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      Report: CHIPS Act just the first step in addressing threats to US leadership in advanced computing

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      When Liu He, a Chinese economist, politician, and “chip czar,” was tapped to lead the charge in a chipmaking arms race with the United States, his message lingered in the air, leaving behind a dewy glaze of tension: “For our country, technology is not just for growth… it is a matter of survival.”

      Once upon a time, the United States’ early technological prowess positioned the nation to outpace foreign rivals and cultivate a competitive advantage for domestic businesses. Yet, 30 years later, America’s lead in advanced computing is continuing to wane. What happened?

      A new report from an MIT researcher and two colleagues sheds light on the decline in U.S. leadership. The scientists looked at high-level measures to examine the shrinkage: overall capabilities, supercomputers, applied algorithms, and semiconductor manufacturing. Through their analysis, they found that not only has China closed the computing gap with the U.S., but nearly 80 percent of American leaders in the field believe that their Chinese competitors are improving capabilities faster — which, the team says, suggests a “broad threat to U.S. competitiveness.”

      To delve deeply into the fray, the scientists conducted the Advanced Computing Users Survey, sampling 120 top-tier organizations, including universities, national labs, federal agencies, and industry. The team estimates that this group comprises one-third and one-half of all the most significant computing users in the United States.

      “Advanced computing is crucial to scientific improvement, economic growth and the competitiveness of U.S. companies,” says Neil Thompson, director of the FutureTech Research Project at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), who helped lead the study.

      Thompson, who is also a principal investigator at MIT’s Initiative on the Digital Economy, wrote the paper with Chad Evans, executive vice president and secretary and treasurer to the board at the Council on Competitiveness, and Daniel Armbrust, who is the co-founder, initial CEO, and member of the board of directors at Silicon Catalyst and former president of SEMATECH, the semiconductor consortium that developed industry roadmaps.

      The semiconductor, supercomputer, and algorithm bonanza

      Supercomputers — the room-sized, “giant calculators” of the hardware world — are an industry no longer dominated by the United States. Through 2015, about half of the most powerful computers were sitting firmly in the U.S., and China was growing slowly from a very slow base. But in the past six years, China has swiftly caught up, reaching near parity with America.

      This disappearing lead matters. Eighty-four percent of U.S. survey respondents said they’re computationally constrained in running essential programs. “This result was telling, given who our respondents are: the vanguard of American research enterprises and academic institutions with privileged access to advanced national supercomputing resources,” says Thompson. 

      With regards to advanced algorithms, historically, the U.S. has fronted the charge, with two-thirds of all significant improvements dominated by U.S.-born inventors. But in recent decades, U.S. dominance in algorithms has relied on bringing in foreign talent to work in the U.S., which the researchers say is now in jeopardy. China has outpaced the U.S. and many other countries in churning out PhDs in STEM fields since 2007, with one report postulating a near-distant future (2025) where China will be home to nearly twice as many PhDs than in the U.S. China’s rise in algorithms can also be seen with the “Gordon Bell Prize,” an achievement for outstanding work in harnessing the power of supercomputers in varied applications. U.S. winners historically dominated the prize, but China has now equaled or surpassed Americans’ performance in the past five years.

      While the researchers note the CHIPS and Science Act of 2022 is a critical step in re-establishing the foundation of success for advanced computing, they propose recommendations to the U.S. Office of Science and Technology Policy. 

      First, they suggest democratizing access to U.S. supercomputing by building more mid-tier systems that push boundaries for many users, as well as building tools so users scaling up computations can have less up-front resource investment. They also recommend increasing the pool of innovators by funding many more electrical engineers and computer scientists being trained with longer-term US residency incentives and scholarships. Finally, in addition to this new framework, the scientists urge taking advantage of what already exists, via providing the private sector access to experimentation with high-performance computing through supercomputing sites in academia and national labs.

      All that and a bag of chips

      Computing improvements depend on continuous advances in transistor density and performance, but creating robust, new chips necessitate a harmonious blend of design and manufacturing.

      Over the last six years, China was not known as the savants of noteworthy chips. In fact, in the past five decades, the U.S. designed most of them. But this changed in the past six years when China created the HiSilicon Kirin 9000, propelling itself to the international frontier. This success was mainly obtained through partnerships with leading global chip designers that began in the 2000s. Now, China now has 14 companies among the world’s top 50 fabless designers. A decade ago, there was only one. 

      Competitive semiconductor manufacturing has been more mixed, where U.S.-led policies and internal execution issues have slowed China’s rise, but as of July 2022, the Semiconductor Manufacturing International Corporation (SMIC) has evidence of 7 nanometer logic, which was not expected until much later. However, with extreme ultraviolet export restrictions, progress below 7 nm means domestic technology development would be expensive. Currently, China is only at parity or better in two out of 12 segments of the semiconductor supply chain. Still, with government policy and investments, the team expects a whopping increase to seven segments in 10 years. So, for the moment, the U.S. retains leadership in hardware manufacturing, but with fewer dimensions of advantage.

