Jinping said, announcing targets for trimming his country's carbon pollution.

Fossil fuel and auto groups are eager to see the EPA climate finding repealed, but they worry it could lead to lawsuits over their emissions.

Legislatures have blasted the effort as an attempt to revoke state laws and exceed "the established bounds of federal authority."

Despite heavy criticism from climate scientists, Energy Secretary Chris Wright said DOE's report contained just one factual disagreement.

Supporters already have won regulatory changes in Utah for so-called balcony solar systems, which provide a small boost of electrical power.

The voluntary initiative comes ahead of the United Nations climate talks scheduled to take place in Brazil in November.

Small versus large, young against long-established — farm policy reform is setting up a clash over scarce funding.

The EU and China have expressed interest, which may make it one of the most significant outcomes of the November summit.

The "King of Storms" led to nearly 1.9 million people being evacuated in China's southern Guangdong province.

Nature Climate Change, Published online: 25 September 2025; doi:10.1038/s41558-025-02421-y

Reducing the wildfire risk of electric grids requires assessing and comparing various adaptation measures. A study shows that a grid technology innovation cuts the risk more cost-effectively than conventional approaches such as burying power lines.

Nature Climate Change, Published online: 25 September 2025; doi:10.1038/s41558-025-02436-5

Extreme events are increasingly becoming severe risks to the electric grid, yet there is limited understanding of the cost-effectiveness of adaptation investments. This research demonstrates that dynamic grid management could reduce large capital spending and limit wildfire risks in the USA.

Mucus is more than just a sticky substance: It contains a wealth of powerful molecules called mucins that help to tame microbes and prevent infection. In a new study, MIT researchers have identified mucins that defend against Salmonella and other bacteria that cause diarrhea.

The researchers now hope to mimic this defense system to create synthetic mucins that could help prevent or treat illness in soldiers or other people at risk of exposure to Salmonella. It could also help prevent “traveler’s diarrhea,” a gastrointestinal infection caused by consuming contaminated food or water.

Mucins are bottlebrush-shaped polymers made of complex sugar molecules known as glycans, which are tethered to a peptide backbone. In this study, the researchers discovered that a mucin called MUC2 turns off genes that Salmonella uses to enter and infect host cells.

“By using and reformatting this motif from the natural innate immune system, we hope to develop strategies to preventing diarrhea before it even starts. This approach could provide a low-cost solution to a major global health challenge that costs billions annually in lost productivity, health care expenses, and human suffering,” says Katharina Ribbeck, the Andrew and Erna Viterbi Professor of Biological Engineering at MIT and the senior author of the study.

MIT Research Scientist Kelsey Wheeler PhD ’21 and Michaela Gold PhD ’22 are the lead authors of the paper, which appeared Tuesday in the journal Cell Reports.

Blocking infection

Mucus lines much of the body, providing a physical barrier to infection, but that’s not all it does. Over the past decade, Ribbeck has identified mucins that can help to disarm Vibrio cholerae, as well as Pseudomonas aeruginosa, which can infect the lungs and other organs, and the yeast Candida albicans.

In the new study, the researchers wanted to explore how mucins from the digestive tract might interact with Salmonella enterica, a foodborne pathogen that can cause illness after consuming raw or undercooked food, or contaminated water.

To infect host cells, Salmonella must produce proteins that are part of the type 3 secretion system (T3SS), which helps bacteria form needle-like complexes that transfer bacterial proteins directly into host cells. These proteins are all encoded on a segment of DNA called Salmonella pathogenicity island 1 (SPI-1).

The researchers found that when they exposed Salmonella to a mucin called MUC2, which is found in the intestines, the bacteria stopped producing the proteins encoded by SPI-1, and they were no longer able to infect cells.

Further studies revealed that MUC2 achieves this by turning off a regulatory bacterial protein known as HilD. When this protein is blocked by mucins, it can no longer activate the T3SS genes.

