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      Computing our climate future

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      On Monday, MIT announced five multiyear flagship projects in the first-ever Climate Grand Challenges, a new initiative to tackle complex climate problems and deliver breakthrough solutions to the world as quickly as possible. This article is the first in a five-part series highlighting the most promising concepts to emerge from the competition, and the interdisciplinary research teams behind them.

      With improvements to computer processing power and an increased understanding of the physical equations governing the Earth’s climate, scientists are continually working to refine climate models and improve their predictive power. But the tools they’re refining were originally conceived decades ago with only scientists in mind. When it comes to developing tangible climate action plans, these models remain inscrutable to the policymakers, public safety officials, civil engineers, and community organizers who need their predictive insight most.

      “What you end up having is a gap between what’s typically used in practice, and the real cutting-edge science,” says Noelle Selin, a professor in the Institute for Data, Systems and Society and the Department of Earth, Atmospheric and Planetary Sciences (EAPS), and co-lead with Professor Raffaele Ferrari on the MIT Climate Grand Challenges flagship project “Bringing Computation to the Climate Crisis.” “How can we use new computational techniques, new understandings, new ways of thinking about modeling, to really bridge that gap between state-of-the-art scientific advances and modeling, and people who are actually needing to use these models?”

      Using this as a driving question, the team won’t just be trying to refine current climate models, they’re building a new one from the ground up.

      This kind of game-changing advancement is exactly what the MIT Climate Grand Challenges is looking for, which is why the proposal has been named one of the five flagship projects in the ambitious Institute-wide program aimed at tackling the climate crisis. The proposal, which was selected from 100 submissions and was among 27 finalists, will receive additional funding and support to further their goal of reimagining the climate modeling system. It also brings together contributors from across the Institute, including the MIT Schwarzman College of Computing, the School of Engineering, and the Sloan School of Management.

      When it comes to pursuing high-impact climate solutions that communities around the world can use, “it’s great to do it at MIT,” says Ferrari, EAPS Cecil and Ida Green Professor of Oceanography. “You’re not going to find many places in the world where you have the cutting-edge climate science, the cutting-edge computer science, and the cutting-edge policy science experts that we need to work together.”

      The climate model of the future

      The proposal builds on work that Ferrari began three years ago as part of a joint project with Caltech, the Naval Postgraduate School, and NASA’s Jet Propulsion Lab. Called the Climate Modeling Alliance (CliMA), the consortium of scientists, engineers, and applied mathematicians is constructing a climate model capable of more accurately projecting future changes in critical variables, such as clouds in the atmosphere and turbulence in the ocean, with uncertainties at least half the size of those in existing models.

      To do this, however, requires a new approach. For one thing, current models are too coarse in resolution — at the 100-to-200-kilometer scale — to resolve small-scale processes like cloud cover, rainfall, and sea ice extent. But also, explains Ferrari, part of this limitation in resolution is due to the fundamental architecture of the models themselves. The languages most global climate models are coded in were first created back in the 1960s and ’70s, largely by scientists for scientists. Since then, advances in computing driven by the corporate world and computer gaming have given rise to dynamic new computer languages, powerful graphics processing units, and machine learning.

      For climate models to take full advantage of these advancements, there’s only one option: starting over with a modern, more flexible language. Written in Julia, a part of Julialab’s Scientific Machine Learning technology, and spearheaded by Alan Edelman, a professor of applied mathematics in MIT’s Department of Mathematics, CliMA will be able to harness far more data than the current models can handle.

      “It’s been real fun finally working with people in computer science here at MIT,” Ferrari says. “Before it was impossible, because traditional climate models are in a language their students can’t even read.”

      The result is what’s being called the “Earth digital twin,” a climate model that can simulate global conditions on a large scale. This on its own is an impressive feat, but the team wants to take this a step further with their proposal.

      “We want to take this large-scale model and create what we call an ‘emulator’ that is only predicting a set of variables of interest, but it’s been trained on the large-scale model,” Ferrari explains. Emulators are not new technology, but what is new is that these emulators, being referred to as the “Earth digital cousins,” will take advantage of machine learning.

