EFF is filing against the Consumer Product Safety Council (CPSC) to ensure that the public has full access to the laws that govern us.

Our client Public.Resource.Org (Public Resource), a tiny non-profit founded by open records advocate Carl Malamud, has a mission that’s both simple and powerful: to make government information more accessible. Public Resource acquires and makes available online a wide variety of public documents such as tax filings, government-produced videos, and federal rules about safety and product designs. Those rules are initially created through private standards organizations and later incorporated into federal law. Such documents are often difficult to access otherwise, meaning the public cannot read, share, or comment on them. 

Working with Harvard Law School’s Cyberlaw Clinic, Public Resource has been submitting Freedom of Information Act requests to the CPSC requesting copies of the legally binding safety codes for children’s products—an area of law of intense interest to child safety advocates and consumer advocates, not to mention the families who use those products. But CPSC says it can’t release the codes, because the private association that coordinated their initial development insists that it retains copyright in them even after they have been adopted into law. That’s like saying a lobbyist who drafted a new tax law gets to control who reads it or shares it, even after it becomes a legal mandate.

Faced with similar claims, some courts, including the Court of Appeals for the Fifth Circuit, have held that the safety codes lose copyright protection when they are incorporated into law. Others, like the D.C. Circuit (in a case EFF defended on Public Resource’s behalf), have held that even if the standards lose copyright once they are incorporated into law, making them fully accessible and usable online is a lawful fair use. 

Now EFF has teamed up with the Cyberlaw Clinic to continue the fight. We’re asking a court to rule that copyright is no barrier to accessing and sharing the rules that are supposed to ensure the safety of our built environment and the products we use every day. With the rule of law under assault around the nation, it is more important than ever to defend our ability to read and speak the law, without restrictions.

Geothermal energy, a clean, continuous energy source accessible in many locations, has been slow to catch on. Nearly 2,000 years ago, the Romans made extensive use of geothermal energy — heat from the Earth — including at the spa complex at present-day Bath, England. Electricity was first produced from geothermal sources in the early 1900s in Italy. In the United States, the Geysers geothermal field in California began generating electricity at scale in 1960, and routinely produces more than 725 megawatts of baseload power today. 

According to the International Energy Agency (IEA), geothermal energy still supplies less than 1 percent of global electricity demand, although countries like Kenya (more than 40 percent of electricity generation) and Iceland (nearly 30 percent of electricity and 90 percent of the heating) have seen widespread adoption.

In recent years, technological advances, an influx of private capital, and shifting energy and environmental policies have driven renewed interest in expanding development of geothermal energy. If project costs continue to decline, the IEA predicts that geothermal energy could meet 15 percent of the growth in global electricity demand between 2024 and 2050. Many countries, including the United States, Indonesia, New Zealand, and Turkey, are prioritizing an expansion of geothermal energy as part of their broader energy strategies.

Achieving large-scale electricity generation from geothermal sources will depend on a significant expansion of so-called next-generation geothermal. This refers to tapping heat from source rocks at temperatures of 100 degrees Celsius to more than 400 C, often at depths of several kilometers below the surface. Last month, U.S. Congressional Rep. Jake Auchincloss (D-MA) and Rep. Mark Amodei (R-NV) introduced bipartisan legislation to promote research, testing, and development of one type of next-generation geothermal energy known as superhot rock.

Geothermal energy at MIT

Through its leadership in producing the influential 2006 “The Future of Geothermal Energy” report led by former MIT professor Jeff Tester, MIT and the predecessor of the MIT Energy Initiative (MITEI) played an important role in national geothermal strategy two decades ago. In 2008, researchers at the Plasma Science and Fusion Center (PSFC) invented millimeter-wave drilling with support from one of the first MITEI seed innovation grants. The technology, which could be particularly useful for geothermal installations in superhot and deep rock, is being commercialized by MIT spinout Quaise Energy.