Using computational simulations, the researchers showed that certain monosaccharides found in glycans, including GlcNAc and GalNAc, can attach to a specific binding site of the HilD protein. However, their studies showed that these monosaccharides can’t turn off HilD on their own — the shutoff only occurs when the glycans are tethered to the peptide backbone of the mucin.

The researchers also discovered that a similar mucin called MUC5AC, which is found in the stomach, can block HilD. And, both MUC2 and MUC5AC can turn off virulence genes in other foodborne pathogens that also use HilD as a gene regulator.

Mucins as medicine

Ribbeck and her students now plan to explore ways to use synthetic versions of these mucins to help boost the body’s natural defenses and protect the GI tract from Salmonella and other infections.

Studies from other labs have shown that in mice, Salmonella tends to infect portions of the GI tract that have a thin mucus barrier, or no barrier at all.

“Part of Salmonella’s evasion strategy for this host defense is to find locations where mucus is absent and then infect there. So, one could imagine a strategy where we try to bolster mucus barriers to protect those areas with limited mucin,” Wheeler says.

One way to deploy synthetic mucins could be to add them to oral rehydration salts — mixtures of electrolytes that are dissolved in water and used to treat dehydration caused by diarrhea and other gastrointestinal illnesses.

Another potential application for synthetic mucins would be to incorporate them into a chewable tablet that could be consumed before traveling to areas where Salmonella and other diarrheal illnesses are common. This kind of “pre-exposure prophylaxis” could help prevent a great deal of suffering and lost productivity due to illness, the researchers say.

“Mucin mimics would particularly shine as preventatives, because that’s how the body evolved mucus — as part of this innate immune system to prevent infection,” Wheeler says.

The research was funded by the U.S. Army Research Office, the U.S. Army Institute for Collaborative Biotechnologies, the U.S. National Science Foundation, the U.S. National Institute of Health and Environmental Sciences, the U.S. National Institutes of Health, and the German Research Foundation.

Annotating regions of interest in medical images, a process known as segmentation, is often one of the first steps clinical researchers take when running a new study involving biomedical images.

For instance, to determine how the size of the brain’s hippocampus changes as patients age, the scientist first outlines each hippocampus in a series of brain scans. For many structures and image types, this is often a manual process that can be extremely time-consuming, especially if the regions being studied are challenging to delineate.

To streamline the process, MIT researchers developed an artificial intelligence-based system that enables a researcher to rapidly segment new biomedical imaging datasets by clicking, scribbling, and drawing boxes on the images. This new AI model uses these interactions to predict the segmentation.

As the user marks additional images, the number of interactions they need to perform decreases, eventually dropping to zero. The model can then segment each new image accurately without user input.

It can do this because the model’s architecture has been specially designed to use information from images it has already segmented to make new predictions.

Unlike other medical image segmentation models, this system allows the user to segment an entire dataset without repeating their work for each image.

In addition, the interactive tool does not require a presegmented image dataset for training, so users don’t need machine-learning expertise or extensive computational resources. They can use the system for a new segmentation task without retraining the model.

In the long run, this tool could accelerate studies of new treatment methods and reduce the cost of clinical trials and medical research. It could also be used by physicians to improve the efficiency of clinical applications, such as radiation treatment planning.

“Many scientists might only have time to segment a few images per day for their research because manual image segmentation is so time-consuming. Our hope is that this system will enable new science by allowing clinical researchers to conduct studies they were prohibited from doing before because of the lack of an efficient tool,” says Hallee Wong, an electrical engineering and computer science graduate student and lead author of a paper on this new tool.

She is joined on the paper by Jose Javier Gonzalez Ortiz PhD ’24; John Guttag, the Dugald C. Jackson Professor of Computer Science and Electrical Engineering; and senior author Adrian Dalca, an assistant professor at Harvard Medical School and MGH, and a research scientist in the MIT Computer Science and Artificial Intelligence Laboratory (CSAIL). The research will be presented at the International Conference on Computer Vision.