      The authors recommend that the White House Office of Science and Technology Policy work with key national agencies, such as the U.S. Department of Defense, U.S. Department of Energy, and the National Science Foundation, to define initiatives to build the hardware and software systems needed for important computing paradigms and workloads critical for economic and security goals. “It is crucial that American enterprises can get the benefit of faster computers,” says Thompson. “With Moore’s Law slowing down, the best way to do this is to create a portfolio of specialized chips (or “accelerators”) that are customized to our needs.”

      The scientists further believe that to lead the next generation of computing, four areas must be addressed. First, by issuing grand challenges to the CHIPS Act National Semiconductor Technology Center, researchers and startups would be motivated to invest in research and development and to seek startup capital for new technologies in areas such as spintronics, neuromorphics, optical and quantum computing, and optical interconnect fabrics. By supporting allies in passing similar acts, overall investment in these technologies would increase, and supply chains would become more aligned and secure. Establishing test beds for researchers to test algorithms on new computing architectures and hardware would provide an essential platform for innovation and discovery. Finally, planning for post-exascale systems that achieve higher levels of performance through next-generation advances would ensure that current commercial technologies don’t limit future computing systems.

      “The advanced computing landscape is in rapid flux — technologically, economically, and politically, with both new opportunities for innovation and rising global rivalries,” says Daniel Reed, Presidential Professor and professor of computer science and electrical and computer engineering at the University of Utah. “The transformational insights from both deep learning and computational modeling depend on both continued semiconductor advances and their instantiation in leading edge, large-scale computing systems — hyperscale clouds and high-performance computing systems. Although the U.S. has historically led the world in both advanced semiconductors and high-performance computing, other nations have recognized that these capabilities are integral to 21st century economic competitiveness and national security, and they are investing heavily.”

      The research was funded, in part, through Thompson’s grant from Good Ventures, which supports his FutureTech Research Group. The paper is being published by the Georgetown Public Policy Review. More

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      A new chip for decoding data transmissions demonstrates record-breaking energy efficiency

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      Imagine using an online banking app to deposit money into your account. Like all information sent over the internet, those communications could be corrupted by noise that inserts errors into the data.

      To overcome this problem, senders encode data before they are transmitted, and then a receiver uses a decoding algorithm to correct errors and recover the original message. In some instances, data are received with reliability information that helps the decoder figure out which parts of a transmission are likely errors.

      Researchers at MIT and elsewhere have developed a decoder chip that employs a new statistical model to use this reliability information in a way that is much simpler and faster than conventional techniques.

      Their chip uses a universal decoding algorithm the team previously developed, which can unravel any error correcting code. Typically, decoding hardware can only process one particular type of code. This new, universal decoder chip has broken the record for energy-efficient decoding, performing between 10 and 100 times better than other hardware.

      This advance could enable mobile devices with fewer chips, since they would no longer need separate hardware for multiple codes. This would reduce the amount of material needed for fabrication, cutting costs and improving sustainability. By making the decoding process less energy intensive, the chip could also improve device performance and lengthen battery life. It could be especially useful for demanding applications like augmented and virtual reality and 5G networks.

      “This is the first time anyone has broken below the 1 picojoule-per-bit barrier for decoding. That is roughly the same amount of energy you need to transmit a bit inside the system. It had been a big symbolic threshold, but it also changes the balance in the receiver of what might be the most pressing part from an energy perspective — we can move that away from the decoder to other elements,” says Muriel Médard, the School of Science NEC Professor of Software Science and Engineering, a professor in the Department of Electrical Engineering and Computer Science, and a co-author of a paper presenting the new chip.

      Médard’s co-authors include lead author Arslan Riaz, a graduate student at Boston University (BU); Rabia Tugce Yazicigil, assistant professor of electrical and computer engineering at BU; and Ken R. Duffy, then director of the Hamilton Institute at Maynooth University and now a professor at Northeastern University, as well as others from MIT, BU, and Maynooth University. The work is being presented at the International Solid-States Circuits Conference.

      Smarter sorting

      Digital data are transmitted over a network in the form of bits (0s and 1s). A sender encodes data by adding an error-correcting code, which is a redundant string of 0s and 1s that can be viewed as a hash. Information about this hash is held in a specific code book. A decoding algorithm at the receiver, designed for this particular code, uses its code book and the hash structure to retrieve the original information, which may have been jumbled by noise. Since each algorithm is code-specific, and most require dedicated hardware, a device would need many chips to decode different codes.

      The researchers previously demonstrated GRAND (Guessing Random Additive Noise Decoding), a universal decoding algorithm that can crack any code. GRAND works by guessing the noise that affected the transmission, subtracting that noise pattern from the received data, and then checking what remains in a code book. It guesses a series of noise patterns in the order they are likely to occur.

      Data are often received with reliability information, also called soft information, that helps a decoder figure out which pieces are errors. The new decoding chip, called ORBGRAND (Ordered Reliability Bits GRAND), uses this reliability information to sort data based on how likely each bit is to be an error.

      But it isn’t as simple as ordering single bits. While the most unreliable bit might be the likeliest error, perhaps the third and fourth most unreliable bits together are as likely to be an error as the seventh-most unreliable bit. ORBGRAND uses a new statistical model that can sort bits in this fashion, considering that multiple bits together are as likely to be an error as some single bits.

