School of Science

Subterms

    More stories

    • 75 Shares149 Views

      in Data Management & Statistics

      Can machine-learning models overcome biased datasets?

      by

      Artificial intelligence systems may be able to complete tasks quickly, but that doesn’t mean they always do so fairly. If the datasets used to train machine-learning models contain biased data, it is likely the system could exhibit that same bias when it makes decisions in practice.

      For instance, if a dataset contains mostly images of white men, then a facial-recognition model trained with these data may be less accurate for women or people with different skin tones.

      A group of researchers at MIT, in collaboration with researchers at Harvard University and Fujitsu Ltd., sought to understand when and how a machine-learning model is capable of overcoming this kind of dataset bias. They used an approach from neuroscience to study how training data affects whether an artificial neural network can learn to recognize objects it has not seen before. A neural network is a machine-learning model that mimics the human brain in the way it contains layers of interconnected nodes, or “neurons,” that process data.

      The new results show that diversity in training data has a major influence on whether a neural network is able to overcome bias, but at the same time dataset diversity can degrade the network’s performance. They also show that how a neural network is trained, and the specific types of neurons that emerge during the training process, can play a major role in whether it is able to overcome a biased dataset.

      “A neural network can overcome dataset bias, which is encouraging. But the main takeaway here is that we need to take into account data diversity. We need to stop thinking that if you just collect a ton of raw data, that is going to get you somewhere. We need to be very careful about how we design datasets in the first place,” says Xavier Boix, a research scientist in the Department of Brain and Cognitive Sciences (BCS) and the Center for Brains, Minds, and Machines (CBMM), and senior author of the paper.  

      Co-authors include former MIT graduate students Timothy Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, and Spandan Madan, a corresponding author who is currently pursuing a PhD at Harvard; Tomotake Sasaki, a former visiting scientist now a senior researcher at Fujitsu Research; Frédo Durand, a professor of electrical engineering and computer science at MIT and a member of the Computer Science and Artificial Intelligence Laboratory; and Hanspeter Pfister, the An Wang Professor of Computer Science at the Harvard School of Enginering and Applied Sciences. The research appears today in Nature Machine Intelligence.

      Thinking like a neuroscientist

      Boix and his colleagues approached the problem of dataset bias by thinking like neuroscientists. In neuroscience, Boix explains, it is common to use controlled datasets in experiments, meaning a dataset in which the researchers know as much as possible about the information it contains.

      The team built datasets that contained images of different objects in varied poses, and carefully controlled the combinations so some datasets had more diversity than others. In this case, a dataset had less diversity if it contains more images that show objects from only one viewpoint. A more diverse dataset had more images showing objects from multiple viewpoints. Each dataset contained the same number of images.

      The researchers used these carefully constructed datasets to train a neural network for image classification, and then studied how well it was able to identify objects from viewpoints the network did not see during training (known as an out-of-distribution combination). 

      For example, if researchers are training a model to classify cars in images, they want the model to learn what different cars look like. But if every Ford Thunderbird in the training dataset is shown from the front, when the trained model is given an image of a Ford Thunderbird shot from the side, it may misclassify it, even if it was trained on millions of car photos.

      The researchers found that if the dataset is more diverse — if more images show objects from different viewpoints — the network is better able to generalize to new images or viewpoints. Data diversity is key to overcoming bias, Boix says.

      “But it is not like more data diversity is always better; there is a tension here. When the neural network gets better at recognizing new things it hasn’t seen, then it will become harder for it to recognize things it has already seen,” he says.

      Testing training methods

      The researchers also studied methods for training the neural network.

      In machine learning, it is common to train a network to perform multiple tasks at the same time. The idea is that if a relationship exists between the tasks, the network will learn to perform each one better if it learns them together.

      But the researchers found the opposite to be true — a model trained separately for each task was able to overcome bias far better than a model trained for both tasks together.

      “The results were really striking. In fact, the first time we did this experiment, we thought it was a bug. It took us several weeks to realize it was a real result because it was so unexpected,” he says.

      They dove deeper inside the neural networks to understand why this occurs.

      They found that neuron specialization seems to play a major role. When the neural network is trained to recognize objects in images, it appears that two types of neurons emerge — one that specializes in recognizing the object category and another that specializes in recognizing the viewpoint.

      When the network is trained to perform tasks separately, those specialized neurons are more prominent, Boix explains. But if a network is trained to do both tasks simultaneously, some neurons become diluted and don’t specialize for one task. These unspecialized neurons are more likely to get confused, he says.

      “But the next question now is, how did these neurons get there? You train the neural network and they emerge from the learning process. No one told the network to include these types of neurons in its architecture. That is the fascinating thing,” he says.

      That is one area the researchers hope to explore with future work. They want to see if they can force a neural network to develop neurons with this specialization. They also want to apply their approach to more complex tasks, such as objects with complicated textures or varied illuminations.

