Researchers 3D print components for a portable mass spectrometer

Mass spectrometers, devices that identify chemical substances, are widely used in applications like crime scene analysis, toxicology testing, and geological surveying. But these machines are bulky, expensive, and easy to damage, which limits where they can be effectively deployed.

Using additive manufacturing, MIT researchers produced a mass filter, which is the core component of a mass spectrometer, that is far lighter and cheaper than the same type of filter made with traditional techniques and materials.

Their miniaturized filter, known as a quadrupole, can be completely fabricated in a matter of hours for a few dollars. The 3D-printed device is as precise as some commercial-grade mass filters that can cost more than $100,000 and take weeks to manufacture.

Built from durable and heat-resistant glass-ceramic resin, the filter is 3D printed in one step, so no assembly is required. Assembly often introduces defects that can hamper the performance of quadrupoles.

This lightweight, cheap, yet precise quadrupole is one important step in Luis Fernando Velásquez-García’s 20-year quest to produce a 3D-printed, portable mass spectrometer.

“We are not the first ones to try to do this. But we are the first ones who succeeded at doing this. There are other miniaturized quadrupole filters, but they are not comparable with professional-grade mass filters. There are a lot of possibilities for this hardware if the size and cost could be smaller without adversely affecting the performance,” says Velásquez-García, a principal research scientist in MIT’s Microsystems Technology Laboratories (MTL) and senior author of a paper detailing the miniaturized quadrupole.

For instance, a scientist could bring a portable mass spectrometer to remote areas of the rainforest, using it to rapidly analyze potential pollutants without shipping samples back to a lab. And a lightweight device would be cheaper and easier to send into space, where it could monitor chemicals in Earth’s atmosphere or on those of distant planets.

Velásquez-García is joined on the paper by lead author Colin Eckhoff, an MIT graduate student in electrical engineering and computer science (EECS); Nicholas Lubinsky, a former MIT postdoc; and Luke Metzler and Randall Pedder of Ardara Technologies. The research is published in Advanced Science.

Size matters

At the heart of a mass spectrometer is the mass filter. This component uses electric or magnetic fields to sort charged particles based on their mass-to-charge ratio. In this way, the device can measure the chemical components in a sample to identify an unknown substance.

A quadrupole, a common type of mass filter, is composed of four metallic rods surrounding an axis. Voltages are applied to the rods, which produce an electromagnetic field. Depending on the properties of the electromagnetic field, ions with a specific mass-to-charge ratio will swirl around through the middle of the filter, while other particles escape out the sides. By varying the mix of voltages, one can target ions with different mass-to-charge ratios.

While fairly simple in design, a typical stainless-steel quadrupole might weigh several kilograms. But miniaturizing a quadrupole is no easy task. Making the filter smaller usually introduces errors during the manufacturing process. Plus, smaller filters collect fewer ions, which makes chemical analysis less sensitive.

“You can’t make quadrupoles arbitrarily smaller — there is a tradeoff,” Velásquez-García adds.

His team balanced this tradeoff by leveraging additive manufacturing to make miniaturized quadrupoles with the ideal size and shape to maximize precision and sensitivity.

They fabricate the filter from a glass-ceramic resin, which is a relatively new printable material that can withstand temperatures up to 900 degrees Celsius and performs well in a vacuum.

The device is produced using vat photopolymerization, a process where a piston pushes into a vat of liquid resin until it nearly touches an array of LEDs at the bottom. These illuminate, curing the resin that remains in the minuscule gap between the piston and the LEDs. A tiny layer of cured polymer is then stuck to the piston, which rises up and repeats the cycle, building the device one tiny layer at a time.

“This is a relatively new technology for printing ceramics that allows you to make very precise 3D objects. And one key advantage of additive manufacturing is that you can aggressively iterate the designs,” Velásquez-García says.

Since the 3D printer can form practically any shape, the researchers designed a quadrupole with hyperbolic rods. This shape is ideal for mass filtering but difficult to make with conventional methods. Many commercial filters employ rounded rods instead, which can reduce performance.

They also printed an intricate network of triangular lattices surrounding the rods, which provides durability while ensuring the rods remain positioned correctly if the device is moved or shaken.

To finish the quadrupole, the researchers used a technique called electroless plating to coat the rods with a thin metal film, which makes them electrically conductive. They cover everything but the rods with a masking chemical and then submerge the quadrupole in a chemical bath heated to a precise temperature and stirring conditions. This deposits a thin metal film on the rods uniformly without damaging the rest of the device or shorting the rods.

“In the end, we made quadrupoles that were the most compact but also the most precise that could be made, given the constraints of our 3D printer,” Velásquez-García says.

Maximizing performance

To test their 3D-printed quadrupoles, the team swapped them into a commercial system and found that they could attain higher resolutions than other types of miniature filters. Their quadrupoles, which are about 12 centimeters in length, are one-quarter the density of comparable stainless-steel filters.