      “If your car isn’t working, soft information might tell you that it is probably the battery. But if it isn’t the battery alone, maybe it is the battery and the alternator together that are causing the problem. This is how a rational person would troubleshoot — you’d say that it could actually be these two things together before going down the list to something that is much less likely,” Médard says.

      This is a much more efficient approach than traditional decoders, which would instead look at the code structure and have a performance that is generally designed for the worst-case.

      “With a traditional decoder, you’d pull out the blueprint of the car and examine each and every piece. You’ll find the problem, but it will take you a long time and you’ll get very frustrated,” Médard explains.

      ORBGRAND stops sorting as soon as a code word is found, which is often very soon. The chip also employs parallelization, generating and testing multiple noise patterns simultaneously so it finds the code word faster. Because the decoder stops working once it finds the code word, its energy consumption stays low even though it runs multiple processes simultaneously.

      Record-breaking efficiency

      When they compared their approach to other chips, ORBGRAND decoded with maximum accuracy while consuming only 0.76 picojoules of energy per bit, breaking the previous performance record. ORBGRAND consumes between 10 and 100 times less energy than other devices.

      One of the biggest challenges of developing the new chip came from this reduced energy consumption, Médard says. With ORBGRAND, generating noise sequences is now so energy-efficient that other processes the researchers hadn’t focused on before, like checking the code word in a code book, consume most of the effort.

      “Now, this checking process, which is like turning on the car to see if it works, is the hardest part. So, we need to find more efficient ways to do that,” she says.

      The team is also exploring ways to change the modulation of transmissions so they can take advantage of the improved efficiency of the ORBGRAND chip. They also plan to see how their technique could be utilized to more efficiently manage multiple transmissions that overlap.

      The research is funded, in part, by the U.S. Defense Advanced Research Projects Agency (DARPA) and Science Foundation Ireland. More

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      A new way for quantum computing systems to keep their cool

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      Heat causes errors in the qubits that are the building blocks of a quantum computer, so quantum systems are typically kept inside refrigerators that keep the temperature just above absolute zero (-459 degrees Fahrenheit).

      But quantum computers need to communicate with electronics outside the refrigerator, in a room-temperature environment. The metal cables that connect these electronics bring heat into the refrigerator, which has to work even harder and draw extra power to keep the system cold. Plus, more qubits require more cables, so the size of a quantum system is limited by how much heat the fridge can remove.

      To overcome this challenge, an interdisciplinary team of MIT researchers has developed a wireless communication system that enables a quantum computer to send and receive data to and from electronics outside the refrigerator using high-speed terahertz waves.

      A transceiver chip placed inside the fridge can receive and transmit data. Terahertz waves generated outside the refrigerator are beamed in through a glass window. Data encoded onto these waves can be received by the chip. That chip also acts as a mirror, delivering data from the qubits on the terahertz waves it reflects to their source.

      This reflection process also bounces back much of the power sent into the fridge, so the process generates only a minimal amount of heat. The contactless communication system consumes up to 10 times less power than systems with metal cables.

      “By having this reflection mode, you really save the power consumption inside the fridge and leave all those dirty jobs on the outside. While this is still just a preliminary prototype and we have some room to improve, even at this point, we have shown low power consumption inside the fridge that is already better than metallic cables. I believe this could be a way to build largescale quantum systems,” says senior author Ruonan Han, an associate professor in the Department of Electrical Engineering and Computer Sciences (EECS) who leads the Terahertz Integrated Electronics Group.

      Han and his team, with expertise in terahertz waves and electronic devices, joined forces with associate professor Dirk Englund and the Quantum Photonics Laboratory team, who provided quantum engineering expertise and joined in conducting the cryogenic experiments.

      Joining Han and Englund on the paper are first author and EECS graduate student Jinchen Wang; Mohamed Ibrahim PhD ’21; Isaac Harris, a graduate student in the Quantum Photonics Laboratory; Nathan M. Monroe PhD ’22; Wasiq Khan PhD ’22; and Xiang Yi, a former postdoc who is now a professor at the South China University of Technology. The paper will be presented at the International Solid-States Circuits Conference.

      Tiny mirrors

      The researchers’ square transceiver chip, measuring about 2 millimeters on each side, is placed on a quantum computer inside the refrigerator, which is called a cryostat because it maintains cryogenic temperatures. These super-cold temperatures don’t damage the chip; in fact, they enable it to run more efficiently than it would at room temperature.

      The chip sends and receives data from a terahertz wave source outside the cryostat using a passive communication process known as backscatter, which involves reflections. An array of antennas on top of the chip, each of which is only about 200 micrometers in size, act as tiny mirrors. These mirrors can be “turned on” to reflect waves or “turned off.”

      The terahertz wave generation source encodes data onto the waves it sends into the cryostat, and the antennas in their “off” state can receive those waves and the data they carry.