      Boix is encouraged that a neural network can learn to overcome bias, and he is hopeful their work can inspire others to be more thoughtful about the datasets they are using in AI applications.

      This work was supported, in part, by the National Science Foundation, a Google Faculty Research Award, the Toyota Research Institute, the Center for Brains, Minds, and Machines, Fujitsu Research, and the MIT-Sensetime Alliance on Artificial Intelligence. More

    • 100 Shares99 Views

      in Data Management & Statistics

      Professor Emery Brown has big plans for anesthesiology

      by

      Emery N. Brown — the Edward Hood Taplin Professor of Medical Engineering and of Computational Neuroscience at MIT, an MIT professor of health sciences and technology, an investigator with The Picower Institute for Learning and Memory at MIT, and the Warren M. Zapol Professor of Anaesthesia at Harvard Medical School and Massachusetts General Hospital (MGH) — clearly excels at many roles. Renowned internationally for his anesthesia and neuroscience research, he embodies a unique blend of anesthesiologist, statistician, neuroscientist, educator, and mentor to both students and colleagues. Notably, Brown is one of the most decorated clinician-scientists in the country; he is one of only 25 people — and the first African-American, statistician, and anesthesiologist — to be elected to all three National Academies (Science, Engineering, and Medicine).

      Now, he is handing off one of his many key roles and responsibilities. After almost 10 years, Brown is stepping down as co-director of the Harvard-MIT Program in Health Sciences and Technology (HST). He will turn his energies toward working to develop a new joint center between MIT and MGH that uses the study of anesthesia to design novel approaches to controlling brain states. While a goal of the new center will be to improve anesthesia and intensive care unit management, according to Brown, it will also study related problems such as treating depression, insomnia, and epilepsy, as well as enhancing coma recovery.

      Founded in 1970, HST is one of the oldest interdisciplinary educational programs focused on training the next generation of clinician-scientists and engineers, who learn to translate science, engineering, and medical research into clinical practice, with the aim of improving human health. The MIT Institute for Medical Engineering and Science (IMES), where Brown is associate director, is HST’s home at MIT. Brown was the first HST co-director after the establishment of IMES in 2012; Wolfram Goessling is the Harvard University co-director of HST.

      “Emery has been an exemplary leader for HST during his tenure, and has helped it become a hub for the training of world-class scientists, engineers, and clinicians,” says Anantha Chandrakasan, dean of the MIT School of Engineering and the Vannevar Bush Professor of Electrical Engineering and Computer Science. “I am deeply grateful for his many years of service and wish him well as he moves on to new endeavors.”

      Elazer R. Edelman, director of IMES, calls Brown “a phenom who has been dedicated to our programs for years.”

      “With his thoughtful leadership and understated style, Emery made many contributions to the HST community,” Edelman continues. “On a personal note, this is bittersweet for me, as Emery has been a partner and mentor in my role as IMES director. And while I know that he will always be there for me, as he has been for all of us at IMES and HST, I will miss our late-night calls and midday conferences on matters of import for MIT, IMES, and HST.”

      Brown says “it was an honor and a privilege to co-direct HST with Wolfram.”

      “The students, staff, and faculty are simply amazing,” Brown continues. “Although, now more than 50 years old, HST remains at the vanguard for training PhD and MD students to work at the intersection between engineering, science, and medicine.”

      Goessling also thanks Brown for his leadership: “I truly valued Emery’s partnership and friendship, working together to deepen ties between the MIT and Harvard sides of HST. I am particularly grateful for working with Emery on our combined diversity efforts, leading to the HST Diversity Ambassadors initiative that made HST a better and stronger program.”

      According to Edelman, Brown was instrumental in the transition to new paradigms and relationships with HMS in the context of IMES. In 2014, he led the establishment of clear criteria for HST faculty membership, thereby strengthening the community of faculty experts who train students and provide research opportunities. More recently, he provided guidance through the turmoil of the ongoing Covid-19 pandemic, including the transition to online instruction and the return to the classroom. And Brown has always been a strong supporter of student diversity efforts, serving as an advocate and advisor to HST students.

      Brown holds BA, MA, and PhD degrees from Harvard University, and an MD from Harvard Medical School. He has been recognized with many awards, including the 2020 Swartz Prize in Theoretical and Computational Neuroscience, the 2018 Dickson Prize in Science, and an NIH Director’s Pioneer Award. Brown also served on President Barack Obama’s BRAIN Initiative Working Group. Among his many accomplishments, he has been cited for developing neural signal processing algorithms to characterize how neural systems represent and transmit information, and for unlocking the neurophysiology of how anesthetics produce the states of general anesthesia.

