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      in Environment

      Q&A: More-sustainable concrete with machine learning

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      As a building material, concrete withstands the test of time. Its use dates back to early civilizations, and today it is the most popular composite choice in the world. However, it’s not without its faults. Production of its key ingredient, cement, contributes 8-9 percent of the global anthropogenic CO2 emissions and 2-3 percent of energy consumption, which is only projected to increase in the coming years. With aging United States infrastructure, the federal government recently passed a milestone bill to revitalize and upgrade it, along with a push to reduce greenhouse gas emissions where possible, putting concrete in the crosshairs for modernization, too.

      Elsa Olivetti, the Esther and Harold E. Edgerton Associate Professor in the MIT Department of Materials Science and Engineering, and Jie Chen, MIT-IBM Watson AI Lab research scientist and manager, think artificial intelligence can help meet this need by designing and formulating new, more sustainable concrete mixtures, with lower costs and carbon dioxide emissions, while improving material performance and reusing manufacturing byproducts in the material itself. Olivetti’s research improves environmental and economic sustainability of materials, and Chen develops and optimizes machine learning and computational techniques, which he can apply to materials reformulation. Olivetti and Chen, along with their collaborators, have recently teamed up for an MIT-IBM Watson AI Lab project to make concrete more sustainable for the benefit of society, the climate, and the economy.

      Q: What applications does concrete have, and what properties make it a preferred building material?

      Olivetti: Concrete is the dominant building material globally with an annual consumption of 30 billion metric tons. That is over 20 times the next most produced material, steel, and the scale of its use leads to considerable environmental impact, approximately 5-8 percent of global greenhouse gas (GHG) emissions. It can be made locally, has a broad range of structural applications, and is cost-effective. Concrete is a mixture of fine and coarse aggregate, water, cement binder (the glue), and other additives.

      Q: Why isn’t it sustainable, and what research problems are you trying to tackle with this project?

      Olivetti: The community is working on several ways to reduce the impact of this material, including alternative fuels use for heating the cement mixture, increasing energy and materials efficiency and carbon sequestration at production facilities, but one important opportunity is to develop an alternative to the cement binder.

      While cement is 10 percent of the concrete mass, it accounts for 80 percent of the GHG footprint. This impact is derived from the fuel burned to heat and run the chemical reaction required in manufacturing, but also the chemical reaction itself releases CO2 from the calcination of limestone. Therefore, partially replacing the input ingredients to cement (traditionally ordinary Portland cement or OPC) with alternative materials from waste and byproducts can reduce the GHG footprint. But use of these alternatives is not inherently more sustainable because wastes might have to travel long distances, which adds to fuel emissions and cost, or might require pretreatment processes. The optimal way to make use of these alternate materials will be situation-dependent. But because of the vast scale, we also need solutions that account for the huge volumes of concrete needed. This project is trying to develop novel concrete mixtures that will decrease the GHG impact of the cement and concrete, moving away from the trial-and-error processes towards those that are more predictive.

      Chen: If we want to fight climate change and make our environment better, are there alternative ingredients or a reformulation we could use so that less greenhouse gas is emitted? We hope that through this project using machine learning we’ll be able to find a good answer.

      Q: Why is this problem important to address now, at this point in history?

      Olivetti: There is urgent need to address greenhouse gas emissions as aggressively as possible, and the road to doing so isn’t necessarily straightforward for all areas of industry. For transportation and electricity generation, there are paths that have been identified to decarbonize those sectors. We need to move much more aggressively to achieve those in the time needed; further, the technological approaches to achieve that are more clear. However, for tough-to-decarbonize sectors, such as industrial materials production, the pathways to decarbonization are not as mapped out.

      Q: How are you planning to address this problem to produce better concrete?

      Olivetti: The goal is to predict mixtures that will both meet performance criteria, such as strength and durability, with those that also balance economic and environmental impact. A key to this is to use industrial wastes in blended cements and concretes. To do this, we need to understand the glass and mineral reactivity of constituent materials. This reactivity not only determines the limit of the possible use in cement systems but also controls concrete processing, and the development of strength and pore structure, which ultimately control concrete durability and life-cycle CO2 emissions.

      Chen: We investigate using waste materials to replace part of the cement component. This is something that we’ve hypothesized would be more sustainable and economic — actually waste materials are common, and they cost less. Because of the reduction in the use of cement, the final concrete product would be responsible for much less carbon dioxide production. Figuring out the right concrete mixture proportion that makes endurable concretes while achieving other goals is a very challenging problem. Machine learning is giving us an opportunity to explore the advancement of predictive modeling, uncertainty quantification, and optimization to solve the issue. What we are doing is exploring options using deep learning as well as multi-objective optimization techniques to find an answer. These efforts are now more feasible to carry out, and they will produce results with reliability estimates that we need to understand what makes a good concrete.

      Q: What kinds of AI and computational techniques are you employing for this?

      Olivetti: We use AI techniques to collect data on individual concrete ingredients, mix proportions, and concrete performance from the literature through natural language processing. We also add data obtained from industry and/or high throughput atomistic modeling and experiments to optimize the design of concrete mixtures. Then we use this information to develop insight into the reactivity of possible waste and byproduct materials as alternatives to cement materials for low-CO2 concrete. By incorporating generic information on concrete ingredients, the resulting concrete performance predictors are expected to be more reliable and transformative than existing AI models.

      Chen: The final objective is to figure out what constituents, and how much of each, to put into the recipe for producing the concrete that optimizes the various factors: strength, cost, environmental impact, performance, etc. For each of the objectives, we need certain models: We need a model to predict the performance of the concrete (like, how long does it last and how much weight does it sustain?), a model to estimate the cost, and a model to estimate how much carbon dioxide is generated. We will need to build these models by using data from literature, from industry, and from lab experiments.

      We are exploring Gaussian process models to predict the concrete strength, going forward into days and weeks. This model can give us an uncertainty estimate of the prediction as well. Such a model needs specification of parameters, for which we will use another model to calculate. At the same time, we also explore neural network models because we can inject domain knowledge from human experience into them. Some models are as simple as multi-layer perceptions, while some are more complex, like graph neural networks. The goal here is that we want to have a model that is not only accurate but also robust — the input data is noisy, and the model must embrace the noise, so that its prediction is still accurate and reliable for the multi-objective optimization.

      Once we have built models that we are confident with, we will inject their predictions and uncertainty estimates into the optimization of multiple objectives, under constraints and under uncertainties.