MITEI is sponsoring next-generation geothermal projects through its Future Energy Systems Center. A project led by MITEI Research Scientist Pablo Duenas-Martinez focuses on the techno-economics of electricity generation from a geothermal plant co-located with a data center, a timely topic given the proliferation of data center power purchase agreements for electricity generated by geothermal energy. MITEI’s March 4 Spring Symposium focused on next-geothermal energy for the generation of firm power, and many of the leading exploration, drilling, reservoir development, and advanced technology companies working in this area sent panelists and speakers. On March 5, MITEI collaborated with the Clean Air Task Force (CATF) to co-host the GeoTech Summit, which explored accelerating technology development for and investment in next-generation geothermal.

To prepare for the recent symposium, MITEI organized a geothermal bootcamp during MIT’s Independent Activities Period (IAP) that introduced more than 40 members of the MIT community to geothermal basics, key technologies, and related MIT research. Carolyn Ruppel, MITEI’s deputy director of science and technology and the organizer of the IAP bootcamp and Spring Symposium, says, “MITEI’s member companies, which represent leading voices on energy, power generation, infrastructure, heavy industry, and digital technology, are increasingly approaching us about their interest in next-generation geothermal. There is also good momentum building across MIT, ranging from projects at the Earth Resources Laboratory to the millimeter-wave testbed being developed by PSFC and its MIT collaborators, individual projects in academic departments, and of course the work MITEI has been funding.”

Geothermal basics

Temperatures a few tens of meters below the ground are typically stable year-round. In some locations, these temperatures are warmer than the surface in winter and cooler in summer, making it possible to use geothermal heat pumps to moderate temperatures in buildings throughout the year. Overlooking the Charles River, Boston University’s 19-story Center for Computing and Data Science meets an estimated 90 percent of its heating and cooling needs using this kind of geothermal system. At the scale of large institutions or whole towns, thermal networks, district heating, and other approaches can efficiently supply heat from shallow geothermal sources without producing greenhouse gas emissions.

Tapping hotter and usually deeper geothermal sources could generate large amounts of electricity for decades at a single site. Next-generation geothermal is the term applied to these higher-temperature systems developed using enhanced, advanced, and superhot technologies. Enhanced geothermal refers to circulating fluids through engineered fracture systems in deep, dry rock with relatively low native permeability. Advanced geothermal adopts a closed loop approach, in which a working fluid is heated by circulating it through pipes embedded in the subsurface. Superhot geothermal, which is in its infancy, will likely use enhanced geothermal technology to circulate supercritical water through rock at almost 400 C.   

Next-generation geothermal

Drill deep enough and higher-temperature resources are nearly ubiquitous beneath the continents, but early-stage development must focus on the most promising sites, where the methods and technologies to routinely reach these hotter rocks can be tested and refined. Locations like Iceland and the southwestern U.S. state of Nevada, where tectonic plates are separating or the Earth’s outer layer is thinning, have hotter temperatures closer to the surface than areas like the northeastern United States, where the Earth’s crust is old, thick, and cooler. Even in the southwestern United States, though, reaching the high temperatures required for generating electricity via geothermal systems will require routinely drilling to depths of greater than 4 kilometers in crystalline rock. This is significantly more challenging than drilling in the sedimentary basins that host most of the world’s oil and gas reserves. 

For a location to be suitable for a next-generation geothermal installation requires not only heat, but also a fluid (usually water) to carry the heat. Water circulated through the rock formation to extract heat can be present naturally or brought from elsewhere and injected into the reservoir. This type of system also requires connected permeability such as an engineered fracture network oriented to prevent significant fluid losses and to channel fluid toward the extraction well. Closed-loop (advanced) systems replace the freely circulating water with a working fluid that has favorable thermal characteristics and that is confined in piping.