      When the tiny mirrors are turned on, they can be set so they either reflect a wave in its current form or invert its phase before bouncing it back. If the reflected wave has the same phase, that represents a 0, but if the phase is inverted, that represents a 1. Electronics outside the cryostat can interpret those binary signals to decode the data.

      “This backscatter technology is not new. For instance, RFIDs are based on backscatter communication. We borrow that idea and bring it into this very unique scenario, and I think this leads to a good combination of all these technologies,” Han says.

      Terahertz advantages

      The data are transmitted using high-speed terahertz waves, which are located on the electromagnetic spectrum between radio waves and infrared light.

      Because terahertz waves are much smaller than radio waves, the chip and its antennas can be smaller, too, which would make the device easier to manufacture at scale. Terahertz waves also have higher frequencies than radio waves, so they can transmit data much faster and move larger amounts of information.

      But because terahertz waves have lower frequencies than the light waves used in photonic systems, the terahertz waves carry less quantum noise, which leads to less interference with quantum processors.

      Importantly, the transceiver chip and terahertz link can be fully constructed with standard fabrication processes on a CMOS chip, so they can be integrated into many current systems and techniques.

      “CMOS compatibility is important. For example, one terahertz link could deliver a large amount of data and feed it to another cryo-CMOS controller, which can split the signal to control multiple qubits simultaneously, so we can reduce the quantity of RF cables dramatically. This is very promising.” Wang says.

      The researchers were able to transmit data at 4 gigabits per second with their prototype, but Han says the sky is nearly the limit when it comes to boosting that speed. The downlink of the contactless system posed about 10 times less heat load than a system with metallic cables, and the temperature of the cryostat fluctuated up to a few millidegrees during experiments.

      Now that the researchers have demonstrated this wireless technology, they want to improve the system’s speed and efficiency using special terahertz fibers, which are only a few hundred micrometers wide. Han’s group has shown that these plastic wires can transmit data at a rate of 100 gigabits per second and have much better thermal insulation than fatter, metal cables.

      The researchers also want to refine the design of their transceiver to improve scalability and continue boosting its energy efficiency. Generating terahertz waves requires a lot of power, but Han’s group is studying more efficient methods that utilize low-cost chips. Incorporating this technology into the system could make the device more cost-effective.

      The transceiver chip was fabricated through the Intel University Shuttle Program. More

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      New chip for mobile devices knocks out unwanted signals

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      Imagine sitting in a packed stadium for a pivotal football game — tens of thousands of people are using mobile phones at the same time, perhaps video chatting with friends or posting photos on social media. The radio frequency signals being sent and received by all these devices could cause interference, which slows device performance and drains batteries.

      Designing devices that can efficiently block unwanted signals is no easy task, especially as 5G networks become more universal and future generations of wireless communication systems are developed. Conventional techniques utilize many filters to block a range of signals, but filters are bulky, expensive, and drive up production costs.

      MIT researchers have developed a circuit architecture that targets and blocks unwanted signals at a receiver’s input without hurting its performance. They borrowed a technique from digital signal processing and used a few tricks that enable it to work effectively in a radio frequency system across a wide frequency range.

      Their receiver blocked even high-power unwanted signals without introducing more noise, or inaccuracies, into the signal processing operations. The chip, which performed about 40 times better than other wideband receivers at blocking a special type of interference, does not require any additional hardware or circuitry. This would make the chip easier to manufacture at scale.

      “We are interested in developing electronic circuits and systems that meet the demands of 5G and future generations of wireless communication systems. In designing our circuits, we look for inspirations from other domains, such as digital signal processing and applied electromagnetics. We believe in circuit elegance and simplicity and try to come up with multifunctional hardware that doesn’t require additional power and chip area,” says senior author Negar Reiskarimian, the X-Window Consortium Career Development Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) and a core faculty member of the Microsystems Technology Laboratories.

      Reiskarimian wrote the paper with EECS graduate students Soroush Araei, who is the lead author, and Shahabeddin Mohin. The work is being presented at the International Solid-States Circuits Conference.

      Harmonic interference

      The researchers developed the receiver chip using what is known as a mixer-first architecture. This means that when a radio frequency signal is received by the device, it is immediately converted to a lower-frequency signal before being passed on to the analog-to-digital converter to extract the digital bits that it is carrying. This approach enables the radio to cover a wide frequency range while filtering out interference located close to the operation frequency.

      While effective, mixer-first receivers are susceptible to a particular kind of interference known as harmonic interference. Harmonic interference comes from signals that have frequencies which are multiples of a device’s operating frequency. For instance, if a device operates at 1 gigahertz, then signals at 2 gigahertz, 3 gigahertz, 5 gigahertz, etc., will cause harmonic interference. These harmonics can be indistinguishable from the original signal during the frequency conversion process.    

      “A lot of other wideband receivers don’t do anything about the harmonics until it is time to see what the bits mean. They do it later in the chain, but this doesn’t work well if you have high-power signals at the harmonic frequencies. Instead, we want to remove harmonics as soon as possible to avoid losing information,” Araei says.

      To do this, the researchers were inspired by a concept from digital signal processing known as block digital filtering. They adapted this technique to the analog domain using capacitors, which hold electric charges. The capacitors are charged up at different times as the signal is received, then they are switched off so that charge can be held and used later for processing the data.  