Streamlining segmentation

There are primarily two methods researchers use to segment new sets of medical images. With interactive segmentation, they input an image into an AI system and use an interface to mark areas of interest. The model predicts the segmentation based on those interactions.

A tool previously developed by the MIT researchers, ScribblePrompt, allows users to do this, but they must repeat the process for each new image.

Another approach is to develop a task-specific AI model to automatically segment the images. This approach requires the user to manually segment hundreds of images to create a dataset, and then train a machine-learning model. That model predicts the segmentation for a new image. But the user must start the complex, machine-learning-based process from scratch for each new task, and there is no way to correct the model if it makes a mistake.

This new system, MultiverSeg, combines the best of each approach. It predicts a segmentation for a new image based on user interactions, like scribbles, but also keeps each segmented image in a context set that it refers to later.

When the user uploads a new image and marks areas of interest, the model draws on the examples in its context set to make a more accurate prediction, with less user input.

The researchers designed the model’s architecture to use a context set of any size, so the user doesn’t need to have a certain number of images. This gives MultiverSeg the flexibility to be used in a range of applications.

“At some point, for many tasks, you shouldn’t need to provide any interactions. If you have enough examples in the context set, the model can accurately predict the segmentation on its own,” Wong says.

The researchers carefully engineered and trained the model on a diverse collection of biomedical imaging data to ensure it had the ability to incrementally improve its predictions based on user input.

The user doesn’t need to retrain or customize the model for their data. To use MultiverSeg for a new task, one can upload a new medical image and start marking it.

When the researchers compared MultiverSeg to state-of-the-art tools for in-context and interactive image segmentation, it outperformed each baseline.

Fewer clicks, better results

Unlike these other tools, MultiverSeg requires less user input with each image. By the ninth new image, it needed only two clicks from the user to generate a segmentation more accurate than a model designed specifically for the task.

For some image types, like X-rays, the user might only need to segment one or two images manually before the model becomes accurate enough to make predictions on its own.

The tool’s interactivity also enables the user to make corrections to the model’s prediction, iterating until it reaches the desired level of accuracy. Compared to the researchers’ previous system, MultiverSeg reached 90 percent accuracy with roughly 2/3 the number of scribbles and 3/4 the number of clicks.

“With MultiverSeg, users can always provide more interactions to refine the AI predictions. This still dramatically accelerates the process because it is usually faster to correct something that exists than to start from scratch,” Wong says.

Moving forward, the researchers want to test this tool in real-world situations with clinical collaborators and improve it based on user feedback. They also want to enable MultiverSeg to segment 3D biomedical images.

This work is supported, in part, by Quanta Computer, Inc. and the National Institutes of Health, with hardware support from the Massachusetts Life Sciences Center.

People are increasingly turning to software to design complex material structures like airplane wings and medical implants. But as design models become more capable, our fabrication techniques haven’t kept up. Even 3D printers struggle to reliably produce the precise designs created by algorithms. The problem has led to a disconnect between the ways a material is expected to perform and how it actually works.

Now, MIT researchers have created a way for models to account for 3D printing’s limitations during the design process. In experiments, they showed their approach could be used to make materials that perform much more closely to the way they’re intended to.

“If you don’t account for these limitations, printers can either over- or under-deposit material by quite a lot, so your part becomes heavier or lighter than intended. It can also over- or underestimate the material performance significantly,” says Gilbert W. Winslow Associate Professor of Civil and Environmental Engineering Josephine Carstensen. “With our technique, you know what you’re getting in terms of performance because the numerical model and experimental results align very well.”

The approach is described in the journal Materials and Design, in an open-access paper co-authored by Carstensen and PhD student Hajin Kim-Tackowiak.