      Edelman says the process is underway to name a successor to Brown as co-director of HST at MIT. More

    • 88 Shares149 Views

      in Data Management & Statistics

      Probing how proteins pair up inside cells

      by

      Despite its minute size, a single cell contains billions of molecules that bustle around and bind to one another, carrying out vital functions. The human genome encodes about 20,000 proteins, most of which interact with partner proteins to mediate upwards of 400,000 distinct interactions. These partners don’t just latch onto one another haphazardly; they only bind to very specific companions that they must recognize inside the crowded cell. If they create the wrong pairings — or even the right pairings at the wrong place or wrong time — cancer or other diseases can ensue. Scientists are hard at work investigating these protein-protein relationships, in order to understand how they work, and potentially create drugs that disrupt or mimic them to treat disease.

      The average human protein is composed of approximately 400 building blocks called amino acids, which are strung together and folded into a complex 3D structure. Within this long string of building blocks, some proteins contain stretches of four to six amino acids called short linear motifs (SLiMs), which mediate protein-protein interactions. Despite their simplicity and small size, SLiMs and their binding partners facilitate key cellular processes. However, it’s been historically difficult to devise experiments to probe how SLiMs recognize their specific binding partners.

      To address this problem, a group led by Theresa Hwang PhD ’21 designed a screening method to understand how SLiMs selectively bind to certain proteins, and even distinguish between those with similar structures. Using the detailed information they gleaned from studying these interactions, the researchers created their own synthetic molecule capable of binding extremely tightly to a protein called ENAH, which is implicated in cancer metastasis. The team shared their findings in a pair of eLife studies, one published on Dec. 2, 2021, and the other published Jan. 25.

      “The ability to test hundreds of thousands of potential SLiMs for binding provides a powerful tool to explore why proteins prefer specific SLiM partners over others,” says Amy Keating, professor of biology and biological engineering and the senior author on both studies. “As we gain an understanding of the tricks that a protein uses to select its partners, we can apply these in protein design to make our own binders to modulate protein function for research or therapeutic purposes.”

      Most existing screens for SLiMs simply select for short, tight binders, while neglecting SLiMs that don’t grip their partner proteins quite as strongly. To survey SLiMs with a wide range of binding affinities, Keating, Hwang, and their colleagues developed their own screen called MassTitr.

      The researchers also suspected that the amino acids on either side of the SLiM’s core four-to-six amino acid sequence might play an underappreciated role in binding. To test their theory, they used MassTitr to screen the human proteome in longer chunks comprised of 36 amino acids, in order to see which “extended” SLiMs would associate with the protein ENAH.

      ENAH, sometimes referred to as Mena, helps cells to move. This ability to migrate is critical for healthy cells, but cancer cells can co-opt it to spread. Scientists have found that reducing the amount of ENAH decreases the cancer cell’s ability to invade other tissues — suggesting that formulating drugs to disrupt this protein and its interactions could treat cancer.

      Thanks to MassTitr, the team identified 33 SLiM-containing proteins that bound to ENAH — 19 of which are potentially novel binding partners. They also discovered three distinct patterns of amino acids flanking core SLiM sequences that helped the SLiMs bind even tighter to ENAH. Of these extended SLiMs, one found in a protein called PCARE bound to ENAH with the highest known affinity of any SLiM to date.

      Next, the researchers combined a computer program called dTERMen with X-ray crystallography in order understand how and why PCARE binds to ENAH over ENAH’s two nearly identical sister proteins (VASP and EVL). Hwang and her colleagues saw that the amino acids flanking PCARE’s core SliM caused ENAH to change shape slightly when the two made contact, allowing the binding sites to latch onto one another. VASP and EVL, by contrast, could not undergo this structural change, so the PCARE SliM did not bind to either of them as tightly.

      Inspired by this unique interaction, Hwang designed her own protein that bound to ENAH with unprecedented affinity and specificity. “It was exciting that we were able to come up with such a specific binder,” she says. “This work lays the foundation for designing synthetic molecules with the potential to disrupt protein-protein interactions that cause disease — or to help scientists learn more about ENAH and other SLiM-binding proteins.”  

      Ylva Ivarsson, a professor of biochemistry at Uppsala University who was not involved with the study, says that understanding how proteins find their binding partners is a question of fundamental importance to cell function and regulation. The two eLife studies, she explains, show that extended SLiMs play an underappreciated role in determining the affinity and specificity of these binding interactions.

      “The studies shed light on the idea that context matters, and provide a screening strategy for a variety of context-dependent binding interactions,” she says. “Hwang and co-authors have created valuable tools for dissecting the cellular function of proteins and their binding partners. Their approach could even inspire ENAH-specific inhibitors for therapeutic purposes.”

      Hwang’s biggest takeaway from the project is that things are not always as they seem: even short, simple protein segments can play complex roles in the cell. As she puts it: “We should really appreciate SLiMs more.” More

    • 113 Shares99 Views

      in Data Management & Statistics

      Professors Elchanan Mossel and Rosalind Picard named 2021 ACM Fellows

      by

      The Association for Computing Machinery (ACM) has named MIT professors Elchanan Mossel and Rosalind Picard as fellows for outstanding accomplishments in computing and information technology.