      “Now we know how to train a model if we have enough data to train them on,” says Ferrari. Machine learning for projects like this has only become possible in recent years as more observational data become available, along with improved computer processing power. The goal is to create smaller, more localized models by training them using the Earth digital twin. Doing so will save time and money, which is key if the digital cousins are going to be usable for stakeholders, like local governments and private-sector developers.

      Adaptable predictions for average stakeholders

      When it comes to setting climate-informed policy, stakeholders need to understand the probability of an outcome within their own regions — in the same way that you would prepare for a hike differently if there’s a 10 percent chance of rain versus a 90 percent chance. The smaller Earth digital cousin models will be able to do things the larger model can’t do, like simulate local regions in real time and provide a wider range of probabilistic scenarios.

      “Right now, if you wanted to use output from a global climate model, you usually would have to use output that’s designed for general use,” says Selin, who is also the director of the MIT Technology and Policy Program. With the project, the team can take end-user needs into account from the very beginning while also incorporating their feedback and suggestions into the models, helping to “democratize the idea of running these climate models,” as she puts it. Doing so means building an interactive interface that eventually will give users the ability to change input values and run the new simulations in real time. The team hopes that, eventually, the Earth digital cousins could run on something as ubiquitous as a smartphone, although developments like that are currently beyond the scope of the project.

      The next thing the team will work on is building connections with stakeholders. Through participation of other MIT groups, such as the Joint Program on the Science and Policy of Global Change and the Climate and Sustainability Consortium, they hope to work closely with policymakers, public safety officials, and urban planners to give them predictive tools tailored to their needs that can provide actionable outputs important for planning. Faced with rising sea levels, for example, coastal cities could better visualize the threat and make informed decisions about infrastructure development and disaster preparedness; communities in drought-prone regions could develop long-term civil planning with an emphasis on water conservation and wildfire resistance.

      “We want to make the modeling and analysis process faster so people can get more direct and useful feedback for near-term decisions,” she says.

      The final piece of the challenge is to incentivize students now so that they can join the project and make a difference. Ferrari has already had luck garnering student interest after co-teaching a class with Edelman and seeing the enthusiasm students have about computer science and climate solutions.

      “We’re intending in this project to build a climate model of the future,” says Selin. “So it seems really appropriate that we would also train the builders of that climate model.” More

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      MIT ReACT welcomes first Afghan cohort to its largest-yet certificate program

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      Through the championing support of the faculty and leadership of the MIT Afghan Working Group convened last September by Provost Martin Schmidt and chaired by Associate Provost for International Activities Richard Lester, MIT has come together to support displaced Afghan learners and scholars in a time of crisis. The MIT Refugee Action Hub (ReACT) has opened opportunities for 25 talented Afghan learners to participate in the hub’s certificate program in computer and data science (CDS), now in its fourth year, welcoming its largest and most diverse cohort to date — 136 learners from 29 countries.

      ”Even in the face of extreme disruption, education and scholarship must continue, and MIT is committed to providing resources and safe forums for displaced scholars,” says Lester. “We greatly appreciate MIT ReACT’s work to create learning opportunities for Afghan students whose lives have been upended by the crisis in their homeland.”

      Currently, more than 3.5 million Afghans are internally displaced, while 2.5 million are registered refugees residing in other parts of the world. With millions in Afghanistan facing famine, poverty, and civil unrest in what has become the world’s largest humanitarian crisis, the United Nations predicts the number of Afghans forced to flee their homes will continue to rise. 

      “Forced displacement is on the rise, fueled not only by constant political, economical, and social turmoil worldwide, but also by the ongoing climate change crisis, which threatens costly disruptions to society and has potential to create unprecedented displacement internationally,” says associate professor of civil and environmental engineering and ReACT’s faculty founder Admir Masic. During the orientation for the new CDS cohort in January, Masic emphasized the great need for educational programs like ReACT’s that address the specific challenges refugees and displaced learners face.