Matching theory with reality

Over the last decade, new design and fabrication technologies have transformed the way things are made, especially in industries like aerospace, automotive, and biomedical engineering, where materials must reach precise weight-to-strength ratios and other performance thresholds. In particular, 3D printing allows materials to be made with more complex internal structures.

“3D printing processes generally give us more flexibility because we don’t have to come up with forms or molds for things that would be made through more traditional means like injection molding,” Kim-Tackowiak explains.

As 3D printing has made production more precise, so have methods for designing complex material structures. One of the most advanced computational design techniques is known as topology optimization. Topology optimization has been used to generate new and often surprising material structures that can outperform conventional designs, in some cases approaching the theoretical limits of certain performance thresholds. It is currently being used to design materials with optimized stiffness and strength, maximized energy absorption, fluid permeability, and more.

But topology optimization often creates designs at extremely fine scales that 3D printers have struggled to reliably reproduce. The problem is the size of the print head that extrudes the material. If the design specifies a layer to be 0.5 millimeters thick, for instance, and the print head is only capable of extruding 1-millimeter-thick layers, the final design will be warped and imprecise.

Another problem has to do with the way 3D printers create parts, with a print head extruding a thin bead of material as it glides across the printing area, gradually building parts layer by layer. That can cause weak bonding between layers, making the part more prone to separation or failure.

The researchers sought to address the disconnect between expected and actual properties of materials that arise from those limitations.

“We thought, ‘We know these limitations in the beginning, and the field has gotten better at quantifying these limitations, so we might as well design from the get-go with that in mind,” Kim-Tackowiak says.

In previous work, Carstensen developed an algorithm that embedded information about the print nozzle size into design algorithms for beam structures. For this paper, the researchers built off that approach to incorporate the direction of the print head and the corresponding impact of weak bonding between layers. They also made it work with more complex, porous structures that can have extremely elastic properties.

The approach allows users to add variables to the design algorithms that account for the center of the bead being extruded from a print head and the exact location of the weaker bonding region between layers. The approach also automatically dictates the path the print head should take during production.

The researchers used their technique to create a series of repeating 2D designs with various sizes of hollow pores, or densities. They compared those creations to materials made using traditional topology optimization designs of the same densities.

In tests, the traditionally designed materials deviated from their intended mechanical performance more than materials designed using the researchers’ new technique at material densities under 70 percent. The researchers also found that conventional designs consistently over-deposited material during fabrication. Overall, the researchers’ approach led to parts with more reliable performance at most densities.

“One of the challenges of topology optimization has been that you need a lot of expertise to get good results, so that once you take the designs off the computer, the materials behave the way you thought they would,” Carstensen says. “We’re trying to make it easy to get these high-fidelity products.”

Scaling a new design approach

The researchers believe this is the first time a design technique has accounted for both the print head size and weak bonding between layers.

“When you design something, you should use as much context as possible,” Kim-Tackowiak says. “It was rewarding to see that putting more context into the design process makes your final materials more accurate. It means there are fewer surprises. Especially when we’re putting so much more computational resources into these designs, it’s nice to see we can correlate what comes out of the computer with what comes out of the production process.”

In future work, the researchers hope to improve their method for higher material densities and for different kinds of materials like cement and ceramics. Still, they said their approach offered an improvement over existing techniques, which often require experienced 3D printing specialists to help account for the limitations of the machines and materials.

“It was cool to see that just by putting in the size of your deposition and the bonding property values, you get designs that would have required the consultation of somebody who’s worked in the space for years,” Kim-Tackowiak says.

The researchers say the work paves the way to design with more materials.

“We’d like to see this enable the use of materials that people have disregarded because printing with them has led to issues,” Kim-Tackowiak says. “Now we can leverage those properties or work with those quirks as opposed to just not using all the material options we have at our disposal.”

California’s Governor should sign S.B. 7, a common-sense bill to end some of the harshest consequences of automated abuse at work. EFF is proud to join dozens of labor, digital rights, and other advocates in support of the “No Robo Bosses Act.”