Various geophysical methods are used to find sites with sufficient heat within a few kilometers of the surface, a prerequisite for their development as next-generation geothermal installations. Apart from direct measurements of temperatures in test boreholes, electrical resistivity and magnetotelluric surveys are among the most useful for inferring subsurface temperature regimes. Both techniques infer the electrical conductivity structure beneath the ground, permitting the identification of relatively warmer and more permeable rocks.

Drilling is often the most time-consuming and expensive part of preparing a site for a geothermal plant. This is particularly true for next-generation geothermal, where the targets can be deep, or the system design may require large-scale horizontal drilling. Over the past few years, numerous innovations have increased drilling rates and attainable depths and temperatures and also lowered costs. Nonetheless, even with high-quality geophysical surveys, “you may spend $10 million on an exploratory well and find no heat,” says Andrew Inglis, the geothermal channel venture builder at MIT Proto Ventures. 

Superhot geothermal, a next-generation geothermal approach that is advancing rapidly, presents special challenges. The metal drilling tools, the rocks in the formation, and circulating fluids all behave differently at temperatures of several hundred degrees, and standard practices, materials, and sensors must be significantly modified to tolerate the tough conditions. Once temperatures exceed 374 C in a borehole even ~1 km deep, water reaches a supercritical state. This presents substantial advantages for extracting heat from the formation, but introduces the specter of rapid metal corrosion and precipitation of salts and silica that can quickly foul a borehole. Researchers are investigating substitution of supercritical carbon dioxide for water as a working fluid for superhot geothermal.

MIT innovations advancing next-generation geothermal

The millimeter-wave drilling technology invented at PSFC and being commercialized by Quaise Energy is the highest-profile next-generation geothermal innovation to emerge from MIT so far. Millimeter-wave technology uses microwave energy to vaporize rock and could prove to be several times faster than conventional drilling. PSFC and a multidisciplinary MIT team are devising a dedicated laboratory to study how millimeter-wave drilling interacts with crystalline rock at realistic pressure and temperature conditions, and to test improvements to the existing technology. Steve Wukitch, interim director and principal research scientist at PSFC, notes that “the facility we are building at MIT will allow us to test samples 500 times larger than is currently possible. This is an important step for investigating technologies that could unlock superhot geothermal energy."

MIT Proto Ventures, which focuses on creating startups based on technology invented at MIT, currently hosts a dedicated geothermal energy channel led by Inglis. Since arriving at MIT in late 2024, Inglis has identified inventions and research that could advance next-generation geothermal from disciplines as disparate as mechanical and materials engineering, earth sciences, and chemistry. Examples of technologies originating with MIT researchers include sensors that measure micro-cracking in high-temperature rock, advanced metal alloys that could handle superhot fluids at a fraction of the cost of titanium, and anti-fouling coatings to protect pipes from the caustic geofluids common in hot, deep systems.

MITEI Spring Symposium

At the recent MITEI Spring Symposium, these MIT innovators introduced their technology to MITEI member companies in a session led by Inglis. Wukitch, who moderated a panel on advanced drilling, described the planned millimeter-wave testbed, and Duenas-Martinez led a panel on power generation and storage. Terra Rogers, director for superhot rock geothermal energy at the CATF and the organizer of the joint CATF-MITEI GeoTech Summit on March 5, led a discussion of international and U.S. policies and the regulatory environment for expansion of next-generation geothermal. 

Poster presenters included MIT graduate students and researchers, MIT’s D-Lab, and the Geo@MIT geothermal-focused MIT student group, which was recognized with a 2024 bonus award by the U.S. Department of Energy’s Geothermal Technologies Office in the nationwide EnergyTech University Prize competition.  

In 2025, Google, Amazon, Microsoft and Meta collectively spent US$380 billion on building artificial-intelligence tools. That number is expected to surge still higher this year, to $650 billion, to fund the building of physical infrastructure, such as data centers (see go.nature.com/3lzf79q). Moreover, these firms are spending lavishly on one particular segment: top technical talent.