      These capacitors can be connected to each other in various ways, including connecting them in parallel, which enables the capacitors to exchange the stored charges. While this technique can target harmonic interference, the process results in significant signal loss. Stacking capacitors is another possibility, but this method alone is not enough to provide harmonic resilience.

      Most radio receivers already use switched-capacitor circuits to perform frequency conversion. This frequency conversion circuitry can be combined with block filtering to target harmonic interference.

      A precise arrangement

      The researchers found that arranging capacitors in a specific layout, by connecting some of them in series and then performing charge sharing, enabled the device to block harmonic interference without losing any information.

      “People have used these techniques, charge sharing and capacitor stacking, separately before, but never together. We found that both techniques must be done simultaneously to get this benefit. Moreover, we have found out how to do this in a passive way within the mixer without using any additional hardware while maintaining signal integrity and keeping the costs down,” he says.

      They tested the device by simultaneously sending a desired signal and harmonic interference. Their chip was able to block harmonic signals effectively with only a slight reduction in signal strength. It was able to handle signals that were 40 times more powerful than previous, state-of-the-art wideband receivers. More

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      Democratizing education: Bringing MIT excellence to the masses

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      How do you quantify the value of education or measure success? For the team behind the MIT Institute for Data, Systems, and Society’s (IDSS) MicroMasters Program in Statistics and Data Science (SDS), providing over 1,000 individuals from around the globe with access to MIT-level programming feels like a pretty good place to start. 

      Thanks to the MIT-conceived MicroMasters-style format, SDS faculty director Professor Devavrat Shah and his colleagues have eliminated the physical restrictions created by a traditional brick-and-mortar education, allowing 1,178 learners and counting from 89 countries access to an MIT education.

      “Taking classes from a Nobel Prize winner doesn’t happen every day,” says Oscar Vele, a strategic development worker for the town of Cuenca, Ecuador. “My dream has always been to study at MIT. I knew it was not easy — now, through this program, my dream came true.”

      “With an online forum, in principle, admission is no longer the gate — the merit is a gate,” says Shah. “If you take a class that is MIT-level, and if you perform at MIT-level, then you should get MIT-level credentials.”

      The MM SDS program, delivered in collaboration with MIT Open Learning, plays a key role in the IDSS mission of advancing education in data science, and supports MIT’s overarching belief that everyone should be able to access a quality education no matter what their life circumstances may be.

      “Getting a program like this up and running to the point where it has credentials and credibility across the globe, is an important milestone for us,” says Shah. “Basically, for us, it says we are here to stay, and we are just getting started.”

      Since the program launched in 2018, Shah says he and his team have seen learners from all walks of life, from high-schoolers looking for a challenge to late-in-life learners looking to either evolve or refresh their knowledge.

      “Then there are individuals who want to prove to themselves that they can achieve serious knowledge and build a career,” Shah says. “Circumstances throughout their lives, whether it’s the country or socioeconomic conditions they’re born in, they have never had the opportunity to do something like this, and now they have an MIT-level education and credentials, which is a huge deal for them.”

      Many learners overcome challenges to complete the program, from financial hardships to balancing work, home life, and coursework, and finding private, internet-enabled space for learning — not to mention the added complications of a global pandemic. One Ukrainian learner even finished the program after fleeing her apartment for a bomb shelter.

      Remapping the way to a graduate degree

      For Diogo da Silva Branco Magalhaes, a 44-year-old lifelong learner, curiosity and the desire to evolve within his current profession brought him to the MicroMasters program. Having spent 15 years working in the public transport sector, da Silva Branco Magalhaes had a very specific challenge at the front of his mind: artificial intelligence.

      “It’s not science fiction; it’s already here,” he says. “Think about autonomous vehicles, on-demand transportation, mobility as a service — AI and data, in particular, are the driving force of a number of disruptions that will affect my industry.”

      When he signed up for the MicroMasters Program in Statistics and Data Science, da Silva Branco Magalhaes’ said he had no long-term plans, but was taking a first step. “I just wanted to have a first contact with this reality, understand the basics, and then let’s see how it goes,” he describes.

      Now, after earning his credentials in 2021, he finds himself a few weeks into an accelerated master’s program at Northwestern University, one of several graduate pathways supported by the MM SDS program.

      “I was really looking to gain some basic background knowledge; I didn’t expect the level of quality and depth they were able to provide in an online lecture format,” he says. “Having access to this kind of content — it’s a privilege, and now that we have it, we have to make the most of it.”

      A refreshing investment

      As an applied mathematician with 15 years of experience in the U.S. defense sector, Celia Wilson says she felt comfortable with her knowledge, though not 100 percent confident that her math skills could stand up against the next generation.

      “I felt I was getting left behind,” she says. “So I decided to take some time out and invest in myself, and this program was a great opportunity to systematize and refresh my knowledge of statistics and data science.”

      Since completing the course, Wilson says she has secured a new job as a director of data and analytics, where she is confident in her ability to manage a team of the “new breed of data scientists.” It turns out, however, that completing the program has given her an even greater gift than self-confidence.