Algorithmic decision-making is a growing threat to workers. Bosses are using AI to assess the body language and voice tone of job candidates. They’re using algorithms to predict when employees are organizing a union or planning to quit. They’re automating choices about who gets fired. And these employment algorithms often discriminate based on gender, race, and other protected statuses. Fortunately, many advocates are resisting.

What the Bill Does

S.B. 7 is a strong step in the right direction. It addresses “automated decision systems” (ADS) across the full landscape of employment. It applies to bosses in the private and government sectors, and it protects workers who are employees and contractors. It addresses all manner of employment decisions that involve automated decisionmaking, including hiring, wages, hours, duties, promotion, discipline, and termination. It covers bosses using ADS to assist or replace a person making a decision about another person.

Algorithmic decision-making is a growing threat to workers.

The bill requires employers to be transparent when they rely on ADS. Before using it to make a decision about a job applicant or current worker, a boss must notify them about the use of ADS. The notice must be in a stand-alone, plain language communication. The notice to a current worker must disclose the types of decisions subject to ADS, and a boss cannot use an ADS for an undisclosed purpose. Further, the notice to a current worker must disclose information about how the ADS works, including what information goes in and how it arrives at its decision (such as whether some factors are weighed more heavily than others).

The bill provides some due process to current workers who face discipline or termination based on the ADS. A boss cannot fire or punish a worker based solely on ADS. Before a boss does so based primarily on ADS, they must ensure a person reviews both the ADS output and other relevant information. A boss must also notify the affected worker of such use of ADS. A boss cannot use customer ratings as the only or primary input for such decisions. And every worker can obtain a copy of the most recent year of their own data that their boss might use as ADS input to punish or fire them.

Other provisions of the bill will further protect workers. A boss must maintain an updated list of all ADS it currently uses. A boss cannot use ADS to violate the law, to infer whether a worker is a member of a protected class, or to target a worker for exercising their labor and other rights. Further, a boss cannot retaliate against a worker who exercises their rights under this new law. Local laws are not preempted, so our cities and counties are free to enact additional protections.

Next Steps

The “No Robo Bosses Act” is a great start. And much more is needed, because many kinds of powerful institutions are using automated decision-making against us. Landlords use it to decide who gets a home. Insurance companies use it to decide who gets health care. ICE uses it to decide who must submit to location tracking by electronic monitoring.

EFF has long been fighting such practices. We believe technology should improve everyone’s lives, not subject them to abuse and discrimination. We hope you will join us.

MIT professors Zachary Hartwig and Wanda Orlikowski exemplify a rare but powerful kind of mentorship — one grounded not just in intellectual excellence, but in deep personal care. They remind us that transformative academic leadership starts with humanity. 

Whether it's Hartwig’s ability to bring engineering brilliance to life through genuine personal connection, or Orlikowski’s unwavering support for those who share in her mission to create meaningful impact, both foster environments where people, not just ideas, can thrive. 

Their students and colleagues describe feeling seen, supported, and encouraged not only to grow as scholars, but as people. It’s this ethic of care, of valuing the human behind the research, that defines their mentorship and elevates those around them.

Hartwig and Orlikowski are two of the 2023-25 Committed to Caring cohort who are fostering transformative research through growth, independence, and support. For MIT graduate students, the Committed to Caring program recognizes those who go above and beyond.

Zachary Hartwig: Signposts on the way to new territory

Zachary (Zach) Seth Hartwig is an associate professor in the Department of Nuclear Science and Engineering (NSE) with a co-appointment at the MIT Plasma Science and Fusion Center (PSFC). He has worked in the areas of large-scale applied superconductivity, magnet fusion device design, radiation detector development, and accelerator science and engineering. His active research focuses on the development of high-field superconducting magnet technologies for fusion energy and accelerated irradiation methods for fusion materials using ion beams.