      Q: How do you balance cost-benefit trade-offs?

      Chen: The multiple objectives we consider are not necessarily consistent, and sometimes they are at odds with each other. The goal is to identify scenarios where the values for our objectives cannot be further pushed simultaneously without compromising one or a few. For example, if you want to further reduce the cost, you probably have to suffer the performance or suffer the environmental impact. Eventually, we will give the results to policymakers and they will look into the results and weigh the options. For example, they may be able to tolerate a slightly higher cost under a significant reduction in greenhouse gas. Alternatively, if the cost varies little but the concrete performance changes drastically, say, doubles or triples, then this is definitely a favorable outcome.

      Q: What kinds of challenges do you face in this work?

      Chen: The data we get either from industry or from literature are very noisy; the concrete measurements can vary a lot, depending on where and when they are taken. There are also substantial missing data when we integrate them from different sources, so, we need to spend a lot of effort to organize and make the data usable for building and training machine learning models. We also explore imputation techniques that substitute missing features, as well as models that tolerate missing features, in our predictive modeling and uncertainty estimate.

      Q: What do you hope to achieve through this work?

      Chen: In the end, we are suggesting either one or a few concrete recipes, or a continuum of recipes, to manufacturers and policymakers. We hope that this will provide invaluable information for both the construction industry and for the effort of protecting our beloved Earth.

      Olivetti: We’d like to develop a robust way to design cements that make use of waste materials to lower their CO2 footprint. Nobody is trying to make waste, so we can’t rely on one stream as a feedstock if we want this to be massively scalable. We have to be flexible and robust to shift with feedstocks changes, and for that we need improved understanding. Our approach to develop local, dynamic, and flexible alternatives is to learn what makes these wastes reactive, so we know how to optimize their use and do so as broadly as possible. We do that through predictive model development through software we have developed in my group to automatically extract data from literature on over 5 million texts and patents on various topics. We link this to the creative capabilities of our IBM collaborators to design methods that predict the final impact of new cements. If we are successful, we can lower the emissions of this ubiquitous material and play our part in achieving carbon emissions mitigation goals.

      Other researchers involved with this project include Stefanie Jegelka, the X-Window Consortium Career Development Associate Professor in the MIT Department of Electrical Engineering and Computer Science; Richard Goodwin, IBM principal researcher; Soumya Ghosh, MIT-IBM Watson AI Lab research staff member; and Kristen Severson, former research staff member. Collaborators included Nghia Hoang, former research staff member with MIT-IBM Watson AI Lab and IBM Research; and Jeremy Gregory, research scientist in the MIT Department of Civil and Environmental Engineering and executive director of the MIT Concrete Sustainability Hub.

      This research is supported by the MIT-IBM Watson AI Lab. More

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      in Environment

      3 Questions: Tolga Durak on building a safety culture at MIT

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      Environment, Health, and Safety Managing Director Tolga Durak heads a team working to build a strong safety culture at the Institute and to implement systems that lead to successful lab and makerspace operations. EHS is also pursuing new opportunities in the areas of safe and sustainable labs and applied makerspace research. 

      Durak holds a BS in mechanical engineering, a MS in industrial and systems engineering, and a PhD in building construction/environmental design and planning. He has over 20 years of experience in engineering and EHS in higher education, having served in such roles as authority having jurisdiction, responsible official, fire marshal, risk manager, radiation safety officer, laser safety officer, safety engineer, project manager, and emergency manager for government agencies, as well as universities with extensive health-care and research facilities.

      Q: What “words of wisdom” regarding lab/shop health and safety would you like to share with the research community? 

      A: EHS staff always strive to help maintain the safety and well-being of the MIT community. When it comes to lab/shop safety or any areas with hazards, first and foremost, we encourage wearing the appropriate personal protective equipment (PPE) when handling potentially hazardous materials. While PPE needs depend on the hazards and the space, common PPE includes safety glasses, lab coats, gloves, clothes that cover your skin, and closed-toe shoes. Shorts and open-toe shoes have no place in the lab/shop setting when hazardous materials are stored or used. Accidents will and do happen. The severity of injuries due to accidental exposures can be minimized when researchers are wearing PPE. Remember, there is only one you!   

      Overall, be aware of your surroundings, be knowledgeable about the hazards of the materials and equipment you are using, and be prepared for the unexpected. Ask yourself, “What’s the worst thing that can happen during this experiment or procedure?” Prepare by doing a thorough risk assessment, ask others who may be knowledgeable for their ideas and help, and standardize procedures where possible. Be prepared to respond appropriately when an emergency arises. 

      Safety in our classrooms, labs, and makerspaces is paramount and requires a collaborative effort. 

      Q: What are the established programs within EHS that students and researchers should be aware of, and what opportunities and challenges do you face trying to advance a healthy safety culture at MIT? 

      A: The EHS program staff in Biosafety, Industrial Hygiene, Environmental Management, Occupational and Construction Safety, and Radiation Protection are ready to assist with risk assessments, chemical safety, physical hazards, hazard-specific training, materials management, and hazardous waste disposal and reuse/recycling. Locally, each department, laboratory, and center has an EHS coordinator, as well as an assigned EHS team, to assist in the implementation of required EHS programs. Each lab/shop also has a designated EHS representative — someone who has local knowledge of your lab/shop and can help you with safety requirements specific to your work area.  

      One of the biggest challenges we face is that due to the decentralized nature of the Institute, no one size fits all when it comes to implementing successful safety practices. We also view this as an opportunity to enhance our safety culture. A strong safety culture is reflected at MIT when all lab and makerspace members are willing to look out for each other, challenge the status quo when necessary, and do the right thing even when no one is looking. In labs/shops with a strong safety culture, faculty and researchers discuss safety topics at group meetings, group members remind each other to wear the appropriate PPE (lab coats, safety glasses, etc.), more experienced team members mentor the newcomers, and riskier operations are reviewed and assessed to make them as safe as possible.  

      Q: Can you describe the new Safe and Sustainable Laboratories (S2L) efforts and the makerspace operational research programs envisioned for the future? 

      A: The MIT EHS Office has a plan for renewing its dedication to sustainability and climate action. We are dedicated to doing our part to promote a research environment that assures the highest level of health and safety but also strives to reduce energy, water, and waste through educating and supporting faculty, students, and researchers. With the goal of integrating sustainability across the lab sector of campus and bridging that with the Institute’s climate action goals, EHS has partnered with the MIT Office of Sustainability, Department of Facilities, vice president for finance, and vice president for campus services and stewardship to relaunch the “green” labs sustainability efforts under a new Safe and Sustainable Labs program.