In addition, further experiments suggest that their 3D-printed quadrupoles could achieve precision that is on par with that of largescale commercial filters.

“Mass spectrometry is one of the most important of all scientific tools, and Velásquez-Garcia and co-workers describe the design, construction, and performance of a quadrupole mass filter that has several advantages over earlier devices,” says Graham Cooks, the Henry Bohn Hass Distinguished Professor of Chemistry in the Aston Laboratories for Mass Spectrometry at Purdue University, who was not involved with this work. “The advantages derive from these facts: It is much smaller and lighter than most commercial counterparts and it is fabricated monolithically, using additive construction. … It is an open question as to how well the performance will compare with that of quadrupole ion traps, which depend on the same electric fields for mass measurement but which do not have the stringent geometrical requirements of quadrupole mass filters.”

“This paper represents a real advance in the manufacture of quadrupole mass filters (QMF). The authors bring together their knowledge of manufacture using advanced materials, QMF drive electronics, and mass spectrometry to produce a novel system with good performance at low cost,” adds Steve Taylor, professor of electrical engineering and electronics at the University of Liverpool, who was also not involved with this paper. “Since QMFs are at the heart of the ‘analytical engine’ in many other types of mass spectrometry systems, the paper has an important significance across the whole mass spectrometry field, which worldwide represents a multibillion-dollar industry.”

In the future, the researchers plan to boost the quadrupole’s performance by making the filters longer. A longer filter can enable more precise measurements since more ions that are supposed to be filtered out will escape as the chemical travels along its length. They also intend to explore different ceramic materials that could better transfer heat.

“Our vision is to make a mass spectrometer where all the key components can be 3D printed, contributing to a device with much less weight and cost without sacrificing performance. There is still a lot of work to do, but this is a great start,” Velásquez-Garcia adds.

This work was funded by Empiriko Corporation.

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

The National Academy of Inventors (NAI) recently announced the election of more than 160 individuals to their 2023 class of fellows. Among them are two members of the MIT Koch Institute for Integrative Cancer Research, Professor Daniel G. Anderson and Principal Research Scientist Ana Jaklenec. In addition, 11 MIT alumni were also recognized.

The highest professional distinction accorded solely to academic inventors, election to the NAI recognizes individuals who have created or facilitated outstanding inventions that have made a tangible impact on quality of life, economic development, and the welfare of society.  

“Daniel and Ana embody some of the Koch Institute’s core values of interdisciplinary innovation and drive to translate their discoveries into real impact for patients,” says Matthew Vander Heiden, director of the Koch Institute. “Their election to the academy is very well-deserved, and we are honored to count them both among the Koch Institute’s and MIT’s research community.”

Daniel Anderson is the Joseph R. Mares (1924) Professor of Chemical Engineering, and a core member of the Institute for Medical Engineering and Science. He is a leading researcher in the fields of nanotherapeutics and biomaterials. Anderson’s work has led to advances in a range of areas, including medical devices, cell therapy, drug delivery, gene therapy, and material science, and has resulted in the publication of more than 500 papers, patents, and patent applications. He has founded several companies, including Living Proof, Olivo Labs, Crispr Therapeutics (CRSP), Sigilon Therapeutics, Verseau Therapeutics, oRNA, and VasoRx. He is a member of National Academy of Medicine, the Harvard-MIT Division of Health Science and Technology, and is an affiliate of the Broad Institute of MIT and Harvard and the Ragon Institute of MGH, MIT and Harvard.

Ana Jaklenec, a principal research scientist and principal investigator at the Koch Institute, is a leader in the fields of bioengineering and materials science, focused on controlled delivery and stability of therapeutics for global health. She is an inventor of several drug delivery technologies that have the potential to enable equitable access to medical care globally. Her lab is developing new manufacturing techniques for the design of materials at the nano- and micro-scale for self-boosting vaccines, 3D printed on-demand microneedles, heat-stable polymer-based carriers for oral delivery of micronutrients and probiotics, and long-term drug delivery systems for cancer immunotherapy. She has published over 100 manuscripts, patents, and patent applications and has founded three companies: Particles for Humanity, VitaKey, and OmniPulse Biosciences.

The 11 MIT alumni who were elected to the NAI for 2023 include:

• Michel Barsoum PhD ’85 (Materials Science and Engineering);
• Eric Burger ’84 (Electrical Engineering and Computer Science);
• Kevin Kelly SM ’88, PhD ’91 (Mechanical Engineering);
• Ali Khademhosseini PhD ’05 (Biological Engineering);
• Joshua Makower ’85 (Mechanical Engineering);
• Marcela Maus ’97 (Biology);
• Milos Popovic SM ’02, PhD ’08 (Electrical Engineering and Computer Science);
• Milica Radisic PhD ’04 (Chemical Engineering);
• David Reinkensmeyer ’88 (Electrical Engineering);
• Boris Rubinsky PhD ’81 (Mechanical Engineering); and
• Paul S. Weiss ’80, SM ’80 (Chemistry).