Meta reportedly offered a single AI researcher, who had cofounded a start-up firm focused on training AI agents to use computers, a compensation package of $250 million over four years (see ...

The conflict threatens to deflate global confidence in natural gas as President Donald Trump hopes to expand U.S. exports.

A former Trump energy adviser is urging the administration to study and regulate solar geoengineering before an adversary weaponizes it.

Republican state attorneys general say two of the nation’s leading scientific bodies helped develop educational materials that favor climate activists.

Republican lawmakers are rushing to defend an agency that has been under administration scrutiny.

The lawsuit is the Trump administration's latest challenge to state action on climate change.

Environmental and scientific groups can still access the underlying data and haven't shown an injury to sue over, the judge ruled.

The automotive package limits technological neutrality, while the Industrial Accelerator Act will hinder exports, CEO Oliver Zipse argues.

It's unclear if Sublime Systems can fulfill a deal with Microsoft to supply as much as 622,500 metric tons of cement over five to eight years.

The phenomenon is poised to add extra warmth to a planet that’s rapidly heating due to human-caused climate change.

Temperatures in Cape Town climbed to 108 degrees Fahrenheit on Wednesday, the highest since records began 66 years ago.

MIT neuroscientists have figured out how the brain is able to focus on a single voice among a cacophony of many voices, shedding light on a longstanding neuroscientific phenomenon known as the cocktail party problem.

This attentional focus becomes necessary when you’re in any crowded environment, such as a cocktail party, with many conversations going on at once. Somehow, your brain is able to follow the voice of the person you’re talking to, despite all the other voices that you’re hearing in the background.

Using a computational model of the auditory system, the MIT team found that amplifying the activity of the neural processing units that respond to features of a target voice, such as its pitch, allows that voice to be boosted to the forefront of attention.

“That simple motif is enough to cause much of the phenotype of human auditory attention to emerge, and the model ends up reproducing a very wide range of human attentional behaviors for sound,” says Josh McDermott, a professor of brain and cognitive sciences at MIT, a member of MIT’s McGovern Institute for Brain Research and Center for Brains, Minds, and Machines, and the senior author of the study.

The findings are consistent with previous studies showing that when people or animals focus on a specific auditory input, neurons in the auditory cortex that respond to features of the target stimulus amplify their activity. This is the first study to show that extra boost is enough to explain how the brain solves the cocktail party problem.

Ian Griffith, a graduate student in the Harvard Program in Speech and Hearing Biosciences and Technology, who is advised by McDermott, is the lead author of the paper. MIT graduate student R. Preston Hess is also an author of the paper, which appears today in Nature Human Behavior.

Modeling attention

Neuroscientists have been studying the phenomenon of selective attention for decades. Many studies in people and animals have shown that when focusing on a particular stimulus like the sound of someone’s voice, neurons that are tuned to features of that voice — for example, high pitch — amplify their activity.

When this amplification occurs, neurons’ firing rates are scaled upward, as though multiplied by a number greater than one. It has been proposed that these “multiplicative gains” allow the brain to focus its attention on certain stimuli. Neurons that aren’t tuned to the target feature exhibit a corresponding reduction in activity.

“The responses of neurons tuned to features that are in the target of attention get scaled up,” Griffith says. “Those effects have been known for a very long time, but what’s been unclear is whether that effect is sufficient to explain what happens when you’re trying to pay attention to a voice or selectively attend to one object.”

This question has remained unanswered because computational models of perception haven’t been able to perform attentional tasks such as picking one voice out of many. Such models can readily perform auditory tasks when there is an unambiguous target sound to identify, but they haven’t been able to perform those tasks when other stimuli are competing for their attention.

“None of our models has had the ability that humans have, to be cued to a particular object or a particular sound and then to base their response on that object or that sound. That’s been a real limitation,” McDermott says.