      “Most importantly,” she adds, “it’s inspired my daughters to tell anyone who will listen that math is definitely for girls.”

      Connecting an engaged community

      Each course is connected to an online forum that allows learners to enhance their experience through real-time conversations with others in their cohort.

      “We have worked hard to provide a scalable version of the traditional teaching assistant support system that you would get in a usual on-campus class, with a great online forum for people to connect with each other as learners,” Shah says.

      David Khachatrian, a data scientist working on improving the drug discovery pipeline, says that leveraging the community to hone his ability to “think clearly and communicate effectively with others” mattered more than anything.

      “Take the opportunity to engage with your community of fellow learners and facilitators — answer questions for others to give back to the community, solidify your own understanding, and practice your ability to explain clearly,” Khachatrian says. “These skills and behaviors will help you to succeed not just in SDS, but wherever you go in the future.”

      “There were a lot of active contributions from a lot of learners and I felt it was really a very strong component of the course,” da Silva Branco Magalhaes adds. “I had some offline contact with other students who are connections that I’ve kept up with to this day.”

      A solid path forward

      “We have a dedicated team supporting the MM SDS community on the MIT side,” Shah says, citing the contributions of Karene Chu, MM SDS assistant director of education; Susana Kevorkova, the MM SDS program manager; and Jeremy Rossen, MM program coordinator. “They’ve done so much to ensure the success of the program and our learners, and they are constantly adding value to the program — like identifying real-time supplementary opportunities for learners to participate in, including the IDSS Policy Hackathon.”

      The program now holds online “graduation” ceremonies, where credential holders from all over the world share their experiences. Says Shah, who looks forward to celebrating the next 1,000 learners: “Every time I think about it, I feel emotional. It feels great, and it keeps us going.” More

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      MIT community members elected to the National Academy of Engineering for 2023

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      Seven MIT researchers are among the 106 new members and 18 international members elected to the National Academy of Engineering (NAE) this week. Fourteen additional MIT alumni, including one member of the MIT Corporation, were also elected as new members.

      One of the highest professional distinctions for engineers, membership to the NAE is given to individuals who have made outstanding contributions to “engineering research, practice, or education, including, where appropriate, significant contributions to the engineering literature” and to “the pioneering of new and developing fields of technology, making major advancements in traditional fields of engineering, or developing/implementing innovative approaches to engineering education.”

      The seven MIT researchers elected this year include:

      Regina Barzilay, the School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science, principal investigator at the Computer Science and Artificial Intelligence Laboratory, and faculty lead for the MIT Abdul Latif Jameel Clinic for Machine Learning in Health, for machine learning models that understand structures in text, molecules, and medical images.

      Markus J. Buehler, the Jerry McAfee (1940) Professor in Engineering from the Department of Civil and Environmental Engineering, for implementing the use of nanomechanics to model and design fracture-resistant bioinspired materials.

      Elfatih A.B. Eltahir SM ’93, ScD ’93, the H.M. King Bhumibol Professor in the Department of Civil and Environmental Engineering, for advancing understanding of how climate and land use impact water availability, environmental and human health, and vector-borne diseases.

      Neil Gershenfeld, director of the Center for Bits and Atoms, for eliminating boundaries between digital and physical worlds, from quantum computing to digital materials to the internet of things.

      Roger D. Kamm SM ’73, PhD ’77, the Cecil and Ida Green Distinguished Professor of Biological and Mechanical Engineering, for contributions to the understanding of mechanics in biology and medicine, and leadership in biomechanics.

      David W. Miller ’82, SM ’85, ScD ’88, the Jerome C. Hunsaker Professor in the Department of Aeronautics and Astronautics, for contributions in control technology for space-based telescope design, and leadership in cross-agency guidance of space technology.

      David Simchi-Levi, professor of civil and environmental engineering, core faculty member in the Institute for Data, Systems, and Society, and principal investigator at the Laboratory for Information and Decision Systems, for contributions using optimization and stochastic modeling to enhance supply chain management and operations.

      Fariborz Maseeh ScD ’90, life member of the MIT Corporation and member of the School of Engineering Dean’s Advisory Council, was also elected as a member for leadership and advances in efficient design, development, and manufacturing of microelectromechanical systems, and for empowering engineering talent through public service.

      Thirteen additional alumni were elected to the National Academy of Engineering this year. They are: Mark George Allen SM ’86, PhD ’89; Shorya Awtar ScD ’04; Inderjit Chopra ScD ’77; David Huang ’85, SM ’89, PhD ’93; Eva Lerner-Lam SM ’78; David F. Merrion SM ’59; Virginia Norwood ’47; Martin Gerard Plys ’80, SM ’81, ScD ’84; Mark Prausnitz PhD ’94; Anil Kumar Sachdev ScD ’77; Christopher Scholz PhD ’67; Melody Ann Swartz PhD ’98; and Elias Towe ’80, SM ’81, PhD ’87.

      “I am delighted that seven members of MIT’s faculty and many members of the wider MIT community were elected to the National Academy of Engineering this year,” says Anantha Chandrakasan, the dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “My warmest congratulations on this recognition of their many contributions to engineering research and education.”