Since its inception in 2012, the NAI Fellows program has grown to include 1,898 exceptional researchers and innovators, who hold over 63,000 U.S. patents and 13,000 licensed technologies. NAI Fellows are known for the societal and economic impact of their inventions, contributing to major advancements in science and consumer technologies. Their innovations have generated over $3 trillion in revenue and generated 1 million jobs.    

“This year’s class of NAI Fellows showcases the caliber of researchers that are found within the innovation ecosystem. Each of these individuals are making significant contributions to both science and society through their work,” says Paul R. Sanberg, president of the NAI. “This new class, in conjunction with our existing fellows, are creating innovations that are driving crucial advancements across a variety of disciplines and are stimulating the global and national economy in immeasurable ways as they move these technologies from lab to marketplace.” 

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AI agents help explain other AI systems

Explaining the behavior of trained neural networks remains a compelling puzzle, especially as these models grow in size and sophistication. Like other scientific challenges throughout history, reverse-engineering how artificial intelligence systems work requires a substantial amount of experimentation: making hypotheses, intervening on behavior, and even dissecting large networks to examine individual neurons. To date, most successful experiments have involved large amounts of human oversight. Explaining every computation inside models the size of GPT-4 and larger will almost certainly require more automation — perhaps even using AI models themselves. 

Facilitating this timely endeavor, researchers from MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) have developed a novel approach that uses AI models to conduct experiments on other systems and explain their behavior. Their method uses agents built from pretrained language models to produce intuitive explanations of computations inside trained networks.

Central to this strategy is the “automated interpretability agent” (AIA), designed to mimic a scientist’s experimental processes. Interpretability agents plan and perform tests on other computational systems, which can range in scale from individual neurons to entire models, in order to produce explanations of these systems in a variety of forms: language descriptions of what a system does and where it fails, and code that reproduces the system’s behavior. Unlike existing interpretability procedures that passively classify or summarize examples, the AIA actively participates in hypothesis formation, experimental testing, and iterative learning, thereby refining its understanding of other systems in real time. 

Complementing the AIA method is the new “function interpretation and description” (FIND) benchmark, a test bed of functions resembling computations inside trained networks, and accompanying descriptions of their behavior. One key challenge in evaluating the quality of descriptions of real-world network components is that descriptions are only as good as their explanatory power: Researchers don’t have access to ground-truth labels of units or descriptions of learned computations. FIND addresses this long-standing issue in the field by providing a reliable standard for evaluating interpretability procedures: explanations of functions (e.g., produced by an AIA) can be evaluated against function descriptions in the benchmark.  

For example, FIND contains synthetic neurons designed to mimic the behavior of real neurons inside language models, some of which are selective for individual concepts such as “ground transportation.” AIAs are given black-box access to synthetic neurons and design inputs (such as “tree,” “happiness,” and “car”) to test a neuron’s response. After noticing that a synthetic neuron produces higher response values for “car” than other inputs, an AIA might design more fine-grained tests to distinguish the neuron’s selectivity for cars from other forms of transportation, such as planes and boats. When the AIA produces a description such as “this neuron is selective for road transportation, and not air or sea travel,” this description is evaluated against the ground-truth description of the synthetic neuron (“selective for ground transportation”) in FIND. The benchmark can then be used to compare the capabilities of AIAs to other methods in the literature. 

Sarah Schwettmann PhD '21, co-lead author of a paper on the new work and a research scientist at CSAIL, emphasizes the advantages of this approach. “The AIAs’ capacity for autonomous hypothesis generation and testing may be able to surface behaviors that would otherwise be difficult for scientists to detect. It’s remarkable that language models, when equipped with tools for probing other systems, are capable of this type of experimental design,” says Schwettmann. “Clean, simple benchmarks with ground-truth answers have been a major driver of more general capabilities in language models, and we hope that FIND can play a similar role in interpretability research.”

Automating interpretability 

Large language models are still holding their status as the in-demand celebrities of the tech world. The recent advancements in LLMs have highlighted their ability to perform complex reasoning tasks across diverse domains. The team at CSAIL recognized that given these capabilities, language models may be able to serve as backbones of generalized agents for automated interpretability. “Interpretability has historically been a very multifaceted field,” says Schwettmann. “There is no one-size-fits-all approach; most procedures are very specific to individual questions we might have about a system, and to individual modalities like vision or language. Existing approaches to labeling individual neurons inside vision models have required training specialized models on human data, where these models perform only this single task. Interpretability agents built from language models could provide a general interface for explaining other systems — synthesizing results across experiments, integrating over different modalities, even discovering new experimental techniques at a very fundamental level.” 