In this study, the MIT team wanted to see if they could train models to perform those types of tasks by enabling the model to produce neuronal activity boosts like those seen in the human brain.

To do that, they began with a neural network that they and other researchers have used to model audition, and then modified the model to allow each of its stages to implement multiplicative gains. Under this architecture, the activation of processing units within the model can be boosted up or down depending on the specific features they represent, such as pitch.

To train the model, on each trial the researchers first fed it a “cue”: an audio clip of the voice that they wanted the model to pay attention to. The unit activations produced by the cue then determined the multiplicative gains that were applied when the model heard a subsequent stimulus.

“Imagine the cue is an excerpt of a voice that has a low pitch. Then, the units in the model that represent low pitch would get multiplied by a large gain, whereas the units that represent high pitch would get attenuated,” Griffith says.

Then, the model was given clips featuring a mix of voices, including the target voice, and asked to identify the second word said by the target voice. The model activations to this mixture were multiplied by the gains that resulted from the previous cue stimulus. This was expected to cause the target voice to be “amplified” within the model, but it was not clear whether this effect would be enough to yield human-like attentional behavior.

The researchers found that under a variety of conditions, the model performed very similarly to humans, and it tended to make errors similar to those that humans make. For example, like humans, it sometimes made mistakes when trying to focus on one of two male voices or one of two female voices, which are more likely to have similar pitches.

“We did experiments measuring how well people can select voices across a pretty wide range of conditions, and the model reproduces the pattern of behavior pretty well,” Griffith says.

Effects of location

Previous research has shown that in addition to pitch, spatial location is a key factor that helps people focus on a particular voice or sound. The MIT team found that the model also learned to use spatial location for attentional selection, performing better when the target voice was at a different location from distractor voices.

The researchers then used the model to discover new properties of human spatial attention. Using their computational model, the researchers were able to test all possible combinations of target locations and distractor locations, an undertaking that would be hugely time-consuming with human subjects.

“You can use the model as a way to screen large numbers of conditions to look for interesting patterns, and then once you find something interesting, you can go and do the experiment in humans,” McDermott says.

These experiments revealed that the model was much better at correctly selecting the target voice when the target and distractor were at different locations in the horizontal plane. When the sounds were instead separated in the vertical plane, this task became much more difficult. When the researchers ran a similar experiment with human subjects, they observed the same result.

“That was just one example where we were able to use the model as an engine for discovery, which I think is an exciting application for this kind of model,” McDermott says.

Another application the researchers are pursuing is using this kind of model to simulate listening through a cochlear implant. These studies, they hope, could lead to improvements in cochlear implants that could help people with such implants focus their attention more successfully in noisy environments.

The research was funded by the National Institutes of Health.

Nature Climate Change, Published online: 13 March 2026; doi:10.1038/s41558-026-02585-1

The massive carbon store in soils is vulnerable to anthropogenic warming. Now, a study shows that climate-driven changes in precipitation can mediate soil carbon responses to warming, with drought amplifying soil carbon losses.

Nature Climate Change, Published online: 13 March 2026; doi:10.1038/s41558-026-02583-3

Increasing risks of post-experimental ecology

Nature Climate Change, Published online: 13 March 2026; doi:10.1038/s41558-026-02580-6

Post-growth scholarship seeks to address the limitations of growth-oriented mitigation scenarios by exploring the potential of profound socio-economic transformations. This Perspective synthesizes core principles for modelling post-growth futures.

Nature Climate Change, Published online: 13 March 2026; doi:10.1038/s41558-026-02584-2

The response of soil carbon to warming is critical feedback that has been difficult to constrain. This study uses a long-term experiment to show that precipitation modulates microbial and therefore carbon dynamics; drought leads to carbon loss with warming, but wet conditions increase soil carbon.