One nominator expressed, “although he didn't formally become my advisor until after I submitted my thesis prospectus, I always felt like Zach had my back.” This feeling of support was shared by Hartwig’s advisees through numerous examples.

When the pandemic started, Hartwig made sure that the student had ongoing support and a safe place to simply exist as an international visiting student during a tumultuous time. This care often presented in small ways: when the mentee needed to debug their cryogenic system, Hartwig showed up at the lab every day to help plan the next test; when this same student struggled to write the introduction of their first paper, Hartwig continued to provide support; and when the student wanted to practice for their qualifying exam, Hartwig insisted on helping until the last day. Additionally, when the advisee’s funding was nearing its end, Hartwig secured transition support to bridge the gap.

The nominator reflected on Hartwig’s cheerful and positive mentorship style, noting that “through it all, he … always valued my ideas, he was never judgmental, he never raised his voice, he never dismissed me.” 

Hartwig characterizes himself as “highly supportive, but from the backseat.” He is active with and available to his students; however, it is essential to him that they are the ones driving the research. “Graduate students need to experience increasing amounts of autonomy, but within a supportive framework that fades as they need to rely on it less and less as they become independent researchers,” he notes.

Hartwig shapes the intellectual maturation of his students. He believes that graduate school is not solely about results or publications, but about whom students become in the process. 

“The most important output of a PhD program is not your results, your papers, or your thesis; it’s YOU,” he emphasizes. His mentorship is built around this philosophy, creating an environment where students steadily evolve into independent researchers.

Importantly, Hartwig cultivates a culture where daring, unconventional ideas are not just allowed — they’re encouraged. He models this approach through his own career, which has taken bold leaps across disciplines and technologies. 

“MIT should do things only MIT can do,” he tells his students. His message is clear: Graduate students should not be afraid to go against the grain.

This philosophy has inspired many of his students to explore nontraditional research paths, armed with the confidence that failure is not a setback, but a sign that they are asking ambitious questions. Hartwig regularly reinforces this, reminding students that null results and dead ends often teach us the most. 

“They’re the signposts you have to pass on the way to new territory,” he says.

Ultimately, one of the most fulfilling parts of Hartwig’s work is witnessing the moment when it all “clicks” for a student — when they begin to lead boldly, push back thoughtfully, and take true ownership of their research. “It’s a beautiful thing when it happens,” he reflects. 

For Hartwig, mentorship is about fostering not only the skills of a scientist, but the identity of one. His students don’t just grow in knowledge, they grow in courage, conviction, and clarity.

Wanda Orlikowski: Shaping research by supporting the people who make it happen

Wanda Orlikowski is the Alfred P. Sloan Professor of Information Technology and Organization Studies at MIT’s Sloan School of Management. Her research examines technologies in the workplace, with a particular focus on how digital reconfigurations generate significant shifts in organizing, coordination, and accountability. She is currently exploring the digital transformation of work.

Through times of uncertainty, students always find support in Orlikowski. One of her nominators shared that they have encountered many moments of doubt during the research development phase of their dissertation. “I [have had] concerns … that I'm not making progress. I do all this work, and it’s not going anywhere, I keep returning back to where I started,” the mentee reflected. 

Orlikowski has walked this advisee through those moments patiently and with great empathy, connecting her own experiences with those of her students. She often talks about the research process not being a straight line of progress, but rather a spiral. 

“This metaphor … suggests that coming back to ideas again and again is in fact progress,” rather than a failure. “Every time I come back to it, I’m at a higher plane, and I’m refining the same idea further and further,” the nominator wrote.

Students say that Orlikowski makes an effort to support them through moments of doubt, turning these moments into opportunities for growth. “It has … been such a benefit for me to have her near-constant availability,” the student said. “She listens to my thoughts and lets me just talk and spitball ideas, without her interrupting.” 