As we enter a regime where the models doing the explaining are black boxes themselves, external evaluations of interpretability methods are becoming increasingly vital. The team’s new benchmark addresses this need with a suite of functions with known structure, that are modeled after behaviors observed in the wild. The functions inside FIND span a diversity of domains, from mathematical reasoning to symbolic operations on strings to synthetic neurons built from word-level tasks. The dataset of interactive functions is procedurally constructed; real-world complexity is introduced to simple functions by adding noise, composing functions, and simulating biases. This allows for comparison of interpretability methods in a setting that translates to real-world performance.      

In addition to the dataset of functions, the researchers introduced an innovative evaluation protocol to assess the effectiveness of AIAs and existing automated interpretability methods. This protocol involves two approaches. For tasks that require replicating the function in code, the evaluation directly compares the AI-generated estimations and the original, ground-truth functions. The evaluation becomes more intricate for tasks involving natural language descriptions of functions. In these cases, accurately gauging the quality of these descriptions requires an automated understanding of their semantic content. To tackle this challenge, the researchers developed a specialized “third-party” language model. This model is specifically trained to evaluate the accuracy and coherence of the natural language descriptions provided by the AI systems, and compares it to the ground-truth function behavior. 

FIND enables evaluation revealing that we are still far from fully automating interpretability; although AIAs outperform existing interpretability approaches, they still fail to accurately describe almost half of the functions in the benchmark. Tamar Rott Shaham, co-lead author of the study and a postdoc in CSAIL, notes that “while this generation of AIAs is effective in describing high-level functionality, they still often overlook finer-grained details, particularly in function subdomains with noise or irregular behavior. This likely stems from insufficient sampling in these areas. One issue is that the AIAs’ effectiveness may be hampered by their initial exploratory data. To counter this, we tried guiding the AIAs’ exploration by initializing their search with specific, relevant inputs, which significantly enhanced interpretation accuracy.” This approach combines new AIA methods with previous techniques using pre-computed examples for initiating the interpretation process.

The researchers are also developing a toolkit to augment the AIAs’ ability to conduct more precise experiments on neural networks, both in black-box and white-box settings. This toolkit aims to equip AIAs with better tools for selecting inputs and refining hypothesis-testing capabilities for more nuanced and accurate neural network analysis. The team is also tackling practical challenges in AI interpretability, focusing on determining the right questions to ask when analyzing models in real-world scenarios. Their goal is to develop automated interpretability procedures that could eventually help people audit systems — e.g., for autonomous driving or face recognition — to diagnose potential failure modes, hidden biases, or surprising behaviors before deployment. 

Watching the watchers

The team envisions one day developing nearly autonomous AIAs that can audit other systems, with human scientists providing oversight and guidance. Advanced AIAs could develop new kinds of experiments and questions, potentially beyond human scientists’ initial considerations. The focus is on expanding AI interpretability to include more complex behaviors, such as entire neural circuits or subnetworks, and predicting inputs that might lead to undesired behaviors. This development represents a significant step forward in AI research, aiming to make AI systems more understandable and reliable.

“A good benchmark is a power tool for tackling difficult challenges,” says Martin Wattenberg, computer science professor at Harvard University who was not involved in the study. “It's wonderful to see this sophisticated benchmark for interpretability, one of the most important challenges in machine learning today. I'm particularly impressed with the automated interpretability agent the authors created. It's a kind of interpretability jiu-jitsu, turning AI back on itself in order to help human understanding.”

Schwettmann, Rott Shaham, and their colleagues presented their work at NeurIPS 2023 in December.  Additional MIT coauthors, all affiliates of the CSAIL and the Department of Electrical Engineering and Computer Science (EECS), include graduate student Joanna Materzynska, undergraduate student Neil Chowdhury, Shuang Li PhD ’23, Assistant Professor Jacob Andreas, and Professor Antonio Torralba. Northeastern University Assistant Professor David Bau is an additional coauthor.

The work was supported, in part, by the MIT-IBM Watson AI Lab, Open Philanthropy, an Amazon Research Award, Hyundai NGV, the U.S. Army Research Laboratory, the U.S. National Science Foundation, the Zuckerman STEM Leadership Program, and a Viterbi Fellowship.

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Complex, unfamiliar sentences make the brain’s language network work harder

With help from an artificial language network, MIT neuroscientists have discovered what kind of sentences are most likely to fire up the brain’s key language processing centers.

The new study reveals that sentences that are more complex, either because of unusual grammar or unexpected meaning, generate stronger responses in these language processing centers. Sentences that are very straightforward barely engage these regions, and nonsensical sequences of words don’t do much for them either.

For example, the researchers found this brain network was most active when reading unusual sentences such as “Buy sell signals remains a particular,” taken from a publicly available language dataset called C4. However, it went quiet when reading something very straightforward, such as “We were sitting on the couch.”

“The input has to be language-like enough to engage the system,” says Evelina Fedorenko, Associate Professor of Neuroscience at MIT and a member of MIT’s McGovern Institute for Brain Research. “And then within that space, if things are really easy to process, then you don’t have much of a response. But if things get difficult, or surprising, if there’s an unusual construction or an unusual set of words that you’re maybe not very familiar with, then the network has to work harder.”