Characterized by weakened or damaged heart musculature, heart failure results in the gradual buildup of fluid in a patient’s lungs, legs, feet, and other parts of the body. The condition is chronic and incurable, often leading to arrhythmias or sudden cardiac arrest. For many centuries, bloodletting and leeches were the treatment of choice, famously practiced by barber surgeons in Europe, during a time when physicians rarely operated on patients. 

In the 21st century, the management of heart failure has become decidedly less medieval: Today, patients undergo a combination of healthy lifestyle changes, prescription of medications, and sometimes use pacemakers. Yet heart failure remains one of the leading causes of morbidity and mortality, placing a substantial burden on health-care systems across the globe. 

“About half of the people diagnosed with heart failure will die within five years of diagnosis,” says Teya Bergamaschi, an MIT PhD student in the lab of Nina T. and Robert H. Rubin Professor Collin Stultz and the co-first author of a new paper introducing a deep learning model for predicting heart failure. “Understanding how a patient will fare after hospitalization is really important in allocating finite resources.”

The paper, published in Lancet eClinical Medicine by a team of researchers at MIT, Mass General Brigham, and Harvard Medical School, shares results from developing and testing PULSE-HF, which stands loosely for “Predict changes in left ventricULar Systolic function from ECGs of patients who have Heart Failure.” The project was conducted in Stultz’s lab, which is affiliated with the MIT Abdul Latif Jameel Clinic for Machine Learning in Health. Developed and retrospectively tested across three different patient cohorts from Massachusetts General Hospital, Brigham and Women’s Hospital, and MIMIC-IV (a publicly available dataset), the deep learning model accurately predicts changes in the left ventricular ejection fraction (LVEF), which is the percentage of blood being pumped out of the left ventricle of the heart.

A healthy human heart pumps out about 50 to 70 percent of blood from the left ventricle with each beat — anything less is considered a sign of a potential problem. “The model takes an [electrocardiogram] and outputs a prediction of whether or not there will be an ejection fraction within the next year that falls below 40 percent,” says Tiffany Yau, an MIT PhD student in Stultz’s lab who is also co-first author of the PULSE-HF paper. “That is the most severe subgroup of heart failure.” 

If PULSE-HF predicts that a patient’s ejection fraction is likely to worsen within a year, the clinician can prioritize the patient for follow-up. Subsequently, lower-risk patients can reduce their number of hospital visits and the amount of time spent getting 10 electrodes adhered to their body for a 12-lead ECG. The model can also be deployed in low-resource clinical settings, including doctors offices in rural areas that don’t typically have a cardiac sonographer employed to run ultrasounds on a daily basis.

“The biggest thing that distinguishes [PULSE-HF] from other heart failure ECG methods is instead of detection, it does forecasting,” says Yau. The paper notes that to date, no other methods exist for predicting future LVEF decline among patients with heart failure.

During the testing and validation process, the researchers used a metric known as "area under the receiver operating characteristic curve" (AUROC) to measure PULSE-HF’s performance. AUROC is typically used to measure a model’s ability to discriminate between classes on a scale from 0 to 1, with 0.5 being random and 1 being perfect. PULSE-HF achieved AUROCs ranging from 0.87 to 0.91 across all three patient cohorts.

Notably, the researchers also built a version of PULSE-HF for single-lead ECGs, meaning only one electrode needs to be placed on the body. While 12-lead ECGs are generally considered superior for being more comprehensive and accurate, the performance of the single-lead version of PULSE-HF was just as strong as the 12-lead version.

Despite the elegant simplicity behind the idea of PULSE-HF, like most clinical AI research, it belies a laborious execution. “It’s taken years [to complete this project],” Bergamaschi recalls. “It’s gone through many iterations.” 

One of the team’s biggest challenges was collecting, processing, and cleaning the ECG and echocardiogram datasets. While the model aims to forecast a patient’s ejection fraction, the labels for the training data weren’t always readily available. Much like a student learning from a textbook with an answer key, labeling is critical for helping machine-learning models correctly identify patterns in data.