Orlikowski pushes and prods her students to elaborate, clarify, and expand their thoughts. She does this proactively, spending many hours every week talking to her students, reading their writing, and making scrupulous comments on their work. 

Orlikowski has been remarkably perceptive when her students need support. One of the nominators struggled during their first holiday season in the PhD program, unable to visit their family. Orlikowski noticed the student’s isolation and reached out, inviting the student to her family’s Christmas dinner, a gesture that turned into a heartwarming tradition. 

“I gave her an orchid that first year, and to this day, it continues to bloom each year. Wanda regularly sends me pictures of it, and the joy she expresses in keeping it alive means so much to me. I feel that in her care, both the orchid and our connection have flourished,” the mentee remarks.

“One of the things I’ve appreciated most about Wanda is that she has never tried to change who I am,” the nominator adds. They go on to describe themselves as not a very strategic or extroverted person by nature, and for a long time, they struggled with the idea that these qualities might hinder their success in academia. “Wanda has helped me embrace my true self.”

“It’s not about fitting into a mold,” Orlikowski reminded the student, “It’s about being true to who you are, and doing great work.” Her support has made the student comfortable with their approach to both research and life.

The academic world often feels like it rewards self-promotion and strategic maneuvering, but Orlikowski has alleviated much of her students’ anxiety about whether they can be competitive without it. “You don’t have to pretend to be something you’re not,” she assures them. “The work will speak for itself.” 

Orlikowski’s support for her students extends beyond encouragement; she advocates for their work, helping them gain visibility and traction in the broader academic community. “It’s not just words — she has actively supported me, promoting my work through her network of students and peers,” the nominator articulated. 

Her belief in her mentees, and her willingness to support their work, has had a profound impact on their academic journey.

Just as the United States has prospered through its ability to draw talent from every corner of the globe, so too has MIT thrived as a magnet for the world’s most keen and curious minds — many of whom remain here to invent solutions, create companies, and teach future leaders, contributing to America’s success.

President Ronald Reagan remarked in 1989 that the United States leads the world “because, unique among nations, we draw our people — our strength — from every country and every corner of the world. And by doing so we continuously renew and enrich our nation.” Those words ring still ring true 36 years later — and the sentiment resonates especially at MIT.

"To find people with the drive, skill, and daring to see, discover, and invent things no one else can, we open ourselves to talent from every corner of the United States and from around the globe,” says MIT President Sally Kornbluth. “MIT is an American university, proudly so — but we would be gravely diminished without the students and scholars who join us from other nations."

MIT’s steadfast commitment to attracting the best and brightest talent from around the world has contributed to not just its own success, but also that of the nation as whole. MIT’s stature as an international hub of education and innovation adds value to the U.S. economy and competitiveness in myriad ways — from foreign-born faculty delivering breakthroughs here and founding American companies that create American jobs to international students contributing over $264 million annually to the U.S. economy during the 2023-24 school year.

Highlighting the extent and value of its global character, the Office of the Vice Provost for International Activities recently expanded a new video series, “The World at MIT.” In it, 20 faculty members born outside the United States tell how they dreamed of coming to MIT while growing up abroad and eventually joined the MIT faculty, where they’ve helped establish and maintain global leadership in science while teaching the next generation of innovators. A common thread running through their stories is the importance of the campus’s distinct nature as a community that is both profoundly American and deeply connected to the people, institutions, and concerns of regions and nations around the globe.

Joining the MIT faculty in 1980, MIT President Emeritus L. Rafael Reif knew almost instantly that he would stay.

“I was impressed by the richness of the variety of groups of people and cultures here,” says Reif, who moved to the United States from Venezuela and eventually served as MIT’s president from 2012 to 2022. “There is no richer place than MIT, because every point of view is here. That is what makes the place so special.”

The benefits of welcoming international students and researchers to campus extend well beyond MIT. More than 17,000 MIT alumni born elsewhere now call the United States home, for example, and many have founded U.S.-based companies that have generated billions of dollars in economic activity.