Fedorenko is the senior author of the study, which appears today in Nature Human Behavior. MIT graduate student Greta Tuckute is the lead author of the paper.

Processing language

In this study, the researchers focused on language-processing regions found in the left hemisphere of the brain, which includes Broca’s area as well as other parts of the left frontal and temporal lobes of the brain.

“This language network is highly selective to language, but it’s been harder to actually figure out what is going on in these language regions,” Tuckute says. “We wanted to discover what kinds of sentences, what kinds of linguistic input, drive the left hemisphere language network.”

The researchers began by compiling a set of 1,000 sentences taken from a wide variety of sources — fiction, transcriptions of spoken words, web text, and scientific articles, among many others.

Five human participants read each of the sentences while the researchers measured their language network activity using functional magnetic resonance imaging (fMRI). The researchers then fed those same 1,000 sentences into a large language model — a model similar to ChatGPT, which learns to generate and understand language from predicting the next word in huge amounts of text — and measured the activation patterns of the model in response to each sentence.

Once they had all of those data, the researchers trained a mapping model, known as an “encoding model,” which relates the activation patterns seen in the human brain with those observed in the artificial language model. Once trained, the model could predict how the human language network would respond to any new sentence based on how the artificial language network responded to these 1,000 sentences.

The researchers then used the encoding model to identify 500 new sentences that would generate maximal activity in the human brain (the “drive” sentences), as well as sentences that would elicit minimal activity in the brain’s language network (the “suppress” sentences).

In a group of three new human participants, the researchers found these new sentences did indeed drive and suppress brain activity as predicted.

“This ‘closed-loop’ modulation of brain activity during language processing is novel,” Tuckute says. “Our study shows that the model we’re using (that maps between language-model activations and brain responses) is accurate enough to do this. This is the first demonstration of this approach in brain areas implicated in higher-level cognition, such as the language network.”

Linguistic complexity

To figure out what made certain sentences drive activity more than others, the researchers analyzed the sentences based on 11 different linguistic properties, including grammaticality, plausibility, emotional valence (positive or negative), and how easy it is to visualize the sentence content.

For each of those properties, the researchers asked participants from crowd-sourcing platforms to rate the sentences. They also used a computational technique to quantify each sentence’s “surprisal,” or how uncommon it is compared to other sentences.

This analysis revealed that sentences with higher surprisal generate higher responses in the brain. This is consistent with previous studies showing people have more difficulty processing sentences with higher surprisal, the researchers say.

Another linguistic property that correlated with the language network’s responses was linguistic complexity, which is measured by how much a sentence adheres to the rules of English grammar and how plausible it is, meaning how much sense the content makes, apart from the grammar.

Sentences at either end of the spectrum — either extremely simple, or so complex that they make no sense at all — evoked very little activation in the language network. The largest responses came from sentences that make some sense but require work to figure them out, such as “Jiffy Lube of — of therapies, yes,” which came from the Corpus of Contemporary American English dataset.

“We found that the sentences that elicit the highest brain response have a weird grammatical thing and/or a weird meaning,” Fedorenko says. “There’s something slightly unusual about these sentences.”

The researchers now plan to see if they can extend these findings in speakers of languages other than English. They also hope to explore what type of stimuli may activate language processing regions in the brain’s right hemisphere.

The research was funded by an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, the MIT-IBM Watson AI Lab, the National Institutes of Health, the McGovern Institute, the Simons Center for the Social Brain, and MIT’s Department of Brain and Cognitive Sciences.

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Building technology that empowers city residents

Kwesi Afrifa came to MIT from his hometown of Accra, Ghana, in 2020 to pursue an interdisciplinary major in urban planning and computer science. Growing up amid the many moving parts of a large, densely populated city, he had often observed aspects of urban life that could be made more efficient. He decided to apply his interest in computing and coding to address these problems by creating software tools for city planners.

Now a senior, Afrifa works at the City Form Lab led by Andres Sevstuk, collaborating on an open-source, Python-based tool that allows researchers and policymakers to analyze pedestrians’ behaviors. The package, which launches next month, will make it more feasible for researchers and city planners to investigate how changes to a city’s structural characteristics impact walkability and the pedestrian experience.

During his first two years at MIT, Afrifa worked in the Civic Data Design Lab led by Associate Professor Sarah Williams, where he helped build sensing tools and created an online portal for people living in Kibera, Nairobi, to access the internet and participate in survey research.

After graduation, he will go on to work as a software engineer at a startup in New York. After several years, he hopes to start his own company, building urban data tools for integration into mapping and location-based software applications.

“I see it as my duty to make city systems more efficient, deepen the connection between residents and their communities, and make existing in them better for everyone, including groups which have often been marginalized,” he says.