      Part of that plan is to implement a Sustainable Labs Certification program. The process is designed to be as easy as possible for the lab groups. We are starting with simple actions like promoting the use of equipment timers in certain locations to conserve energy, fume hood/ventilation management, preventative maintenance for ultra-low-temperature freezers, increasing recycling, and helping labs update their central chemical inventory system, which can help forecast MIT’s potential waste streams. 

      EHS has also partnered with Project Manus to build a test-bed lab to study potential health and environmental exposures present in makerspaces as a result of specialized equipment and processes with our new Applied Makerspace Research Initiative.   More

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      in Energy

      Design’s new frontier

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      In the 1960s, the advent of computer-aided design (CAD) sparked a revolution in design. For his PhD thesis in 1963, MIT Professor Ivan Sutherland developed Sketchpad, a game-changing software program that enabled users to draw, move, and resize shapes on a computer. Over the course of the next few decades, CAD software reshaped how everything from consumer products to buildings and airplanes were designed.

      “CAD was part of the first wave in computing in design. The ability of researchers and practitioners to represent and model designs using computers was a major breakthrough and still is one of the biggest outcomes of design research, in my opinion,” says Maria Yang, Gail E. Kendall Professor and director of MIT’s Ideation Lab.

      Innovations in 3D printing during the 1980s and 1990s expanded CAD’s capabilities beyond traditional injection molding and casting methods, providing designers even more flexibility. Designers could sketch, ideate, and develop prototypes or models faster and more efficiently. Meanwhile, with the push of a button, software like that developed by Professor Emeritus David Gossard of MIT’s CAD Lab could solve equations simultaneously to produce a new geometry on the fly.

      In recent years, mechanical engineers have expanded the computing tools they use to ideate, design, and prototype. More sophisticated algorithms and the explosion of machine learning and artificial intelligence technologies have sparked a second revolution in design engineering.

      Researchers and faculty at MIT’s Department of Mechanical Engineering are utilizing these technologies to re-imagine how the products, systems, and infrastructures we use are designed. These researchers are at the forefront of the new frontier in design.

      Computational design

      Faez Ahmed wants to reinvent the wheel, or at least the bicycle wheel. He and his team at MIT’s Design Computation & Digital Engineering Lab (DeCoDE) use an artificial intelligence-driven design method that can generate entirely novel and improved designs for a range of products — including the traditional bicycle. They create advanced computational methods to blend human-driven design with simulation-based design.

      “The focus of our DeCoDE lab is computational design. We are looking at how we can create machine learning and AI algorithms to help us discover new designs that are optimized based on specific performance parameters,” says Ahmed, an assistant professor of mechanical engineering at MIT.

      For their work using AI-driven design for bicycles, Ahmed and his collaborator Professor Daniel Frey wanted to make it easier to design customizable bicycles, and by extension, encourage more people to use bicycles over transportation methods that emit greenhouse gases.

      To start, the group gathered a dataset of 4,500 bicycle designs. Using this massive dataset, they tested the limits of what machine learning could do. First, they developed algorithms to group bicycles that looked similar together and explore the design space. They then created machine learning models that could successfully predict what components are key in identifying a bicycle style, such as a road bike versus a mountain bike.

      Once the algorithms were good enough at identifying bicycle designs and parts, the team proposed novel machine learning tools that could use this data to create a unique and creative design for a bicycle based on certain performance parameters and rider dimensions.

      Ahmed used a generative adversarial network — or GAN — as the basis of this model. GAN models utilize neural networks that can create new designs based on vast amounts of data. However, using GAN models alone would result in homogeneous designs that lack novelty and can’t be assessed in terms of performance. To address these issues in design problems, Ahmed has developed a new method which he calls “PaDGAN,” performance augmented diverse GAN.

      “When we apply this type of model, what we see is that we can get large improvements in the diversity, quality, as well as novelty of the designs,” Ahmed explains.

      Using this approach, Ahmed’s team developed an open-source computational design tool for bicycles freely available on their lab website. They hope to further develop a set of generalizable tools that can be used across industries and products.

      Longer term, Ahmed has his sights set on loftier goals. He hopes the computational design tools he develops could lead to “design democratization,” putting more power in the hands of the end user.

      “With these algorithms, you can have more individualization where the algorithm assists a customer in understanding their needs and helps them create a product that satisfies their exact requirements,” he adds.

      Using algorithms to democratize the design process is a goal shared by Stefanie Mueller, an associate professor in electrical engineering and computer science and mechanical engineering.

      Personal fabrication

      Platforms like Instagram give users the freedom to instantly edit their photographs or videos using filters. In one click, users can alter the palette, tone, and brightness of their content by applying filters that range from bold colors to sepia-toned or black-and-white. Mueller, X-Window Consortium Career Development Professor, wants to bring this concept of the Instagram filter to the physical world.

      “We want to explore how digital capabilities can be applied to tangible objects. Our goal is to bring reprogrammable appearance to the physical world,” explains Mueller, director of the HCI Engineering Group based out of MIT’s Computer Science and Artificial Intelligence Laboratory.

      Mueller’s team utilizes a combination of smart materials, optics, and computation to advance personal fabrication technologies that would allow end users to alter the design and appearance of the products they own. They tested this concept in a project they dubbed “Photo-Chromeleon.”

      First, a mix of photochromic cyan, magenta, and yellow dies are airbrushed onto an object — in this instance, a 3D sculpture of a chameleon. Using software they developed, the team sketches the exact color pattern they want to achieve on the object itself. An ultraviolet light shines on the object to activate the dyes.

      To actually create the physical pattern on the object, Mueller has developed an optimization algorithm to use alongside a normal office projector outfitted with red, green, and blue LED lights. These lights shine on specific pixels on the object for a given period of time to physically change the makeup of the photochromic pigments.

      “This fancy algorithm tells us exactly how long we have to shine the red, green, and blue light on every single pixel of an object to get the exact pattern we’ve programmed in our software,” says Mueller.

      Giving this freedom to the end user enables limitless possibilities. Mueller’s team has applied this technology to iPhone cases, shoes, and even cars. In the case of shoes, Mueller envisions a shoebox embedded with UV and LED light projectors. Users could put their shoes in the box overnight and the next day have a pair of shoes in a completely new pattern.