      The ACM Fellows program recognizes wide-ranging and fundamental contributions in areas including algorithms, computer science education, cryptography, data security and privacy, medical informatics, and mobile and networked systems, among many other areas. The accomplishments of the 2021 ACM Fellows underpin important innovations that shape the technologies we use every day.

      Elchanan Mossel

      Mossel is a professor of mathematics and a member at the Statistics and Data Science Center of the MIT Institute for Data, Systems and Society. His research in discrete functional inequalities, isoperimetry, and hypercontractivity led to the proof that Majority is Stablest and confirmed the optimality of the Goemans-Williamson MAX-CUT algorithm under the unique games conjecture from computational complexity. His work on the reconstruction problem on trees provides optimal algorithms and bounds for phylogenetic reconstruction in molecular biology and has led to sharp results in the analysis of Gibbs samplers from statistical physics and inference problems on graphs. His research has resolved open problems in computational biology, machine learning, social choice theory, and economics.Mossel received a BS from the Open University in Israel in 1992, and MS (1997) and PhD (2000) degrees in mathematics from the Hebrew University of Jerusalem. He was a postdoc at the Microsoft Research Theory Group and a Miller Fellow at University of California at Berkeley. He joined the UC Berkeley faculty in 2003 as a professor of statistics and computer science, and spent leaves as a professor at the Weizmann Institute and at the Wharton School before joining MIT in 2016 as a full professor.

      In 2020, he received the Vannevar Bush Faculty Fellowship of the U.S. Department of Defense. Other distinctions include being named a Simons Investigator in Mathematics in 2019, being selected as a fellow of the AMS, and receiving a Sloan Research Fellowship, NSF CAREER Award, and the Bergmann Memorial Award from the U.S.-Israel Binational Science Foundation.

      “I am honored by this award,” says Mossel. “It makes me realize how fortunate I’ve been, working with creative and generous colleagues, and mentoring brilliant young minds.”

      Rosalind Picard

      Picard is a scientist, engineer, author, and professor of media arts and sciences at the MIT Media Lab. She is recognized as the founder of the field of affective computing, and has carried this research forward as head of the Media Lab’s Affective Computing research group. She is also a founding faculty chair of MIT’s MindHandHeart Initiative, and a faculty member of the MIT Center for Neurobiological Engineering. Picard is an IEEE fellow, and a member of the National Academy of Engineering. 

      Picard’s inventions are in use by thousands of research teams worldwide as well as in numerous products and services. She has co-founded two companies: Affectiva (now part of Smart Eye), providing emotion AI technologies now used by more than 25 percent of the Global Fortune 500, and Empatica, providing wearable sensors and analytics to improve health. Starting from inventions by Picard and her team, Empatica created the first AI-based smart watch cleared by the FDA (in neurology for monitoring seizures), which is now helping to bring potentially lifesaving help for people with epilepsy. 

      “This award makes me think of how blessed I am to work with so many amazing people here at MIT, especially at the Media Lab,” Picard notes. “Whenever any one of us has our contributions recognized, it is also a recognition of how special a place this is.” More

    • 63 Shares99 Views

      in Data Management & Statistics

      Understanding air pollution from space

      by

      Climate change and air pollution are interlocking crises that threaten human health. Reducing emissions of some air pollutants can help achieve climate goals, and some climate mitigation efforts can in turn improve air quality.

      One part of MIT Professor Arlene Fiore’s research program is to investigate the fundamental science in understanding air pollutants — how long they persist and move through our environment to affect air quality.

      “We need to understand the conditions under which pollutants, such as ozone, form. How much ozone is formed locally and how much is transported long distances?” says Fiore, who notes that Asian air pollution can be transported across the Pacific Ocean to North America. “We need to think about processes spanning local to global dimensions.”

      Fiore, the Peter H. Stone and Paola Malanotte Stone Professor in Earth, Atmospheric and Planetary Sciences, analyzes data from on-the-ground readings and from satellites, along with models, to better understand the chemistry and behavior of air pollutants — which ultimately can inform mitigation strategies and policy setting.

      A global concern

      At the United Nations’ most recent climate change conference, COP26, air quality management was a topic discussed over two days of presentations.

      “Breathing is vital. It’s life. But for the vast majority of people on this planet right now, the air that they breathe is not giving life, but cutting it short,” said Sarah Vogel, senior vice president for health at the Environmental Defense Fund, at the COP26 session.

      “We need to confront this twin challenge now through both a climate and clean air lens, of targeting those pollutants that warm both the air and harm our health.”

      Earlier this year, the World Health Organization (WHO) updated its global air quality guidelines it had issued 15 years earlier for six key pollutants including ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). The new guidelines are more stringent based on what the WHO stated is the “quality and quantity of evidence” of how these pollutants affect human health. WHO estimates that roughly 7 million premature deaths are attributable to the joint effects of air pollution.