“Cities are special places”

Afrifa believes that in urban settings, technology has a unique power to both accelerate development and empower citizens.

He witnessed such unifying power in high school, when he created the website ghanabills.com, which aggregated bills of parliament in Ghana, providing easy access to this information as well as a place for people to engage in discussion on the bills. He describes the effect of this technology as a “democratizing force.”

Afrifa also explored the connection between cities and community as an executive member of Code for Good, a program that connects MIT students interested in software with nonprofits throughout the Boston area. He served as a mentor for students and worked on finding nonprofits to match them up with.

Language and visibility

Sharing African languages and cultures is also important to Afrifa. In his first two years at MIT, he and other African students across the country started the Mandla app, which he describes as a Duolingo for African languages. It had gamified lessons, voice translations, and other interactive features for learning. “We wanted to solve the problem of language revitalization and bring African languages to the broader diaspora,” he says. At its peak a year ago, the app had 50,000 daily active users.

Although the Mandla App was discontinued due to lack of funding, Afrifa has found other ways to promote African culture at MIT. He is currently collaborating with architecture graduate students TJ Bayowa and Courage Kpodo on a “A Tale of Two Coasts,” an upcoming short film and multimedia installation that delves into the intricate connections between perceptions of African art and identity spanning two coasts of the Atlantic Ocean. This ongoing collaboration, which Afrifa says is still taking shape, is something he hopes to expand beyond MIT.

Discovering arts

As a child, Afrifa enjoyed writing poetry. Growing up with parents who loved literature, Afrifa was encouraged to become involved with the theater and art scene of Accra. He didn’t expect to continue this interest at MIT, but then he discovered the Black Theater Guild (BTG).

The theater group had been active at MIT from the 1990s to around 2005. It was revived by Afrifa in his sophomore year when Professor Jay Scheib, head of Music and Theater Arts at MIT, encouraged him to write, direct, and produce more of his work after his final project for 21M.710 (Script Analysis), a dramaturgy class taught by Scheib.

Since then, the BTG has held two productions in the past two years: “Nkrumah’s Last Day,” in spring 2022, and “Shooting the Sheriff,” in spring 2023, both of which were written and directed by Afrifa. “It’s been very rewarding to conceptualize ideas, write stories and have this amazing community of people come together and produce it,” he says.

When asked if he will continue to pursue theater post-grad, Afrifa says: “That’s 100 percent the goal.”

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Culturally informed design: Unearthing ingenuity where it always was

Pedro Reynolds-Cuéllar, an MIT PhD student in both media arts and sciences and art, culture, and technology (ACT), explores how technology and culture intersect in spaces often overlooked by mainstream society, stretching beyond the usual scope of design research.

A former lecturer and researcher at MIT D-Lab with experience in robotics, Reynolds-Cuéllar is an ACT Future Heritage Lab affiliate, a member of the Space Enabled Group within the MIT Media Lab, and a MAD Fellow who hails from rural Colombia, where resourcefulness isn't a skill but a way of life. “I grew up seeing impressive ingenuity in solving a lot of problems, building contraptions, tools, and infrastructure … all sorts of things. Investigating this ingenuity has been the question driving my entire PhD,” he reflects.

Emphasizing the importance of cultural elements in how people collaborate, his work encourages a more localized, culturally informed perspective on technology design. “I am interested in investigating how technology takes place in geographies and spaces that are outside of mainstream society, mostly rural places,” he says.

At the heart of South America, Colombia is home to over 80 distinct groups of Indigenous tribes known to exist, each carrying unique customs, beliefs, and practices. This contributes to Colombia's cultural mosaic and linguistic diversity, with more than 68 spoken languages. This meant plenty of opportunities for Reynolds-Cuéllar to engage with communities without trying to reshape or “fix” them, but rather to amplify their intrinsic strengths and amplify their voices.

“My colleagues and I developed a digital platform meticulously documenting collaborative processes when designing technology. This platform, called Retos, captures the invaluable social capital that blooms from these interactions,” Reynolds-Cuéllar explains. Born from a need to foster cross-pollination, the platform serves as a bridge between universities, companies, and rural Colombian organizations, enhancing their existing initiatives and facilitating processes such as funding applications. It received an award from MIT Solve and the 2022 MIT Prize for Open Data from MIT Libraries. 

Designing with culture in mind

Reynolds-Cuéllar's approach isn’t formulaic. “Culture is pivotal in shaping collaboration dynamics,” he emphasizes. “Reading about collaboration can make it seem like something universal, but I don’t think it works that way. This means common research methods are not always effective. You must ‘tune in,’ and build upon existing methods in the local fabric.” This understanding fuels Reynolds-Cuéllar’s work, allowing him to sculpt each project to resonate with a community's distinct cultural context. At the heart of his doctoral research, he integrates Indigenous knowledge and what he calls “ancestral technology into design practices — a form of world-making (design) that primarily supports cultural cohesion, rooted in bounded geography and with a history that lives through collective memory. “I'm prompting designers, who may lack direct access to Indigenous scholarship, to recalibrate their design approaches,” Reynolds-Cuéllar articulates.