      Mueller wants to expand her personal fabrication methods to the clothes we wear. Rather than utilize the light projection technique developed in the PhotoChromeleon project, her team is exploring the possibility of weaving LEDs directly into clothing fibers, allowing people to change their shirt’s appearance as they wear it. These personal fabrication technologies could completely alter consumer habits.

      “It’s very interesting for me to think about how these computational techniques will change product design on a high level,” adds Mueller. “In the future, a consumer could buy a blank iPhone case and update the design on a weekly or daily basis.”

      Computational fluid dynamics and participatory design

      Another team of mechanical engineers, including Sili Deng, the Brit (1961) & Alex (1949) d’Arbeloff Career Development Professor, are developing a different kind of design tool that could have a large impact on individuals in low- and middle-income countries across the world.

      As Deng walked down the hallway of Building 1 on MIT’s campus, a monitor playing a video caught her eye. The video featured work done by mechanical engineers and MIT D-Lab on developing cleaner burning briquettes for cookstoves in Uganda. Deng immediately knew she wanted to get involved.

      “As a combustion scientist, I’ve always wanted to work on such a tangible real-world problem, but the field of combustion tends to focus more heavily on the academic side of things,” explains Deng.

      After reaching out to colleagues in MIT D-Lab, Deng joined a collaborative effort to develop a new cookstove design tool for the 3 billion people across the world who burn solid fuels to cook and heat their homes. These stoves often emit soot and carbon monoxide, leading not only to millions of deaths each year, but also worsening the world’s greenhouse gas emission problem.

      The team is taking a three-pronged approach to developing this solution, using a combination of participatory design, physical modeling, and experimental validation to create a tool that will lead to the production of high-performing, low-cost energy products.

      Deng and her team in the Deng Energy and Nanotechnology Group use physics-based modeling for the combustion and emission process in cookstoves.

      “My team is focused on computational fluid dynamics. We use computational and numerical studies to understand the flow field where the fuel is burned and releases heat,” says Deng.

      These flow mechanics are crucial to understanding how to minimize heat loss and make cookstoves more efficient, as well as learning how dangerous pollutants are formed and released in the process.

      Using computational methods, Deng’s team performs three-dimensional simulations of the complex chemistry and transport coupling at play in the combustion and emission processes. They then use these simulations to build a combustion model for how fuel is burned and a pollution model that predicts carbon monoxide emissions.

      Deng’s models are used by a group led by Daniel Sweeney in MIT D-Lab to test the experimental validation in prototypes of stoves. Finally, Professor Maria Yang uses participatory design methods to integrate user feedback, ensuring the design tool can actually be used by people across the world.

      The end goal for this collaborative team is to not only provide local manufacturers with a prototype they could produce themselves, but to also provide them with a tool that can tweak the design based on local needs and available materials.

      Deng sees wide-ranging applications for the computational fluid dynamics her team is developing.

      “We see an opportunity to use physics-based modeling, augmented with a machine learning approach, to come up with chemical models for practical fuels that help us better understand combustion. Therefore, we can design new methods to minimize carbon emissions,” she adds.

      While Deng is utilizing simulations and machine learning at the molecular level to improve designs, others are taking a more macro approach.

      Designing intelligent systems

      When it comes to intelligent design, Navid Azizan thinks big. He hopes to help create future intelligent systems that are capable of making decisions autonomously by using the enormous amounts of data emerging from the physical world. From smart robots and autonomous vehicles to smart power grids and smart cities, Azizan focuses on the analysis, design, and control of intelligent systems.

      Achieving such massive feats takes a truly interdisciplinary approach that draws upon various fields such as machine learning, dynamical systems, control, optimization, statistics, and network science, among others.

      “Developing intelligent systems is a multifaceted problem, and it really requires a confluence of disciplines,” says Azizan, assistant professor of mechanical engineering with a dual appointment in MIT’s Institute for Data, Systems, and Society (IDSS). “To create such systems, we need to go beyond standard approaches to machine learning, such as those commonly used in computer vision, and devise algorithms that can enable safe, efficient, real-time decision-making for physical systems.”

      For robot control to work in the complex dynamic environments that arise in the real world, real-time adaptation is key. If, for example, an autonomous vehicle is going to drive in icy conditions or a drone is operating in windy conditions, they need to be able to adapt to their new environment quickly.

      To address this challenge, Azizan and his collaborators at MIT and Stanford University have developed a new algorithm that combines adaptive control, a powerful methodology from control theory, with meta learning, a new machine learning paradigm.

      “This ‘control-oriented’ learning approach outperforms the existing ‘regression-oriented’ methods, which are mostly focused on just fitting the data, by a wide margin,” says Azizan.

      Another critical aspect of deploying machine learning algorithms in physical systems that Azizan and his team hope to address is safety. Deep neural networks are a crucial part of autonomous systems. They are used for interpreting complex visual inputs and making data-driven predictions of future behavior in real time. However, Azizan urges caution.

      “These deep neural networks are only as good as their training data, and their predictions can often be untrustworthy in scenarios not covered by their training data,” he says. Making decisions based on such untrustworthy predictions could lead to fatal accidents in autonomous vehicles or other safety-critical systems.

      To avoid these potentially catastrophic events, Azizan proposes that it is imperative to equip neural networks with a measure of their uncertainty. When the uncertainty is high, they can then be switched to a “safe policy.”

      In pursuit of this goal, Azizan and his collaborators have developed a new algorithm known as SCOD — Sketching Curvature of Out-of-Distribution Detection. This framework could be embedded within any deep neural network to equip them with a measure of their uncertainty.

      “This algorithm is model-agnostic and can be applied to neural networks used in various kinds of autonomous systems, whether it’s drones, vehicles, or robots,” says Azizan.

      Azizan hopes to continue working on algorithms for even larger-scale systems. He and his team are designing efficient algorithms to better control supply and demand in smart energy grids. According to Azizan, even if we create the most efficient solar panels and batteries, we can never achieve a sustainable grid powered by renewable resources without the right control mechanisms.

      Mechanical engineers like Ahmed, Mueller, Deng, and Azizan serve as the key to realizing the next revolution of computing in design.

      “MechE is in a unique position at the intersection of the computational and physical worlds,” Azizan says. “Mechanical engineers build a bridge between theoretical, algorithmic tools and real, physical world applications.”