      “We’ve had all these health-motivated reductions of aerosol and ozone precursor emissions. What are the implications for the climate system, both locally but also around the globe? How does air quality respond to climate change? We study these two-way interactions between air pollution and the climate system,” says Fiore.

      But fundamental science is still required to understand how gases, such as ozone and nitrogen dioxide, linger and move throughout the troposphere — the lowermost layer of our atmosphere, containing the air we breathe.

      “We care about ozone in the air we’re breathing where we live at the Earth’s surface,” says Fiore. “Ozone reacts with biological tissue, and can be damaging to plants and human lungs. Even if you’re a healthy adult, if you’re out running hard during an ozone smog event, you might feel an extra weight on your lungs.”

      Telltale signs from space

      Ozone is not emitted directly, but instead forms through chemical reactions catalyzed by radiation from the sun interacting with nitrogen oxides — pollutants released in large part from burning fossil fuels—and volatile organic compounds. However, current satellite instruments cannot sense ground-level ozone.

      “We can’t retrieve surface- or even near-surface ozone from space,” says Fiore of the satellite data, “although the anticipated launch of a new instrument looks promising for new advances in retrieving lower-tropospheric ozone”. Instead, scientists can look at signatures from other gas emissions to get a sense of ozone formation. “Nitrogen dioxide and formaldehyde are a heavy focus of our research because they serve as proxies for two of the key ingredients that go on to form ozone in the atmosphere.”

      To understand ozone formation via these precursor pollutants, scientists have gathered data for more than two decades using spectrometer instruments aboard satellites that measure sunlight in ultraviolet and visible wavelengths that interact with these pollutants in the Earth’s atmosphere — known as solar backscatter radiation.

      Satellites, such as NASA’s Aura, carry instruments like the Ozone Monitoring Instrument (OMI). OMI, along with European-launched satellites such as the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY), and the newest generation TROPOspheric Monitoring instrument (TROPOMI), all orbit the Earth, collecting data during daylight hours when sunlight is interacting with the atmosphere over a particular location.

      In a recent paper from Fiore’s group, former graduate student Xiaomeng Jin (now a postdoc at the University of California at Berkeley), demonstrated that she could bring together and “beat down the noise in the data,” as Fiore says, to identify trends in ozone formation chemistry over several U.S. metropolitan areas that “are consistent with our on-the-ground understanding from in situ ozone measurements.”

      “This finding implies that we can use these records to learn about changes in surface ozone chemistry in places where we lack on-the-ground monitoring,” says Fiore. Extracting these signals by stringing together satellite data — OMI, GOME, and SCIAMACHY — to produce a two-decade record required reconciling the instruments’ differing orbit days, times, and fields of view on the ground, or spatial resolutions. 

      Currently, spectrometer instruments aboard satellites are retrieving data once per day. However, newer instruments, such as the Geostationary Environment Monitoring Spectrometer launched in February 2020 by the National Institute of Environmental Research in the Ministry of Environment of South Korea, will monitor a particular region continuously, providing much more data in real time.

      Over North America, the Tropospheric Emissions: Monitoring of Pollution Search (TEMPO) collaboration between NASA and the Smithsonian Astrophysical Observatory, led by Kelly Chance of Harvard University, will provide not only a stationary view of the atmospheric chemistry over the continent, but also a finer-resolution view — with the instrument recording pollution data from only a few square miles per pixel (with an anticipated launch in 2022).

      “What we’re very excited about is the opportunity to have continuous coverage where we get hourly measurements that allow us to follow pollution from morning rush hour through the course of the day and see how plumes of pollution are evolving in real time,” says Fiore.

      Data for the people

      Providing Earth-observing data to people in addition to scientists — namely environmental managers, city planners, and other government officials — is the goal for the NASA Health and Air Quality Applied Sciences Team (HAQAST).

      Since 2016, Fiore has been part of HAQAST, including collaborative “tiger teams” — projects that bring together scientists, nongovernment entities, and government officials — to bring data to bear on real issues.

      For example, in 2017, Fiore led a tiger team that provided guidance to state air management agencies on how satellite data can be incorporated into state implementation plans (SIPs). “Submission of a SIP is required for any state with a region in non-attainment of U.S. National Ambient Air Quality Standards to demonstrate their approach to achieving compliance with the standard,” says Fiore. “What we found is that small tweaks in, for example, the metrics we use to convey the science findings, can go a long way to making the science more usable, especially when there are detailed policy frameworks in place that must be followed.”

      Now, in 2021, Fiore is part of two tiger teams announced by HAQAST in late September. One team is looking at data to address environmental justice issues, by providing data to assess communities disproportionately affected by environmental health risks. Such information can be used to estimate the benefits of governmental investments in environmental improvements for disproportionately burdened communities. The other team is looking at urban emissions of nitrogen oxides to try to better quantify and communicate uncertainties in the estimates of anthropogenic sources of pollution.