      A former Bosnian refugee, Masic spent his teenage years in Croatia, where educational opportunities were limited for young people with refugee status. His experience motivated him to found ReACT, which launched in 2017. Housed within Open Learning, ReACT is an MIT-wide effort to deliver global education and professional development programs to underserved communities, including refugees and migrants. ReACT’s signature program, CDS is a year-long, online program that combines MITx courses in programming and data science, personal and professional development workshops including MIT Bootcamps, and opportunities for practical experience.

      ReACT’s group of 25 learners from Afghanistan, 52 percent of whom are women, joins the larger CDS cohort in the program. They will receive support from their new colleagues as well as members of ReACT’s mentor and alumni network. While the majority of the group are residing around the world, including in Europe, North America, and neighboring countries, several still remain in Afghanistan. With the support of the Afghan Working Group, ReACT is working to connect with communities from the region to provide safe and inclusive learning environments for the cohort. ​​

      Building community and confidence

      Selected from more than 1,000 applicants, the new CDS cohort reflected on their personal and professional goals during a weeklong orientation.

      “I am here because I want to change my career and learn basics in this field to then obtain networks that I wouldn’t have got if it weren’t for this program,” said Samiullah Ajmal, who is joining the program from Afghanistan.

      Interactive workshops on topics such as leadership development and virtual networking rounded out the week’s events. Members of ReACT’s greater community — which has grown in recent years to include a network of external collaborators including nonprofits, philanthropic supporters, universities, and alumni — helped facilitate these workshops and other orientation activities.

      For instance, Na’amal, a social enterprise that connects refugees to remote work opportunities, introduced the CDS learners to strategies for making career connections remotely. “We build confidence while doing,” says Susan Mulholland, a leadership and development coach with Na’amal who led the networking workshop.

      Along with the CDS program’s cohort-based model, ReACT also uses platforms that encourage regular communication between participants and with the larger ReACT network — making connections a critical component of the program.

      “I not only want to meet new people and make connections for my professional career, but I also want to test my communication and social skills,” says Pablo Andrés Uribe, a learner who lives in Colombia, describing ReACT’s emphasis on community-building. 

      Over the last two years, ReACT has expanded its geographic presence, growing from a hub in Jordan into a robust global community of many hubs, including in Colombia and Uganda. These regional sites connect talented refugees and displaced learners to internships and employment, startup networks and accelerators, and pathways to formal undergraduate and graduate education.

      This expansion is thanks to the generous support internally from the MIT Office of the Provost and Associate Provost Richard Lester and external organizations including the Western Union Foundation. ReACT will build new hubs this year in Greece, Uruguay, and Afghanistan, as a result of gifts from the Hatsopoulos family and the Pfeffer family.

      Holding space to learn from each other

      In addition to establishing new global hubs, ReACT plans to expand its network of internship and experiential learning opportunities, increasing outreach to new collaborators such as nongovernmental organizations (NGOs), companies, and universities. Jointly with Na’amal and Paper Airplanes, a nonprofit that connects conflict-affected individuals with personal language tutors, ReACT will host the first Migration Summit. Scheduled for April 2022, the month-long global convening invites a broad range of participants, including displaced learners, universities, companies, nonprofits and NGOs, social enterprises, foundations, philanthropists, researchers, policymakers, employers, and governments, to address the key challenges and opportunities for refugee and migrant communities. The theme of the summit is “Education and Workforce Development in Displacement.”

      “The MIT Migration Summit offers a platform to discuss how new educational models, such as those employed in ReACT, can help solve emerging challenges in providing quality education and career opportunities to forcibly displaced and marginalized people around the world,” says Masic. 

      A key goal of the convening is to center the voices of those most directly impacted by displacement, such as ReACT’s learners from Afghanistan and elsewhere, in solution-making. More

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      Exact symbolic artificial intelligence for faster, better assessment of AI fairness

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      The justice system, banks, and private companies use algorithms to make decisions that have profound impacts on people’s lives. Unfortunately, those algorithms are sometimes biased — disproportionately impacting people of color as well as individuals in lower income classes when they apply for loans or jobs, or even when courts decide what bail should be set while a person awaits trial.