      Including this year’s inductees, 156 members of the National Academy of Engineering are current or retired members of the MIT faculty and staff, or members of the MIT Corporation. More

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      in Data Management & Statistics

      Efficient technique improves machine-learning models’ reliability

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      Powerful machine-learning models are being used to help people tackle tough problems such as identifying disease in medical images or detecting road obstacles for autonomous vehicles. But machine-learning models can make mistakes, so in high-stakes settings it’s critical that humans know when to trust a model’s predictions.

      Uncertainty quantification is one tool that improves a model’s reliability; the model produces a score along with the prediction that expresses a confidence level that the prediction is correct. While uncertainty quantification can be useful, existing methods typically require retraining the entire model to give it that ability. Training involves showing a model millions of examples so it can learn a task. Retraining then requires millions of new data inputs, which can be expensive and difficult to obtain, and also uses huge amounts of computing resources.

      Researchers at MIT and the MIT-IBM Watson AI Lab have now developed a technique that enables a model to perform more effective uncertainty quantification, while using far fewer computing resources than other methods, and no additional data. Their technique, which does not require a user to retrain or modify a model, is flexible enough for many applications.

      The technique involves creating a simpler companion model that assists the original machine-learning model in estimating uncertainty. This smaller model is designed to identify different types of uncertainty, which can help researchers drill down on the root cause of inaccurate predictions.

      “Uncertainty quantification is essential for both developers and users of machine-learning models. Developers can utilize uncertainty measurements to help develop more robust models, while for users, it can add another layer of trust and reliability when deploying models in the real world. Our work leads to a more flexible and practical solution for uncertainty quantification,” says Maohao Shen, an electrical engineering and computer science graduate student and lead author of a paper on this technique.

      Shen wrote the paper with Yuheng Bu, a former postdoc in the Research Laboratory of Electronics (RLE) who is now an assistant professor at the University of Florida; Prasanna Sattigeri, Soumya Ghosh, and Subhro Das, research staff members at the MIT-IBM Watson AI Lab; and senior author Gregory Wornell, the Sumitomo Professor in Engineering who leads the Signals, Information, and Algorithms Laboratory RLE and is a member of the MIT-IBM Watson AI Lab. The research will be presented at the AAAI Conference on Artificial Intelligence.

      Quantifying uncertainty

      In uncertainty quantification, a machine-learning model generates a numerical score with each output to reflect its confidence in that prediction’s accuracy. Incorporating uncertainty quantification by building a new model from scratch or retraining an existing model typically requires a large amount of data and expensive computation, which is often impractical. What’s more, existing methods sometimes have the unintended consequence of degrading the quality of the model’s predictions.

      The MIT and MIT-IBM Watson AI Lab researchers have thus zeroed in on the following problem: Given a pretrained model, how can they enable it to perform effective uncertainty quantification?

      They solve this by creating a smaller and simpler model, known as a metamodel, that attaches to the larger, pretrained model and uses the features that larger model has already learned to help it make uncertainty quantification assessments.

      “The metamodel can be applied to any pretrained model. It is better to have access to the internals of the model, because we can get much more information about the base model, but it will also work if you just have a final output. It can still predict a confidence score,” Sattigeri says.

      They design the metamodel to produce the uncertainty quantification output using a technique that includes both types of uncertainty: data uncertainty and model uncertainty. Data uncertainty is caused by corrupted data or inaccurate labels and can only be reduced by fixing the dataset or gathering new data. In model uncertainty, the model is not sure how to explain the newly observed data and might make incorrect predictions, most likely because it hasn’t seen enough similar training examples. This issue is an especially challenging but common problem when models are deployed. In real-world settings, they often encounter data that are different from the training dataset.

      “Has the reliability of your decisions changed when you use the model in a new setting? You want some way to have confidence in whether it is working in this new regime or whether you need to collect training data for this particular new setting,” Wornell says.

      Validating the quantification

      Once a model produces an uncertainty quantification score, the user still needs some assurance that the score itself is accurate. Researchers often validate accuracy by creating a smaller dataset, held out from the original training data, and then testing the model on the held-out data. However, this technique does not work well in measuring uncertainty quantification because the model can achieve good prediction accuracy while still being over-confident, Shen says.

      They created a new validation technique by adding noise to the data in the validation set — this noisy data is more like out-of-distribution data that can cause model uncertainty. The researchers use this noisy dataset to evaluate uncertainty quantifications.

      They tested their approach by seeing how well a meta-model could capture different types of uncertainty for various downstream tasks, including out-of-distribution detection and misclassification detection. Their method not only outperformed all the baselines in each downstream task but also required less training time to achieve those results.

      This technique could help researchers enable more machine-learning models to effectively perform uncertainty quantification, ultimately aiding users in making better decisions about when to trust predictions.

      Moving forward, the researchers want to adapt their technique for newer classes of models, such as large language models that have a different structure than a traditional neural network, Shen says.

      The work was funded, in part, by the MIT-IBM Watson AI Lab and the U.S. National Science Foundation. More

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      in Data Management & Statistics

      When should data scientists try a new technique?