      “For our HAQAST work, we’re looking at not just the estimate of the exposure to air pollutants, or in other words their concentrations,” says Fiore, “but how confident are we in our exposure estimates, which in turn affect our understanding of the public health burden due to exposure. We have stakeholder partners at the New York Department of Health who will pair exposure datasets with health data to help prioritize decisions around public health.

      “I enjoy working with stakeholders who have questions that require science to answer and can make a difference in their decisions.” Fiore says. More

    • 125 Shares199 Views

      in Data Management & Statistics

      At UN climate change conference, trying to “keep 1.5 alive”

      by

      After a one-year delay caused by the Covid-19 pandemic, negotiators from nearly 200 countries met this month in Glasgow, Scotland, at COP26, the United Nations climate change conference, to hammer out a new global agreement to reduce greenhouse gas emissions and prepare for climate impacts. A delegation of approximately 20 faculty, staff, and students from MIT was on hand to observe the negotiations, share and conduct research, and launch new initiatives.

      On Saturday, Nov. 13, following two weeks of negotiations in the cavernous Scottish Events Campus, countries’ representatives agreed to the Glasgow Climate Pact. The pact reaffirms the goal of the 2015 Paris Agreement “to pursue efforts” to limit the global average temperature increase to 1.5 degrees Celsius above preindustrial levels, and recognizes that achieving this goal requires “reducing global carbon dioxide emissions by 45 percent by 2030 relative to the 2010 level and to net zero around mid-century.”

      “On issues like the need to reach net-zero emissions, reduce methane pollution, move beyond coal power, and tighten carbon accounting rules, the Glasgow pact represents some meaningful progress, but we still have so much work to do,” says Maria Zuber, MIT’s vice president for research, who led the Institute’s delegation to COP26. “Glasgow showed, once again, what a wicked complex problem climate change is, technically, economically, and politically. But it also underscored the determination of a global community of people committed to addressing it.”

      An “ambition gap”

      Both within the conference venue and at protests that spilled through the streets of Glasgow, one rallying cry was “keep 1.5 alive.” Alok Sharma, who was appointed by the UK government to preside over COP26, said in announcing the Glasgow pact: “We can now say with credibility that we have kept 1.5 degrees alive. But, its pulse is weak and it will only survive if we keep our promises and translate commitments into rapid action.”

      In remarks delivered during the first week of the conference, Sergey Paltsev, deputy director of MIT’s Joint Program on the Science and Policy of Global Change, presented findings from the latest MIT Global Change Outlook, which showed a wide gap between countries’ nationally determined contributions (NDCs) — the UN’s term for greenhouse gas emissions reduction pledges — and the reductions needed to put the world on track to meet the goals of the Paris Agreement and, now, the Glasgow pact.

      Pointing to this ambition gap, Paltsev called on all countries to do more, faster, to cut emissions. “We could dramatically reduce overall climate risk through more ambitious policy measures and investments,” says Paltsev. “We need to employ an integrated approach of moving to zero emissions in energy and industry, together with sustainable development and nature-based solutions, simultaneously improving human well-being and providing biodiversity benefits.”

      Finalizing the Paris rulebook

      A key outcome of COP26 (COP stands for “conference of the parties” to the UN Framework Convention on Climate Change, held for the 26th time) was the development of a set of rules to implement Article 6 of the Paris Agreement, which provides a mechanism for countries to receive credit for emissions reductions that they finance outside their borders, and to cooperate by buying and selling emissions reductions on international carbon markets.

      An agreement on this part of the Paris “rulebook” had eluded negotiators in the years since the Paris climate conference, in part because negotiators were concerned about how to prevent double-counting, wherein both buyers and sellers would claim credit for the emissions reductions.

      Michael Mehling, the deputy director of MIT’s Center for Energy and Environmental Policy Research (CEEPR) and an expert on international carbon markets, drew on a recent CEEPR working paper to describe critical negotiation issues under Article 6 during an event at the conference on Nov. 10 with climate negotiators and private sector representatives.

      He cited research that finds that Article 6, by leveraging the cost-efficiency of global carbon markets, could cut in half the cost that countries would incur to achieve their nationally determined contributions. “Which, seen from another angle, means you could double the ambition of these NDCs at no additional cost,” Mehling noted in his talk, adding that, given the persistent ambition gap, “any such opportunity is bitterly needed.”

      Andreas Haupt, a graduate student in the Institute for Data, Systems, and Society, joined MIT’s COP26 delegation to follow Article 6 negotiations. Haupt described the final days of negotiations over Article 6 as a “roller coaster.” Once negotiators reached an agreement, he says, “I felt relieved, but also unsure how strong of an effect the new rules, with all their weaknesses, will have. I am curious and hopeful regarding what will happen in the next year until the next large-scale negotiations in 2022.”