      MIT researchers have developed a new artificial intelligence programming language that can assess the fairness of algorithms more exactly, and more quickly, than available alternatives.

      Their Sum-Product Probabilistic Language (SPPL) is a probabilistic programming system. Probabilistic programming is an emerging field at the intersection of programming languages and artificial intelligence that aims to make AI systems much easier to develop, with early successes in computer vision, common-sense data cleaning, and automated data modeling. Probabilistic programming languages make it much easier for programmers to define probabilistic models and carry out probabilistic inference — that is, work backward to infer probable explanations for observed data.

      “There are previous systems that can solve various fairness questions. Our system is not the first; but because our system is specialized and optimized for a certain class of models, it can deliver solutions thousands of times faster,” says Feras Saad, a PhD student in electrical engineering and computer science (EECS) and first author on a recent paper describing the work. Saad adds that the speedups are not insignificant: The system can be up to 3,000 times faster than previous approaches.

      SPPL gives fast, exact solutions to probabilistic inference questions such as “How likely is the model to recommend a loan to someone over age 40?” or “Generate 1,000 synthetic loan applicants, all under age 30, whose loans will be approved.” These inference results are based on SPPL programs that encode probabilistic models of what kinds of applicants are likely, a priori, and also how to classify them. Fairness questions that SPPL can answer include “Is there a difference between the probability of recommending a loan to an immigrant and nonimmigrant applicant with the same socioeconomic status?” or “What’s the probability of a hire, given that the candidate is qualified for the job and from an underrepresented group?”

      SPPL is different from most probabilistic programming languages, as SPPL only allows users to write probabilistic programs for which it can automatically deliver exact probabilistic inference results. SPPL also makes it possible for users to check how fast inference will be, and therefore avoid writing slow programs. In contrast, other probabilistic programming languages such as Gen and Pyro allow users to write down probabilistic programs where the only known ways to do inference are approximate — that is, the results include errors whose nature and magnitude can be hard to characterize.

      Error from approximate probabilistic inference is tolerable in many AI applications. But it is undesirable to have inference errors corrupting results in socially impactful applications of AI, such as automated decision-making, and especially in fairness analysis.

      Jean-Baptiste Tristan, associate professor at Boston College and former research scientist at Oracle Labs, who was not involved in the new research, says, “I’ve worked on fairness analysis in academia and in real-world, large-scale industry settings. SPPL offers improved flexibility and trustworthiness over other PPLs on this challenging and important class of problems due to the expressiveness of the language, its precise and simple semantics, and the speed and soundness of the exact symbolic inference engine.”

      SPPL avoids errors by restricting to a carefully designed class of models that still includes a broad class of AI algorithms, including the decision tree classifiers that are widely used for algorithmic decision-making. SPPL works by compiling probabilistic programs into a specialized data structure called a “sum-product expression.” SPPL further builds on the emerging theme of using probabilistic circuits as a representation that enables efficient probabilistic inference. This approach extends prior work on sum-product networks to models and queries expressed via a probabilistic programming language. However, Saad notes that this approach comes with limitations: “SPPL is substantially faster for analyzing the fairness of a decision tree, for example, but it can’t analyze models like neural networks. Other systems can analyze both neural networks and decision trees, but they tend to be slower and give inexact answers.”

      “SPPL shows that exact probabilistic inference is practical, not just theoretically possible, for a broad class of probabilistic programs,” says Vikash Mansinghka, an MIT principal research scientist and senior author on the paper. “In my lab, we’ve seen symbolic inference driving speed and accuracy improvements in other inference tasks that we previously approached via approximate Monte Carlo and deep learning algorithms. We’ve also been applying SPPL to probabilistic programs learned from real-world databases, to quantify the probability of rare events, generate synthetic proxy data given constraints, and automatically screen data for probable anomalies.”

      The new SPPL probabilistic programming language was presented in June at the ACM SIGPLAN International Conference on Programming Language Design and Implementation (PLDI), in a paper that Saad co-authored with MIT EECS Professor Martin Rinard and Mansinghka. SPPL is implemented in Python and is available open source. More