Clean, linear text in the form of TXT files typically works best when training models. But echocardiogram files typically come in the form of PDFs, and when PDFs are converted to TXT files, the text (which gets broken up by line breaks and formatting) becomes difficult for the model to read. The unpredictable nature of real-life scenarios, like a restless patient or a loose lead, also marred the data. “There are a lot of signal artifacts that need to be cleaned,” Bergamaschi says. “It’s kind of a never-ending rabbit hole.”

While Bergamaschi and Yau acknowledge that more complicated methods could help filter the data for better signals, there is a limit to the usefulness of these approaches. “At what point do you stop?” Yau asks. “You have to think about the use case — is it easiest to have this model that works on data that is slightly messy? Because it probably will be.”

The researchers anticipate that the next step for PULSE-HF will be testing the model in a prospective study on real patients, whose future ejection fraction is unknown.

Despite the challenges inherent to bringing clinical AI tools like PULSE-HF over the finish line, including the possible risk of prolonging a PhD by another year, the students feel that the years of hard work were worthwhile. 

“I think things are rewarding partially because they’re challenging,” Bergamaschi says. “A friend said to me, ‘If you think you will find your calling after graduation, if your calling is truly calling, it will be there in the one additional year it takes you to graduate.’ … The way we’re measured as researchers in [the ML and health] space is different from other researchers in ML space. Everyone in this community understands the unique challenges that exist here.”

“There’s too much suffering in the world,” says Yau, who joined Stultz’s lab after a health event made her realize the importance of machine learning in health care. “Anything that tries to ease suffering is something that I would consider a valuable use of my time.” 

“It’s a real validation of all the work behind the scenes,” says Karl Berggren, faculty head of electrical engineering within the MIT Department of Electrical Engineering and Computer Science (EECS). He’s looking at the numbers of new enrollees in Course 6-5, Electrical Engineering With Computing, the flagship electrical engineering degree offered by EECS, which was launched last fall. 

The new major has been embraced by the MIT student community. “The fact that Course 6-5 is now the third-most selected major among first-year students shows that the department is clearly meeting a growing need for a curriculum that bridges electrical engineering and computing. This growth is coming from students already interested in pursuing a degree in EECS,” says Anantha Chandrakasan, MIT’s provost. “The major was thoughtfully designed to offer a strong foundation in core electrical engineering concepts — such as circuits, signals, systems, and architecture — while also providing well-structured specialization tracks that prepare students for the future of the field.”

Those tracks include structured paths to explore not only the traditional domains of electrical engineering (such as hardware design and energy systems), but cutting-edge fields such as nanoelectronics, quantum systems engineering, and photonics. 

“They are very flexible, and essentially allow me to take whatever I want, with the tracks filling up almost automatically,” says 6-5 major Charles Reischer. “For me, it essentially reduces the amount of specific required classes in the major, which has been helpful for choosing the classes I find interesting.” 

Jelena Notaros, who helped develop the Electromagnetics and Photonics track within the new major, has seen the new wave of student interest from the other side. “It’s been incredibly rewarding … I think students are excited to have the opportunity to take a class where they can learn about a cutting-edge field and test real state-of-the-art chip hardware using industry-standard equipment.” Notaros’s class, 6.2320  (Silicon Photonics), includes features not found in a university class anywhere else, such as a sequence in which students can test actual chips at three electronic-photonic probe stations. 

Another 6-5 track, Quantum Systems Engineering, features direct student access to quantum hardware, including electron-nuclear systems and state-of-the-art simulations methods and tools. Professor Dirk Englund, who teaches multiple courses within the track, explains, “it’s been so successful in part through strong industry support, including from QuTools Inc. Students work with the same tech we use in the Boston-Area Quantum Network Testbed — the metro quantum network linking MIT, Lincoln Lab, and Harvard, and the NSF CQN.” 