      Sophisticated computational tools, coupled with the ground truth mechanical engineers have in the physical world, could unlock limitless possibilities for design engineering, well beyond what could have been imagined in those early days of CAD. More

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      in Energy

      MIT makes strides on climate action plan

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      Two recent online events related to MIT’s ambitious new climate action plan highlighted several areas of progress, including uses of the campus as a real-life testbed for climate impact research, the creation of new planning bodies with opportunities for input from all parts of the MIT community, and a variety of moves toward reducing the Institute’s own carbon footprint in ways that may also provide a useful model for others.

      On Monday, MIT’s Office of Sustainability held its seventh annual “Sustainability Connect” event, bringing together students, faculty, staff, and alumni to learn about and share ideas for addressing climate change. This year’s virtual event emphasized the work toward carrying out the climate plan, titled “Fast Forward: MIT’s Climate Action Plan for the Decade,” which was announced in May. An earlier event, the “MIT Climate Tune-in” on Nov. 3, provided an overview of the many areas of MIT’s work to tackle climate change and featured a video message from Maria Zuber, MIT’s vice president for research, who was attending the COP26 international climate meeting in Glasgow, Scotland, as part of an 18-member team from MIT.

      Zuber pointed out some significant progress that was made at the conference, including a broad agreement by over 100 nations to end deforestation by the end of the decade; she also noted that the U.S. and E.U. are leading a global coalition of countries committed to curbing methane emissions by 30 percent from 2020 levels by decade’s end. “It’s easy to be pessimistic,” she said, “but being here in Glasgow, I’m actually cautiously optimistic, seeing the thousands and thousands of people here who are working toward meaningful climate action. And I know that same spirit exists on our own campus also.”

      As for MIT’s own climate plan, Zuber emphasized three points: “We’re committed to action; second of all, we’re committed to moving fast; and third, we’ve organized ourselves better for success.” That organization includes the creation of the MIT Climate Steering Committee, to oversee and coordinate MIT’s strategies on climate change; the Climate Nucleus, to oversee the management and implementation of the new plan; and three working groups that are forming now, to involve all parts of the MIT community.

      The “Fast Forward” plan calls for reducing the campus’s net greenhouse gas emissions to zero by 2026 and eliminating all such emissions, including indirect ones, by 2050. At Monday’s event, Director of Sustainability Julie Newman pointed out that the climate plan includes no less than 14 specific commitments related to the campus itself. These can be grouped into five broad areas, she said: mitigation, resiliency, electric vehicle infrastructure, investment portfolio sustainability, and climate leadership. “Each of these commitments has due dates, and they range from the tactical to the strategic,” she said. “We’re in the midst of activating our internal teams” to address these commitments, she added, noting that there are 30 teams that involve 75 faculty and researcher members, plus up to eight student positions.

      One specific project that is well underway involves preparing a detailed map of the flood risks to the campus as sea levels rise and storm surges increase. While previous attempts to map out the campus flooding risks had treated buildings essentially as uniform blocks, the new project has already mapped out in detail the location, elevation, and condition of every access point — doors, windows, and drains — in every building in the main campus, and now plans to extend the work to the residence buildings and outlying parts of campus. The project’s methods for identifying and quantifying the risks to specific parts of the campus, Newman said, represents “part of our mission for leveraging the campus as a test bed” by creating a map that is “true to the nature of the topography and the infrastructure,” in order to be prepared for the effects of climate change.

      Also speaking at the Sustainability Connect event, Vice President for Campus Services and Stewardship Joe Higgins outlined a variety of measures that are underway to cut the carbon footprint of the campus as much as possible, as quickly as possible. Part of that, he explained, involves using the campus as a testbed for the development of the equivalent of a “smart thermostat” system for campus buildings. While such products exist commercially for homeowners, there is no such system yet for large institutional or commercial buildings.

      There is a team actively developing such a pilot program in some MIT buildings, he said, focusing on some large lab buildings that have especially high energy usage. They are examining the use of artificial intelligence to reduce energy consumption, he noted. By adding systems to monitor energy use, temperatures, occupancy, and so on, and to control heating, lighting and air conditioning systems, Higgins said at least a 3 to 5 percent reduction in energy use can be realized. “It may be well beyond that,” he added. “There’s a huge opportunity here.”

      Higgins also outlined the ongoing plan to convert the existing steam distribution system for campus heating into a hot water system. Though the massive undertaking may take decades to complete, he said that project alone may reduce campus carbon emissions by 10 percent. Other efforts include the installation of an additional 400 kilowatts of rooftop solar installations.

      Jeremy Gregory, executive director of MIT’s climate and sustainability consortium, described efforts to deal with the most far-reaching areas of greenhouse gas emission, the so-called Scope 3 emissions. He explained that Scope 1 is the direct emissions from the campus itself, from buildings and vehicles; Scope 2 includes indirect emissions from the generation of electricity; and Scope 3 is “everything else.” That includes employee travel, buildings that MIT leases from others and to others, and all goods and services, he added, “so it includes a lot of different categories of emissions.” Gregory said his team, including several student fellows, is actively investigating and quantifying these Scope 3 emissions at MIT, along with potential methods of reducing them.

      Professor Noelle Selin, who was recently named as co-chair of the new Climate Nucleus along with Professor Anne White, outlined their plans for the coming year, including the setting up of the three working groups.

      Selin said the nucleus consists of representatives of departments, labs, centers, and institutes that have significant responsibilities under the climate plan. That body will make recommendations to the steering committee, which includes the deans of all five of MIT’s schools and the MIT Schwarzman College of Computing, “about how to amplify MIT’s impact in the climate sphere. We have an implementation role, but we also have an accelerator pedal that can really make MIT’s climate impact more ambitious, and really push the buttons and make sure that the Institute’s commitments are actually borne out in reality.”

      The MIT Climate Tune-In also featured Selin and White, as well as a presentation on MIT’s expanded educational offerings on climate and sustainability, from Sarah Meyers, ESI’s education program manager; students Derek Allmond and Natalie Northrup; and postdoc Peter Godart. Professor Dennis Whyte also spoke about MIT and Commonwealth Fusion Systems’ recent historical advance toward commercial fusion energy. Organizers said that the Climate Tune-In event is the first of what they hope will be many opportunities to hear updates on the wide range of work happening across campus to implement the Fast Forward plan, and to spark conversations within the MIT community. More

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      in Environment

      MIT collaborates with Biogen on three-year, $7 million initiative to address climate, health, and equity

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      MIT and Biogen have announced that they will collaborate with the goal to accelerate the science and action on climate change to improve human health. This collaboration is supported by a three-year, $7 million commitment from the company and the Biogen Foundation. The biotechnology company, headquartered in Cambridge, Massachusetts’ Kendall Square, discovers and develops therapies for people living with serious neurological diseases.

