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    Neurodegenerative disease can progress in newly identified patterns

    Neurodegenerative diseases — like amyotrophic lateral sclerosis (ALS, or Lou Gehrig’s disease), Alzheimer’s, and Parkinson’s — are complicated, chronic ailments that can present with a variety of symptoms, worsen at different rates, and have many underlying genetic and environmental causes, some of which are unknown. ALS, in particular, affects voluntary muscle movement and is always fatal, but while most people survive for only a few years after diagnosis, others live with the disease for decades. Manifestations of ALS can also vary significantly; often slower disease development correlates with onset in the limbs and affecting fine motor skills, while the more serious, bulbar ALS impacts swallowing, speaking, breathing, and mobility. Therefore, understanding the progression of diseases like ALS is critical to enrollment in clinical trials, analysis of potential interventions, and discovery of root causes.

    However, assessing disease evolution is far from straightforward. Current clinical studies typically assume that health declines on a downward linear trajectory on a symptom rating scale, and use these linear models to evaluate whether drugs are slowing disease progression. However, data indicate that ALS often follows nonlinear trajectories, with periods where symptoms are stable alternating with periods when they are rapidly changing. Since data can be sparse, and health assessments often rely on subjective rating metrics measured at uneven time intervals, comparisons across patient populations are difficult. These heterogenous data and progression, in turn, complicate analyses of invention effectiveness and potentially mask disease origin.

    Now, a new machine-learning method developed by researchers from MIT, IBM Research, and elsewhere aims to better characterize ALS disease progression patterns to inform clinical trial design.

    “There are groups of individuals that share progression patterns. For example, some seem to have really fast-progressing ALS and others that have slow-progressing ALS that varies over time,” says Divya Ramamoorthy PhD ’22, a research specialist at MIT and lead author of a new paper on the work that was published this month in Nature Computational Science. “The question we were asking is: can we use machine learning to identify if, and to what extent, those types of consistent patterns across individuals exist?”

    Their technique, indeed, identified discrete and robust clinical patterns in ALS progression, many of which are non-linear. Further, these disease progression subtypes were consistent across patient populations and disease metrics. The team additionally found that their method can be applied to Alzheimer’s and Parkinson’s diseases as well.

    Joining Ramamoorthy on the paper are MIT-IBM Watson AI Lab members Ernest Fraenkel, a professor in the MIT Department of Biological Engineering; Research Scientist Soumya Ghosh of IBM Research; and Principal Research Scientist Kenney Ng, also of IBM Research. Additional authors include Kristen Severson PhD ’18, a senior researcher at Microsoft Research and former member of the Watson Lab and of IBM Research; Karen Sachs PhD ’06 of Next Generation Analytics; a team of researchers with Answer ALS; Jonathan D. Glass and Christina N. Fournier of the Emory University School of Medicine; the Pooled Resource Open-Access ALS Clinical Trials Consortium; ALS/MND Natural History Consortium; Todd M. Herrington of Massachusetts General Hospital (MGH) and Harvard Medical School; and James D. Berry of MGH.

    Play video

    MIT Professor Ernest Fraenkel describes early stages of his research looking at root causes of amyotrophic lateral sclerosis (ALS).

    Reshaping health decline

    After consulting with clinicians, the team of machine learning researchers and neurologists let the data speak for itself. They designed an unsupervised machine-learning model that employed two methods: Gaussian process regression and Dirichlet process clustering. These inferred the health trajectories directly from patient data and automatically grouped similar trajectories together without prescribing the number of clusters or the shape of the curves, forming ALS progression “subtypes.” Their method incorporated prior clinical knowledge in the way of a bias for negative trajectories — consistent with expectations for neurodegenerative disease progressions — but did not assume any linearity. “We know that linearity is not reflective of what’s actually observed,” says Ng. “The methods and models that we use here were more flexible, in the sense that, they capture what was seen in the data,” without the need for expensive labeled data and prescription of parameters.

    Primarily, they applied the model to five longitudinal datasets from ALS clinical trials and observational studies. These used the gold standard to measure symptom development: the ALS functional rating scale revised (ALSFRS-R), which captures a global picture of patient neurological impairment but can be a bit of a “messy metric.” Additionally, performance on survivability probabilities, forced vital capacity (a measurement of respiratory function), and subscores of ALSFRS-R, which looks at individual bodily functions, were incorporated.

    New regimes of progression and utility

    When their population-level model was trained and tested on these metrics, four dominant patterns of disease popped out of the many trajectories — sigmoidal fast progression, stable slow progression, unstable slow progression, and unstable moderate progression — many with strong nonlinear characteristics. Notably, it captured trajectories where patients experienced a sudden loss of ability, called a functional cliff, which would significantly impact treatments, enrollment in clinical trials, and quality of life.