Many of Englund’s students have gone on to pursue a career in quantum information science, either in grad school or in industry. “Students recognize quantum engineering is the future. They see they’re building the foundation for metro-scale quantum networks.” 

The new curriculum’s emphasis on hands-on learning is deliberate, and ubiquitous throughout 6-5. Within the Circuits track, students who enroll in class 6.208 (Semiconductor Electronic Circuits) will get an opportunity not only to design a circuit, but to actually see their design made, in a process called “tape-out.” Professor Ruonan Han, who helped design the course, explains, “a tape-out is a perfect training that poses [real-life] constraints and forces the students to solve practical engineering problems. Through circuit simulation using mainstream industry CAD tools, the students better understand how deep-scaled transistors differ from the ideal behaviors taught in textbooks. By drawing the layouts of the silicon and metal patterns, the students learn how a modern chip is made, layer by layer. The complex (and often frustrating) rules of the layout also keep reminding the students of all the technical limitations during the chip manufacturing, and make them better appreciate all the accomplishments in semiconductor manufacturing. Even the firm and non-negotiable tape-out submission deadline forces the students to not only wisely manage their development timeline, but also to experience heart-beating moments when decisions on critical engineering trade-offs should be made (in order to deliver). To these students, it was such relentless efforts that gave them lots of satisfaction and pride when they finally hold their own chips in hand.” 

The sense of completing a full problem-solving cycle is echoed in class 6.900 (Engineering for Impact), a capstone course designed by Professor Joel Voldman, a former faculty head of electrical engineering, along with Senior Lecturer Joe Steinmeyer. Over the course of a semester, students team with city governments and nonprofits to solve complex local issues. The course is designed not only to introduce students to realistic project management factors (such as budgets, timelines, and stakeholders), but also to give them a taste of the satisfaction of engineering a solution that meets a real community’s need. 

“I’ve taken 6.900, and it’s been eye-opening in the collaboration of hardware, firmware, and software to create a cohesive and working product,” says Andrea Leang, a senior majoring in 6-2 who nonetheless decided to try the new course. “In my 6-2 experience, I spent the first two years taking more CS [computer science] classes, but as I went into junior year, I wanted to explore more EE [electrical engineering].” That desire led Leang to Voltage, the student group for electrical engineers. “Honestly, it was the first big community of EE I’ve joined. Joining Voltage opened my eyes to what MIT had to offer on EE, and a community who was enthusiastic to share their knowledge.” 

Matthew Kim, one of the executives of the Voltage group, echoes Leang’s experience. “​​It has been great working [...] to build a community for EE. We heard faculty say that they wanted to be more engaged with students and communicate more, and it has definitely been felt with the restart and support of Voltage. And I’m hopeful that the community will continue to grow.” 

That growth has been rapid. The new major’s enrollment is now roughly equivalent to the combined enrollment in the older 6-1 and 6-2 programs, showing the desirability of a major that incorporates fundamentals of both computing and electrical engineering. 

Department head Professor Asu Ozdaglar is thrilled with the energizing effect of the new major. “We are delighted to see the initial success of the 6-5 major, which provides our students an exciting and forward-looking curriculum, developed through extensive work and great deal of thought by electrical engineering faculty. The new curriculum reflects the critical role computing plays in electrical engineering, whether in designing new devices and circuits, analyzing data, or in studying complex systems, which almost invariably combine hardware and software."

“What excites me most about this major is how it empowers students to bring ideas to life — from the invisible signals that connect our world to the complex systems that drive modern technology,” says Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and the Henry Warren Professor of Electrical Engineering and Computer Science. “Students are using computation as a creative and analytical tool to expand the boundaries of engineering. They gain a deep understanding of how hardware and software come together to drive technological progress.” 

The new degree program’s designers are gratified by the swell of student interest. 

“The buzz surrounding the classes and the new 6-5 degree program is fantastic,” says Voldman. “It’s great to see the strong student interest in what we’ve put together.”