      “We have long believed it is imperative for Biogen to make the fight against climate change central to our long-term corporate responsibility commitments. Through this collaboration with MIT, we aim to identify and share innovative climate solutions that will deliver co-benefits for both health and equity,” says Michel Vounatsos, CEO of Biogen. “We are also proud to support the MIT Museum, which promises to make world-class science and education accessible to all, and honor Biogen co-founder Phillip A. Sharp with a dedication inside the museum that recognizes his contributions to its development.”

      Biogen and the Biogen Foundation are supporting research and programs across a range of areas at MIT.

      Advancing climate, health, and equity

      The first such effort involves new work within the MIT Joint Program on the Science and Policy of Global Change to establish a state-of-the-art integrated model of climate and health aimed at identifying targets that deliver climate and health co-benefits.

      “Evidence suggests that not all climate-related actions deliver equal health benefits, yet policymakers, planners, and stakeholders traditionally lack the tools to consider how decisions in one arena impact the other,” says C. Adam Schlosser, deputy director of the MIT Joint Program. “Biogen’s collaboration with the MIT Joint Program — and its support of a new distinguished Biogen Fellow who will develop the new climate/health model — will accelerate our efforts to provide decision-makers with these tools.”

      Biogen is also supporting the MIT Technology and Policy Program’s Research to Policy Engagement Initiative to infuse human health as a key new consideration in decision-making on the best pathways forward to address the global climate crisis, and bridge the knowledge-to-action gap by connecting policymakers, researchers, and diverse stakeholders. As part of this work, Biogen is underwriting a distinguished Biogen Fellow to advance new research on climate, health, and equity.

      “Our work with Biogen has allowed us to make progress on key questions that matter to human health and well-being under climate change,” says Noelle Eckley Selin, who directs the MIT Technology and Policy Program and is a professor in the MIT Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences. “Further, their support of the Research to Policy Engagement Initiative helps all of our research become more effective in making change.”

      In addition, Biogen has joined 13 other companies in the MIT Climate and Sustainability Consortium (MCSC), which is supporting faculty and student research and developing impact pathways that present a range of actionable steps that companies can take — within and across industries — to advance progress toward climate targets.

      “Biogen joining the MIT Climate and Sustainability Consortium represents our commitment to working with member companies across a diverse range of industries, an approach that aims to drive changes swift and broad enough to match the scale of the climate challenge,” says Jeremy Gregory, executive director of the MCSC. “We are excited to welcome a member from the biotechnology space and look forward to harnessing Biogen’s perspectives as we continue to collaborate and work together with the MIT community in exciting and meaningful ways.”

      Making world-class science and education available to MIT Museum visitors

      Support from Biogen will honor Nobel laureate, MIT Institute professor, and Biogen co-founder Phillip A. Sharp with a named space inside the new Kendall Square location of the MIT Museum, set to open in spring 2022. Biogen also is supporting one of the museum’s opening exhibitions, “Essential MIT,” with a section focused on solving real-world problems such as climate change. It is also providing programmatic support for the museum’s Life Sciences Maker Engagement Program.

      “Phil has provided fantastic support to the MIT Museum for more than a decade as an advisory board member and now as board chair, and he has been deeply involved in plans for the new museum at Kendall Square,” says John Durant, the Mark R. Epstein (Class of 1963) Director of the museum. “Seeing his name on the wall will be a constant reminder of his key role in this development, as well as a mark of our gratitude.”

      Inspiring and empowering the next generation of scientists

      Biogen funding is also being directed to engage the next generation of scientists through support for the Biogen-MIT Biotech in Action: Virtual Lab, a program designed to foster a love of science among diverse and under-served student populations.

      Biogen’s support is part of its Healthy Climate, Healthy Lives initiative, a $250 million, 20-year commitment to eliminate fossil fuels across its operations and collaborate with renowned institutions to advance the science of climate and health and support under-served communities. Additional support is provided by the Biogen Foundation to further its long-standing focus on providing students with equitable access to outstanding science education. More

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      in Energy

      MIT Energy Initiative awards seven Seed Fund grants for early-stage energy research

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      The MIT Energy Initiative (MITEI) has awarded seven Seed Fund grants to support novel, early-stage energy research by faculty and researchers at MIT. The awardees hail from a range of disciplines, but all strive to bring their backgrounds and expertise to address the global climate crisis by improving the efficiency, scalability, and adoption of clean energy technologies.

      “Solving climate change is truly an interdisciplinary challenge,” says MITEI Director Robert C. Armstrong. “The Seed Fund grants foster collaboration and innovation from across all five of MIT’s schools and one college, encouraging an ‘all hands on deck approach’ to developing the energy solutions that will prove critical in combatting this global crisis.”

      This year, MITEI’s Seed Fund grant program received 70 proposals from 86 different principal investigators (PIs) across 25 departments, labs, and centers. Of these proposals, 31 involved collaborations between two or more PIs, including 24 that involved multiple departments.

      The winning projects reflect this collaborative nature with topics addressing the optimization of low-energy thermal cooling in buildings; the design of safe, robust, and resilient distributed power systems; and how to design and site wind farms with consideration of wind resource uncertainty due to climate change.

      Increasing public support for low-carbon technologies

      One winning team aims to leverage work done in the behavioral sciences to motivate sustainable behaviors and promote the adoption of clean energy technologies.

      “Objections to scalable low-carbon technologies such as nuclear energy and carbon sequestration have made it difficult to adopt these technologies and reduce greenhouse gas emissions,” says Howard Herzog, a senior research scientist at MITEI and co-PI. “These objections tend to neglect the sheer scale of energy generation required and the inability to meet this demand solely with other renewable energy technologies.”

      This interdisciplinary team — which includes researchers from MITEI, the Department of Nuclear Science and Engineering, and the MIT Sloan School of Management — plans to convene industry professionals and academics, as well as behavioral scientists, to identify common objections, design messaging to overcome them, and prove that these messaging campaigns have long-lasting impacts on attitudes toward scalable low-carbon technologies.

      “Our aim is to provide a foundation for shifting the public and policymakers’ views about these low-carbon technologies from something they, at best, tolerate, to something they actually welcome,” says co-PI David Rand, the Erwin H. Schell Professor and professor of management science and brain and cognitive sciences at MIT Sloan School of Management.