      Nature-based climate solutions

      World leaders also announced new agreements on the sidelines of the formal UN negotiations. One such agreement, a declaration on forests signed by more than 100 countries, commits to “working collectively to halt and reverse forest loss and land degradation by 2030.”

      A team from MIT’s Environmental Solutions Initiative (ESI), which has been working with policymakers and other stakeholders on strategies to protect tropical forests and advance other nature-based climate solutions in Latin America, was at COP26 to discuss their work and make plans for expanding it.

      Marcela Angel, a research associate at ESI, moderated a panel discussion featuring John Fernández, professor of architecture and ESI’s director, focused on protecting and enhancing natural carbon sinks, particularly tropical forests such as the Amazon that are at risk of deforestation, forest degradation, and biodiversity loss.

      “Deforestation and associated land use change remain one of the main sources of greenhouse gas emissions in most Amazonian countries, such as Brazil, Peru, and Colombia,” says Angel. “Our aim is to support these countries, whose nationally determined contributions depend on the effectiveness of policies to prevent deforestation and promote conservation, with an approach based on the integration of targeted technology breakthroughs, deep community engagement, and innovative bioeconomic opportunities for local communities that depend on forests for their livelihoods.”

      Energy access and renewable energy

      Worldwide, an estimated 800 million people lack access to electricity, and billions more have only limited or erratic electrical service. Providing universal access to energy is one of the UN’s sustainable development goals, creating a dual challenge: how to boost energy access without driving up greenhouse gas emissions.

      Rob Stoner, deputy director for science and technology of the MIT Energy Initiative (MITEI), and Ignacio Pérez-Arriaga, a visiting professor at the Sloan School of Management, attended COP26 to share their work as members of the Global Commission to End Energy Poverty, a collaboration between MITEI and the Rockefeller Foundation. It brings together global energy leaders from industry, the development finance community, academia, and civil society to identify ways to overcome barriers to investment in the energy sectors of countries with low energy access.

      The commission’s work helped to motivate the formation, announced at COP26 on Nov. 2, of the Global Energy Alliance for People and Planet, a multibillion-dollar commitment by the Rockefeller and IKEA foundations and Bezos Earth Fund to support access to renewable energy around the world.

      Another MITEI member of the COP26 delegation, Martha Broad, the initiative’s executive director, spoke about MIT research to inform the U.S. goal of scaling offshore wind energy capacity from approximately 30 megawatts today to 30 gigawatts by 2030, including significant new capacity off the coast of New England.

      Broad described research, funded by MITEI member companies, on a coating that can be applied to the blades of wind turbines to prevent icing that would require the turbines’ shutdown; the use of machine learning to inform preventative turbine maintenance; and methodologies for incorporating the effects of climate change into projections of future wind conditions to guide wind farm siting decisions today. She also spoke broadly about the need for public and private support to scale promising innovations.

      “Clearly, both the public sector and the private sector have a role to play in getting these technologies to the point where we can use them in New England, and also where we can deploy them affordably for the developing world,” Broad said at an event sponsored by America Is All In, a coalition of nonprofit and business organizations.

      Food and climate alliance

      Food systems around the world are increasingly at risk from the impacts of climate change. At the same time, these systems, which include all activities from food production to consumption and food waste, are responsible for about one-third of the human-caused greenhouse gas emissions warming the planet.

      At COP26, MIT’s Abdul Latif Jameel Water and Food Systems Lab announced the launch of a new alliance to drive research-based innovation that will make food systems more resilient and sustainable, called the Food and Climate Systems Transformation (FACT) Alliance. With 16 member institutions, the FACT Alliance will better connect researchers to farmers, food businesses, policymakers, and other food systems stakeholders around the world.

      Looking ahead

      By the end of 2022, the Glasgow pact asks countries to revisit their nationally determined contributions and strengthen them to bring them in line with the temperature goals of the Paris Agreement. The pact also “notes with deep regret” the failure of wealthier countries to collectively provide poorer countries $100 billion per year in climate financing that they pledged in 2009 to begin in 2020.

      These and other issues will be on the agenda for COP27, to be held in Sharm El-Sheikh, Egypt, next year.

      “Limiting warming to 1.5 degrees is broadly accepted as a critical goal to avoiding worsening climate consequences, but it’s clear that current national commitments will not get us there,” says ESI’s Fernández. “We will need stronger emissions reductions pledges, especially from the largest greenhouse gas emitters. At the same time, expanding creativity, innovation, and determination from every sector of society, including research universities, to get on with real-world solutions is essential. At Glasgow, MIT was front and center in energy systems, cities, nature-based solutions, and more. The year 2030 is right around the corner so we can’t afford to let up for one minute.” More

    • 88 Shares119 Views

      in Data Management & Statistics

      Studying learner engagement during the Covid-19 pandemic

      by

      While massive open online classes (MOOCs) have been a significant trend in higher education for many years now, they have gained a new level of attention during the Covid-19 pandemic. Open online courses became a critical resource for a wide audience of new learners during the first stages of the pandemic — including students whose academic programs had shifted online, teachers seeking online resources, and individuals suddenly facing lockdown or unemployment and looking to build new skills.