This appeal to look into multiple perspectives and methodologies broadens the horizons of conventional design thinking. Beyond designing things for a specific function or solution, Reynolds-Cuéllar looks at practices that also help maintain the cultural fabric of a place. He gives the example of weaving looms, which are not only the result of ingenious design, but also allow Indigenous communities to build artifacts with great cultural meaning and economic benefit: “When I work on the loom … I feel differently. I have access to a different state of mind and can easily get into a flow. I am building things where I can tell the story of my life within my culture. I'm making something that is meaningful for people around me, and I'm not doing it alone, we're doing it all together,” adds Reynolds-Cuéllar.

Among his ventures, Reynolds-Cuéllar's work with coffee farmers stands out. His projects in collaboration with these communities are all about empowering coffee farmers to refine their processes and gain agency over their livelihood and economic undertakings.

“The coffee industry in Colombia is intricate, with various layers influencing farmers’ lives, from bioengineered seeds to chemical fertilizers, and centralized roasting operations. It’s political and even philosophical,” Reynolds-Cuéllar states. Coffee farmers could sell the raw beans for a low price to the powerful Federación Nacional de Cafeteros (the National Federation of Coffee Growers of Colombia), but there are other alternatives to foster agency and self-determination. “We collaborate with coffee growing collectives, helping them to achieve consistency in roasting procedures, improve equipment designs, and set up packaging infrastructure,” which means farmers can produce higher-value specialty coffee which they can choose to sell directly to consumers. Reynolds-Cuéllar's work creates ripple effects, bolstering autonomy and local economies.

Too many questions

Throughout his research, Reynolds-Cuéllar describes a turning point in meeting an Indigenous cultural and social leader: “We were collaborating with a group of fishermen on Colombia's Atlantic coast, within an Indigenous community. Our initial curriculum mirrored conventional design methods. Yet, the leader's insight shifted my perspective profoundly. It was the first time my methods were being challenged.” The encounter prompted Reynolds-Cuéllar to scrutinize his methodology: “This leader told me: ‘You guys ask a lot of questions.’ I started explaining the benefit of questions, and methods in the usual design jargon. He replied: ‘I still think you ask too many questions. We ask the most important questions, and then we spend a lot of time reflecting on them,” remembers Reynolds-Cuéllar. This shift underscored the realization that there is no such thing as universal design, and that standardized methodologies don’t universally translate. They sometimes inadvertently strip away cultural nuances, where they could instead cultivate their dynamic expression.

For Reynolds-Cuéllar, his participation in MAD’s design fellowship has been instrumental. The fellowship not only provided essential funding but also offered a sense of community. “The fellowship facilitated meaningful conversations, especially talks like Dori Tunstall's on ‘Decolonizing Design,’” Reynolds-Cuéllar reflects. The financial support also translated into practical aid, allowing him to advance his projects, including compensating field researchers in Colombia.

Beyond academic pursuits, Reynolds-Cuéllar envisions writing a book titled “The Atlas of Ancestral Technology of Colombia.” More than mere documentation, this large atlas format would be a compendium of the myriad stories Reynolds-Cuéllar has unearthed, with illustrating images crafted in Colombia — visual representations from each culture, descriptions, and local stories about these artifacts. “I want a book that could counter some of the predominant narratives on design,” asserts Reynolds-Cuéllar. Through his work, Reynolds-Cuéllar already started to craft a blueprint for approaching design with cultural significance and intention, laying the foundation for a more inclusive and purposeful approach to technology and innovation.

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Climate action, here and now

A few years ago, David Hsu started taking a keen interest in some apartment buildings in Brooklyn and the Bronx — but not because he was looking for a place to live. Hsu, an associate professor at MIT, works on urban climate change solutions. The property owners were retrofitting their buildings to make them net-zero emitters of carbon dioxide via better insulation, ventilation, and electric heating and appliances. They also wanted to see the effect on interior air quality.

In the process, the owners started working with Hsu and an MIT team to assess the results using top-grade air quality sensors. They found that beyond its climate benefits, retrofitting lowered indoor pollutants from high levels to almost-undetectable levels. It is a win-win outcome.

“Not only are those buildings cleaner and use less energy and do not emit greenhouse gases, they also have better air quality,” Hsu says. “The hopeful thing is that as we remake our buildings for decarbonization, a lot of technologies are so superior that our lives will be better, too.”

Hsu’s projects frequently yield practical, concrete steps for climate action. In New York City, Hsu found, mandating the measurement of energy use lowered consumption 13 to 14 percent over four years. In a 2017 paper, he and his co-authors studied which climate actions would most reduce carbon emissions in 11 major U.S. cities. Cleveland and Denver can greatly reduce use of fossil fuels, for example, while better energy efficiency in new homes would make a big difference in Houston and Phoenix.