      Siting and designing wind farms

      Michael Howland, an assistant professor of civil and environmental engineering, will use his Seed Fund grant to develop a foundational methodology for wind farm siting and design that accounts for the uncertainty of wind resources resulting from climate change.

      “The optimal wind farm design and its resulting cost of energy is inherently dependent on the wind resource at the location of the farm,” says Howland. “But wind farms are currently sited and designed based on short-term climate records that do not account for the future effects of climate change on wind patterns.”

      Wind farms are capital-intensive infrastructure that cannot be relocated and often have lifespans exceeding 20 years — all of which make it especially important that developers choose the right locations and designs based not only on wind patterns in the historical climate record, but also based on future predictions. The new siting and design methodology has the potential to replace current industry standards to enable a more accurate risk analysis of wind farm development and energy grid expansion under climate change-driven energy resource uncertainty.

      Membraneless electrolyzers for hydrogen production

      Producing hydrogen from renewable energy-powered water electrolyzers is central to realizing a sustainable and low-carbon hydrogen economy, says Kripa Varanasi, a professor of mechanical engineering and a Seed Fund award recipient. The idea of using hydrogen as a fuel has existed for decades, but it has yet to be widely realized at a considerable scale. Varanasi hopes to change that with his Seed Fund grant.

      “The critical economic hurdle for successful electrolyzers to overcome is the minimization of the capital costs associated with their deployment,” says Varanasi. “So, an immediate task at hand to enable electrochemical hydrogen production at scale will be to maximize the effectiveness of the most mature, least complex, and least expensive water electrolyzer technologies.”

      To do this, he aims to combine the advantages of existing low-temperature alkaline electrolyzer designs with a novel membraneless electrolyzer technology that harnesses a gas management system architecture to minimize complexity and costs, while also improving efficiency. Varanasi hopes his project will demonstrate scalable concepts for cost-effective electrolyzer technology design to help realize a decarbonized hydrogen economy.

      Since its establishment in 2008, the MITEI Seed Fund Program has supported 194 energy-focused seed projects through grants totaling more than $26 million. This funding comes primarily from MITEI’s founding and sustaining members, supplemented by gifts from generous donors.

      Recipients of the 2021 MITEI Seed Fund grants are:

      “Design automation of safe, robust, and resilient distributed power systems” — Chuchu Fan of the Department of Aeronautics and Astronautics
      “Advanced MHD topping cycles: For fission, fusion, solar power plants” — Jeffrey Freidberg of the Department of Nuclear Science and Engineering and Dennis Whyte of the Plasma Science and Fusion Center
      “Robust wind farm siting and design under climate-change‐driven wind resource uncertainty” — Michael Howland of the Department of Civil and Environmental Engineering
      “Low-energy thermal comfort for buildings in the Global South: Optimal design of integrated structural-thermal systems” — Leslie Norford of the Department of Architecture and Caitlin Mueller of the departments of Architecture and Civil and Environmental Engineering
      “New low-cost, high energy-density boron-based redox electrolytes for nonaqueous flow batteries” — Alexander Radosevich of the Department of Chemistry
      “Increasing public support for scalable low-carbon energy technologies using behavorial science insights” — David Rand of the MIT Sloan School of Management, Koroush Shirvan of the Department of Nuclear Science and Engineering, Howard Herzog of the MIT Energy Initiative, and Jacopo Buongiorno of the Department of Nuclear Science and Engineering
      “Membraneless electrolyzers for efficient hydrogen production using nanoengineered 3D gas capture electrode architectures” — Kripa Varanasi of the Department of Mechanical Engineering More

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      in Environment

      For campus “porosity hunters,” climate resilience is the goal

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      At MIT, it’s not uncommon to see groups navigating campus with smartphones and measuring devices in hand, using the Institute as a test bed for research. During one week this summer more than a dozen students, researchers, and faculty, plus an altimeter, could be seen doing just that as they traveled across MIT to measure the points of entry into campus buildings — including windows, doors, and vents — known as a building’s porosity.

      Why measure campus building porosity?

      The group was part of the MIT Porosity Hunt, a citizen-science effort that is using the MIT campus as a place to test emerging methodologies, instruments, and data collection processes to better understand the potential impact of a changing climate — and specifically storm scenarios resulting from it — on infrastructure. The hunt is a collaborative effort between the Urban Risk Lab, led by director and associate professor of architecture and urbanism Miho Mazereeuw, and the Office of Sustainability (MITOS), aimed at supporting an MIT that is resilient to the impacts of climate change, including flooding and extreme heat events. Working over three days, members of the hunt catalogued openings in dozens of buildings across campus to better support flood mapping and resiliency planning at MIT.

      For Mazereeuw, the data collection project lies at the nexus of her work with the Urban Risk Lab and as a member of MIT’s Climate Resiliency Committee. While the lab’s mission is to “develop methods, prototypes, and technologies to embed risk reduction and preparedness into the design of cities and regions to increase resilience,” the Climate Resiliency Committee — made up of faculty, staff, and researchers — is focused on assessing, planning, and operationalizing a climate-resilient MIT. The work of both the lab and the committee is embedded in the recently released MIT Climate Resiliency Dashboard, a visualization tool that allows users to understand potential flooding impacts of a number of storm scenarios and drive decision-making.

      While the debut of the tool signaled a big advancement in resiliency planning at MIT, some, including Mazereeuw, saw an opportunity for enhancement. In working with Ken Strzepek, a MITOS Faculty Fellow and research scientist at the MIT Center for Global Change Science who was also an integral part of this work, Mazereeuw says she was surprised to learn that even the most sophisticated flood modeling treats buildings as solid blocks. With all buildings being treated the same, despite varying porosity, the dashboard is limited in some flood scenario analysis. To address this, Mazereeuw and others got to work to fill in that additional layer of data, with the citizen science efforts a key factor of that work. “Understanding the porosity of the building is important to understanding how much water actually goes in the building in these scenarios,” she explains.

      Though surveyors are often used to collect and map this type of information, Mazereeuw wanted to leverage the MIT community in order to collect data quickly while engaging students, faculty, and researchers as resiliency stewards for the campus. “It’s important for projects like this to encourage awareness,” she explains. “Generally, when something fails, we notice it, but otherwise we don’t. With climate change bringing on more uncertainty in the scale and intensity of events, we need everyone to be more aware and help us understand things like vulnerabilities.”

      To do this, MITOS and the Urban Risk Lab reached out to more than a dozen students, who were joined by faculty, staff, and researchers, to map porosity of 31 campus buildings connected by basements. The buildings were chosen based on this connectivity, understanding that water that reaches one basement could potentially flow to another.