      Mary Ellen Wiltrout, director of online and blended learning initiatives and lecturer in digital learning in the Department of Biology, and Virginia “Katie” Blackwell, currently an MIT PhD student in biology, published a paper this summer in the European MOOC Stakeholder Summit (EMOOCs 2021) conference proceedings evaluating data for the online course 7.00x (Introduction to Biology). Their research objective was to better understand whether the shift to online learning that occurred during the pandemic led to increased learner engagement in the course.Blackwell participated in this research as part of the Bernard S. and Sophie G. Gould MIT Summer Research Program (MSRP) in Biology, during the uniquely remote MSRPx-Biology 2020 student cohort. She collaborated on the project while working toward her bachelor’s degree in biochemistry and molecular biology from the University of Texas at Dallas, and collaborated on the research while in Texas. She has since applied and been accepted into MIT’s PhD program in biology.

      “MSRP Biology was a transformative experience for me. I learned a lot about the nature of research and the MIT community in a very short period of time and loved every second of the program. Without MSRP, I would never have even considered applying to MIT for my PhD. After MSRP and working with Mary Ellen, MIT biology became my first-choice program and I felt like I had a shot at getting in,” says Blackwell.

      Play video

      Many MOOC platforms experienced increased website traffic in 2020, with 30 new MOOC-based degrees and more than 60 million new learners.

      “We find that the tremendous, lifelong learning opportunities that MOOCs provide are even more important and sought-after when traditional education is disrupted. During the pandemic, people had to be at home more often, and some faced unemployment requiring a career transition,” says Wiltrout.

      Wiltrout and Blackwell wanted to build a deeper understanding of learner profiles rather than looking exclusively at enrollments. They looked at all available data, including: enrollment demographics (i.e., country and “.edu” participants); proportion of learners engaged with videos, problems, and forums; number of individual engagement events with videos, problems, and forums; verification and performance; and the course “track” level — including auditing (for free) and verified (paying and receiving access to additional course content, including access to a comprehensive competency exam). They analyzed data in these areas from five runs of 7.00x in this study, including three pre-pandemic runs of April, July, and November 2019 and two pandemic runs of March and July 2020. 

      The March 2020 run had the same count of verified-track participants as all three pre-pandemic runs combined. The July 2020 run enrolled nearly as many verified-track participants as the March 2020 run. Wiltrout says that introductory biology content may have attracted great attention during the early days and months of the Covid-19 pandemic, as people may have had a new (or renewed) interest in learning about (or reviewing) viruses, RNA, the inner workings of cells, and more.

      Wiltrout and Blackwell found that the enrollment count for the March 2020 run of the course increased at almost triple the rate of the three pre-pandemic runs. During the early days of March 2020, the enrollment metrics appeared similar to enrollment metrics for the April 2019 run — both in rate and count — but the enrollment rate increased sharply around March 15, 2020. The July 2020 run began with more than twice as many learners already enrolled by the first day of the course, but continued with half the enrollment rate of the March 2020 course. In terms of learner demographics, during the pandemic, there was a higher proportion of learners with .edu addresses, indicating that MOOCs were often used by students enrolled in other schools. 

      Viewings of course videos increased at the beginning of the pandemic. During the March 2020 run, both verified-track and certified participants viewed far more unique videos during March 2020 than in the pre-pandemic runs of the course; even auditor-track learners — not aiming for certification — still viewed all videos offered. During the July 2020 run, however, both verified-track and certified participants viewed far fewer unique videos than during all prior runs. The proportion of participants who viewed at least one video decreased in the July 2020 run to 53 percent, from a mean of 64 percent in prior runs. Blackwell and Wiltrout say that this decrease — as well as the overall dip in participation in July 2020 — might be attributed to shifting circumstances for learners that allowed for less time to watch videos and participate in the course, as well as some fatigue from the extra screen time.

      The study found that 4.4 percent of March 2020 participants and 4.5 percent of July 2020 participants engaged through forum posting — which was 1.4 to 3.3 times higher than pre-pandemic proportions of forum posting. The increase in forum engagement may point to a desire for community engagement during a time when many were isolated and sheltering in place.

      “Through the day-to-day work of my research team and also through the engagement of the learners in 7.00x, we can see that there is great potential for meaningful connections in remote experiences,” says Wiltrout. “An increase in participation for an online course may not always remain at the same high level, in the long term, but overall, we’re continuing to see an increase in the number of MOOCs and other online programs offered by all universities and institutions, as well as an increase in online learners.” More

    • 188 Shares149 Views

      in Data Management & Statistics

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

      by

      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