“You have to figure out what works and doesn’t work,” Hsu says. “I try to figure out how we can have cleaner and healthier cities that will be more sustainable, equitable, and more just.”

Significantly, Hsu does not just prescribe climate action elsewhere, he also works for change at MIT. He helped create a zero-emissions roadmap for MIT’s School of Architecture and Planning as well as the Department of Urban Studies and Planning, where he is an associate professor of urban and environmental planning and is part of Fast Forward: MIT’s Climate Action Plan for the Decade, serving in the Climate Education Working Group.

“People can get depressed about how you tackle this large, civilization-wide problem, and then you realize lots of other people care about this. Lots of smart people at MIT and other places are working on it, and there are lots of things we can do, individually and collectively,” Hsu says.

And as Hsu’s work shows, lots of people tackle the climate crisis by working on local issues. For his research and teaching, Hsu was granted tenure at MIT this year.

Urban planning by way of Amherst

Hsu studies cities, but is not from one. Growing up in the college town of Amherst, Massachusetts, Hsu could walk out of his home and “be in the woods in a minute.” He attended Yale University as an undergraduate, majoring in physics, and started venturing into New York City with friends. After graduation, Hsu moved there and got a job.

Or three jobs, really. Over the next 10 years, Hsu worked as an engineer, in real estate finance, and for the New York City government as a vice president at the NYC Economic Development Corporation, where he helped manage the city’s post-September 11 redevelopment of the East River waterfront. Eventually, he decided to pursue graduate studies in urban planning, building on his experience.

“Engineering, finance, and government, you put those three things together and they’re basically urban planning,” Hsu says. “It took me a decade after school to realize urban planning is a thing I could do. I say to students, ‘You’re lucky, you have this major. I never had this in college.’”

As a graduate student, Hsu received an MS from Cornell University in applied and engineering physics, then an MSc from the London School of Economics and Political Science in city design and social science, before getting his PhD in urban design and planning at the University of Washington in Seattle. He served on the faculty at the University of Pennsylvania before moving to MIT in 2015.

Hsu studies an array of topics involving local governments and climate policy. He has published multiple papers on Philadelphia’s attempts to refurbish its stormwater infrastructure, for example. His studies about retrofitted apartment buildings are forthcoming as three papers. A 2022 Hsu paper, “Straight out of Cape Cod,” looked at the origins of Community Choice Aggregation, an approach to purchasing clean energy that started in a few Massachusetts communities and now involves 11 percent of the U.S. population.

“I joke that the ideal reader of my articles is not a mayor and it’s not an academic, it’s a midcareer bureaucrat trying to implement a policy,” Hsu says.

Actually, that’s no mere joke. At MIT, City of Cambridge officials have contacted Hsu to discuss his studies of New York and Philadelphia, something he welcomes. Even if not in local government himself, Hsu says, “I know I can do research that might move some of those projects along. It’s my way of trying to contribute to the world outside of academia.”

“It’s all important”

There is still another way Hsu contributes to climate action: by influencing what MIT does. He helped craft the climate policies of the School of Architecture and Planning and the Department of Urban Studies and Planning, which aim to produce net zero emissions for the department through the use of tools like carbon offsets for travel. As part of the Institute-wide Climate Education Working Group convened under the Fast Forward plan, Hsu is busy thinking about how to integrate climate studies into MIT education.

“Our Fast Forward team does great work together. David McGee, Lisa Ghaffari, Kate Trimble, Antje Danielson, Curt Newton, they’re so engaged,” says Hsu. “Our students are terrifically hard-working and skilled and care about climate change, but don’t know how to affect it necessarily. We want to give them on-ramps and skills.”

He is also chair of the fast-growing 11-6 major that combines urban studies and planning with computer science.

“Climate change is happening so fast, and is so big, that every job could be climate-change related,” Hsu says. “If people leave MIT with a higher base understanding of climate change, then you can be a lawyer or consultant or work in finance or computer science and address the unsolved problems.”

Indeed, Hsu thinks many students, who he believes increasingly recognize the severity of climate change, need to prioritize the battle against it when shaping their careers.

“Our fight against climate change is not going to be over by 2050, but 25 years from now, we’re going to know if we transitioned to a net-zero-emitting society for the sake of humanity,” Hsu says. “The students are more aware than ever that climate change is going to dominate their lives. I want students to look back with satisfaction that they helped society.”

More bluntly, he says: “Are you going to say, ‘Oh, I made some money and enhanced my career, but the planet’s going to be destroyed? Or ideally will you find a job that’s satisfying and can support your future hopes for yourself and your family, and also save the planet? Because I think there are a lot of [job] options like that out there.”

Hsu adds, “We’re going to need people pulling in different directions. It’s all important. That’s the message to our students. Go find something you think is important and use your skills. We’re going to need that many people to work on climate change.”

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