      Urban Risk Lab research scientists Aditya Barve and Mayank Ojha aided the group’s efforts by creating a mapping app and chatbot to support consistency in reporting and ease of use. Each team member used the app to find buildings where porosity points needed to be mapped. As teams arrived at the building exteriors, they entered their location in the app, which then triggered the Facebook and LINE-powered chatbot on their phone. There, students were guided through measuring the opening, adjusting for elevation to correlate to the City of Cambridge base datum, and, based on observable features, noting the materials and quality of the opening on a one-through-three scale. Over just three days, the team, which included Mazereeuw herself, mapped 1,030 porosity points that will aid in resiliency planning and preparation on campus in a number of ways.

      “The goal is to understand various heights for flood waters around porous spots on campus,” says Mazereeuw. “But the impact can be different depending on the space. We hope this data can inform safety as well as understanding potential damage to research or disruption to campus operations from future storms.”

      The porosity data collection is complete for this round — future hunts will likely be conducted to confirm and converge data — but one team member’s work continues at the basement level of MIT. Katarina Boukin, a PhD student in civil and environmental engineering and PhD student fellow with MITOS, has been focused on methods of collecting data beneath buildings at MIT to understand how they would be impacted if flood water were to enter. “We have a number of connected basements on campus, and if one of them floods, potentially all of them do,” explains Boukin. “By looking at absolute elevation and porosity, we’re connecting the outside to the inside and tracking how much and where water may flow.” With the added data from the Porosity Hunt, a complete picture of vulnerabilities and resiliency opportunities can be shared.

      Synthesizing much of this data is where Eva Then ’21 comes in. Then was among the students who worked to capture data points over the three days and is now working in ArcGIS — an online mapping software that also powers the Climate Resiliency Dashboard — to process and visualize the data collected. Once completed, the data will be incorporated into the campus flood model to increase the accuracy of projections on the Climate Resiliency Dashboard. “Over the next decades, the model will serve as an adaptive planning tool to make campus safe and resilient amid growing climate risks,” Then says.

      For Mazereeuw, the Porosity Hunt and data collected additionally serve as a study in scalability, providing valuable insight on how similar research efforts inspired by the MIT test bed approach could be undertaken and inform policy beyond MIT. She also hopes it will inspire students to launch their own hunts in the future, becoming resiliency stewards for their campus and dorms. “Going through measuring and documenting turns on and shows a new set of goggles — you see campus and buildings in a slightly different way,” she says, “Having people look carefully and document change is a powerful tool in climate and resiliency planning.” 

      Mazereeuw also notes that recent devastating flooding events across the country, including those resulting from Hurricane Ida, have put a special focus on this work. “The loss of life that occurred in that storm, including those who died as waters flooded their basement homes  underscores the urgency of this type of research, planning, and readiness.” More

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      in Environment

      Taylor Perron receives 2021 MacArthur Fellowship

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      Taylor Perron, professor of geology and associate department head for education in MIT’s Department of Earth, Atmospheric, and Planetary Sciences, has been named a recipient of a 2021 MacArthur Fellowship.

      Often referred to as “genius grants,” the fellowships are awarded by the John D. and Catherine T. MacArthur Foundation to talented individuals in a variety of fields. Each MacArthur fellow receives a $625,000 stipend, which they are free to use as they see fit. Recipients are notified by the foundation of their selection shortly before the fellowships are publicly announced.

      “After I had absorbed what they were saying, the first thing I thought was, I couldn’t wait to tell my wife, Lisa,” Perron says of receiving the call. “We’ve been a team through all of this and have had a pretty incredible journey, and I was just eager to share that with her.”

      Perron is a geomorphologist who seeks to understand the mechanisms that shape landscapes on Earth and other planets. His work combines mathematical modeling and computer simulations of landscape evolution; analysis of remote-sensing and spacecraft data; and field studies in regions such as the Appalachian Mountains, Hawaii, and the Amazon rainforest to trace how landscapes evolved over time and how they may change in the future.

      “If we can understand how climate and life and geological processes have interacted over a long time to create the landscapes we see now, we can use that information to anticipate where the landscape is headed in the future,” Perron says.

      His group has developed models that describe how river systems generate intricate branching patterns as a result of competing erosional processes, and how climate influences erosion on continents, islands, and reefs.

      Perron has also applied his methods beyond Earth, to retrace the evolution of the surfaces of Mars and Saturn’s moon Titan. His group has used spacecraft images and data to show how features on Titan, which appear to be active river networks, were likely carved out by raining liquid methane. On Mars, his analyses have supported the idea that the Red Planet once harbored an ocean and that the former shoreline of this Martian ocean is now warped as a result of a shift in the planet’s spin axis.

      He is continuing to map out the details of Mars and Titan’s landscape histories, which he hopes will provide clues to their ancient climates and habitability.

      “I think answers to some of the big questions about the solar system are written in planetary landscapes,” Perron says. “For example, why did Mars start off with lakes and rivers, but end up as a frozen desert? And if a world like Titan has weather like ours, but with a methane cycle instead of a water cycle, could an environment like that have supported life? One thing we try to do is figure out how to read the landscape to find the answers to those questions.”

      Perron has expanded his group’s focus to examine how changing landscapes affect biodiversity, for instance in Appalachia and in the Amazon — both freshwater systems that host some of the most diverse populations of life on the planet.

      “If we can figure out how changes in the physical landscape may have generated regions of really high biodiversity, that should help us learn how to conserve it,” Perron says.

      Recently, his group has also begun to investigate the influence of landscape evolution on human history. Perron is collaborating with archaeologists on projects to study the effect of physical landscapes on human migration in the Americas, and how the response of rivers to ice ages may have helped humans develop complex farming societies in the Amazon.

      Looking ahead, he plans to apply the MacArthur grant toward these projects and other “intellectual risks” — ideas that have potential for failure but could be highly rewarding if they succeed. The fellowship will also provide resources for his group to continue collaborating across disciplines and continents.

      “I’ve learned a lot from reaching out to people in other fields — everything from granular mechanics to fish biology,” Perron says. “That has broadened my scientific horizons and helped us do innovative work. Having the fellowship will provide more flexibility to allow us to continue connecting with people from other fields and other parts of the world.”

      Perron holds a BA in earth and planetary sciences and archaeology from Harvard University and a PhD in earth and planetary science from the University of California at Berkeley. He joined MIT as a faculty member in 2009. More