Contributing to America’s prestige internationally, one-third of MIT’s 104 Nobel laureates — including seven of the eight Nobel winners over the last decade — were born abroad. Drawn to MIT, they went on to make their breakthroughs in the United States. Among them is Lester Wolfe Professor of Chemistry Moungi Bawendi, who won the Nobel Prize in Chemistry in 2023 for his work in the chemical production of high-quality quantum dots.   

“MIT is a great environment. It’s very collegial, very collaborative. As a result, we also have amazing students,” says Bawendi, who lived in France and Tunisia as a child before moving to the U.S. “I couldn’t have done my first three years here, which eventually got me a Nobel Prize, without having really bold, smart, adventurous graduate students.”

The give-and-take among MIT faculty and students also inspires electrical engineering and computer science professor Akintunde Ibitayo (Tayo) Akinwande, who grew up in Nigeria.

“Anytime I teach a class, I always learn something from my students’ probing questions,” Akinwande says. “It gives me new insights sometimes, and that’s always the kind of environment I like — where I’m learning something new all the time.”

MIT’s global vibe inspires its students to not only explore worlds of ideas in campus labs and classrooms, but to journey the world itself. Forty-three percent of undergraduates pursued international experiences during the last academic year — taking courses at foreign universities, conducting research, or interning at multinational companies. MIT students and faculty alike are regularly engaged in research outside the United States, addressing some of the world’s toughest challenges and devising solutions that can be deployed back home, as well as abroad. In so doing, they embody MIT’s motto of “mens et manus” (“mind and hand”), reflecting the educational ideals of MIT’s founders who promoted education for practical application.

As someone who loves exploring “lofty questions” along with the practical design of things, Nergis Mavalvala found a perfect fit at MIT and calls her position as the Marble Professor of Astrophysics and dean of the School of Science “the best job in the world.”

“Everybody here wants to make the world a better place and are using their intellectual gifts and their education to do so,” says Mavalvala, who emigrated from Pakistan. “And I think that’s an amazing community to be part of.”

Daniela Rus agrees. Now the Andrew and Erna Viterbi Professor of Electrical Engineering and Computer Science and director of MIT’s Computer Science and Artificial Intelligence Laboratory, Rus was drawn to the practical application of mathematics while still a student in her native Romania.   

“And so, now here I am at MIT, essentially bringing together the world of science and math with the world of making things,” Rus says. “I’ve been here for two decades, and it’s been an extraordinary journey.”

The daughter of an Albert Einstein afficionado, Yukiko Yamashita grew up in Japan thinking of science not as a job, but a calling. MIT, where she is a professor of biology, is a place where people “are really open to unconventional ideas” and “intellectual freedom” thrives.

“There is something sacred about doing science. That’s how I grew up,” Yamashita says. “There are some distinct MIT characteristics. In a good way, people can’t let go. Every day, I am creating more mystery than I answer.”

For more about the paths that brought Yamashita and others to MIT and stories of how their disparate personal histories enrich the campus and wider community, visit the “World at MIT” videos website.

“Our global community’s multiplicity of ideas, experiences, and perspectives contributes enormously to MIT’s innovative and entrepreneurial spirit and, by extension, to the innovation and competitiveness of the U.S.,” says Vice Provost for International Activities Duane Boning, whose department developed the video series. “The bottom line is that both MIT and the U.S. grow stronger when we harness the talents of the world’s best and brightest.”

This is a weird story:

The US Secret Service disrupted a network of telecommunications devices that could have shut down cellular systems as leaders gather for the United Nations General Assembly in New York City.

The agency said on Tuesday that last month it found more than 300 SIM servers and 100,000 SIM cards that could have been used for telecom attacks within the area encompassing parts of New York, New Jersey and Connecticut.

“This network had the power to disable cell phone towers and essentially shut down the cellular network in New York City,” said special agent in charge Matt McCool...