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      If a scientist wanted to forecast ocean currents to understand how pollution travels after an oil spill, she could use a common approach that looks at currents traveling between 10 and 200 kilometers. Or, she could choose a newer model that also includes shorter currents. This might be more accurate, but it could also require learning new software or running new computational experiments. How to know if it will be worth the time, cost, and effort to use the new method?

      A new approach developed by MIT researchers could help data scientists answer this question, whether they are looking at statistics on ocean currents, violent crime, children’s reading ability, or any number of other types of datasets.

      The team created a new measure, known as the “c-value,” that helps users choose between techniques based on the chance that a new method is more accurate for a specific dataset. This measure answers the question “is it likely that the new method is more accurate for this data than the common approach?”

      Traditionally, statisticians compare methods by averaging a method’s accuracy across all possible datasets. But just because a new method is better for all datasets on average doesn’t mean it will actually provide a better estimate using one particular dataset. Averages are not application-specific.

      So, researchers from MIT and elsewhere created the c-value, which is a dataset-specific tool. A high c-value means it is unlikely a new method will be less accurate than the original method on a specific data problem.

      In their proof-of-concept paper, the researchers describe and evaluate the c-value using real-world data analysis problems: modeling ocean currents, estimating violent crime in neighborhoods, and approximating student reading ability at schools. They show how the c-value could help statisticians and data analysts achieve more accurate results by indicating when to use alternative estimation methods they otherwise might have ignored.

      “What we are trying to do with this particular work is come up with something that is data specific. The classical notion of risk is really natural for someone developing a new method. That person wants their method to work well for all of their users on average. But a user of a method wants something that will work on their individual problem. We’ve shown that the c-value is a very practical proof-of-concept in that direction,” says senior author Tamara Broderick, an associate professor in the Department of Electrical Engineering and Computer Science (EECS) and a member of the Laboratory for Information and Decision Systems and the Institute for Data, Systems, and Society.

      She’s joined on the paper by Brian Trippe PhD ’22, a former graduate student in Broderick’s group who is now a postdoc at Columbia University; and Sameer Deshpande ’13, a former postdoc in Broderick’s group who is now an assistant professor at the University of Wisconsin at Madison. An accepted version of the paper is posted online in the Journal of the American Statistical Association.

      Evaluating estimators

      The c-value is designed to help with data problems in which researchers seek to estimate an unknown parameter using a dataset, such as estimating average student reading ability from a dataset of assessment results and student survey responses. A researcher has two estimation methods and must decide which to use for this particular problem.

      The better estimation method is the one that results in less “loss,” which means the estimate will be closer to the ground truth. Consider again the forecasting of ocean currents: Perhaps being off by a few meters per hour isn’t so bad, but being off by many kilometers per hour makes the estimate useless. The ground truth is unknown, though; the scientist is trying to estimate it. Therefore, one can never actually compute the loss of an estimate for their specific data. That’s what makes comparing estimates challenging. The c-value helps a scientist navigate this challenge.

      The c-value equation uses a specific dataset to compute the estimate with each method, and then once more to compute the c-value between the methods. If the c-value is large, it is unlikely that the alternative method is going to be worse and yield less accurate estimates than the original method.

      “In our case, we are assuming that you conservatively want to stay with the default estimator, and you only want to go to the new estimator if you feel very confident about it. With a high c-value, it’s likely that the new estimate is more accurate. If you get a low c-value, you can’t say anything conclusive. You might have actually done better, but you just don’t know,” Broderick explains.

      Probing the theory

      The researchers put that theory to the test by evaluating three real-world data analysis problems.

      For one, they used the c-value to help determine which approach is best for modeling ocean currents, a problem Trippe has been tackling. Accurate models are important for predicting the dispersion of contaminants, like pollution from an oil spill. The team found that estimating ocean currents using multiple scales, one larger and one smaller, likely yields higher accuracy than using only larger scale measurements.

      “Oceans researchers are studying this, and the c-value can provide some statistical ‘oomph’ to support modeling the smaller scale,” Broderick says.

      In another example, the researchers sought to predict violent crime in census tracts in Philadelphia, an application Deshpande has been studying. Using the c-value, they found that one could get better estimates about violent crime rates by incorporating information about census-tract-level nonviolent crime into the analysis. They also used the c-value to show that additionally leveraging violent crime data from neighboring census tracts in the analysis isn’t likely to provide further accuracy improvements.

      “That doesn’t mean there isn’t an improvement, that just means that we don’t feel confident saying that you will get it,” she says.

      Now that they have proven the c-value in theory and shown how it could be used to tackle real-world data problems, the researchers want to expand the measure to more types of data and a wider set of model classes.

      The ultimate goal is to create a measure that is general enough for many more data analysis problems, and while there is still a lot of work to do to realize that objective, Broderick says this is an important and exciting first step in the right direction.

      This research was supported, in part, by an Advanced Research Projects Agency-Energy grant, a National Science Foundation CAREER Award, the Office of Naval Research, and the Wisconsin Alumni Research Foundation. More