    The researchers compared their method against other commonly used linear and nonlinear approaches in the field to separate the contribution of clustering and linearity to the model’s accuracy. The new work outperformed them, even patient-specific models, and found that subtype patterns were consistent across measures. Impressively, when data were withheld, the model was able to interpolate missing values, and, critically, could forecast future health measures. The model could also be trained on one ALSFRS-R dataset and predict cluster membership in others, making it robust, generalizable, and accurate with scarce data. So long as 6-12 months of data were available, health trajectories could be inferred with higher confidence than conventional methods.

    The researchers’ approach also provided insights into Alzheimer’s and Parkinson’s diseases, both of which can have a range of symptom presentations and progression. For Alzheimer’s, the new technique could identify distinct disease patterns, in particular variations in the rates of conversion of mild to severe disease. The Parkinson’s analysis demonstrated a relationship between progression trajectories for off-medication scores and disease phenotypes, such as the tremor-dominant or postural instability/gait difficulty forms of Parkinson’s disease.

    The work makes significant strides to find the signal amongst the noise in the time-series of complex neurodegenerative disease. “The patterns that we see are reproducible across studies, which I don’t believe had been shown before, and that may have implications for how we subtype the [ALS] disease,” says Fraenkel. As the FDA has been considering the impact of non-linearity in clinical trial designs, the team notes that their work is particularly pertinent.

    As new ways to understand disease mechanisms come online, this model provides another tool to pick apart illnesses like ALS, Alzheimer’s, and Parkinson’s from a systems biology perspective.

    “We have a lot of molecular data from the same patients, and so our long-term goal is to see whether there are subtypes of the disease,” says Fraenkel, whose lab looks at cellular changes to understand the etiology of diseases and possible targets for cures. “One approach is to start with the symptoms … and see if people with different patterns of disease progression are also different at the molecular level. That might lead you to a therapy. Then there’s the bottom-up approach, where you start with the molecules” and try to reconstruct biological pathways that might be affected. “We’re going [to be tackling this] from both ends … and finding if something meets in the middle.”

    This research was supported, in part, by the MIT-IBM Watson AI Lab, the Muscular Dystrophy Association, Department of Veterans Affairs of Research and Development, the Department of Defense, NSF Gradate Research Fellowship Program, Siebel Scholars Fellowship, Answer ALS, the United States Army Medical Research Acquisition Activity, National Institutes of Health, and the NIH/NINDS. More

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    New program to support translational research in AI, data science, and machine learning

    The MIT School of Engineering and Pillar VC today announced the MIT-Pillar AI Collective, a one-year pilot program funded by a gift from Pillar VC that will provide seed grants for projects in artificial intelligence, machine learning, and data science with the goal of supporting translational research. The program will support graduate students and postdocs through access to funding, mentorship, and customer discovery.

    Administered by the MIT Deshpande Center for Technological Innovation, the MIT-Pillar AI Collective will center on the market discovery process, advancing projects through market research, customer discovery, and prototyping. Graduate students and postdocs will aim to emerge from the program having built minimum viable products, with support from Pillar VC and experienced industry leaders.

    “We are grateful for this support from Pillar VC and to join forces to converge the commercialization of translational research in AI, data science, and machine learning, with an emphasis on identifying and cultivating prospective entrepreneurs,” says Anantha Chandrakasan, dean of the MIT School of Engineering and Vannevar Bush Professor of Electrical Engineering and Computer Science. “Pillar’s focus on mentorship for our graduate students and postdoctoral researchers, and centering the program within the Deshpande Center, will undoubtedly foster big ideas in AI and create an environment for prospective companies to launch and thrive.” 

    Founded by Jamie Goldstein ’89, Pillar VC is committed to growing companies and investing in personal and professional development, coaching, and community.

    “Many of the most promising companies of the future are living at MIT in the form of transformational research in the fields of data science, AI, and machine learning,” says Goldstein. “We’re honored by the chance to help unlock this potential and catalyze a new generation of founders by surrounding students and postdoctoral researchers with the resources and mentorship they need to move from the lab to industry.”

    The program will launch with the 2022-23 academic year. Grants will be open only to MIT faculty and students, with an emphasis on funding for graduate students in their final year, as well as postdocs. Applications must be submitted by MIT employees with principal investigator status. A selection committee composed of three MIT representatives will include Devavrat Shah, faculty director of the Deshpande Center, the Andrew (1956) and Erna Viterbi Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society; the chair of the selection committee; and a representative from the MIT Schwarzman College of Computing. The committee will also include representation from Pillar VC. Funding will be provided for up to nine research teams.

    “The Deshpande Center will serve as the perfect home for the new collective, given its focus on moving innovative technologies from the lab to the marketplace in the form of breakthrough products and new companies,” adds Chandrakasan. 

    “The Deshpande Center has a 20-year history of guiding new technologies toward commercialization, where they can have a greater impact,” says Shah. “This new collective will help the center expand its own impact by helping more projects realize their market potential and providing more support to researchers in the fast-growing fields of AI, machine learning, and data science.” More

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    Q&A: Global challenges surrounding the deployment of AI

    The AI Policy Forum (AIPF) is an initiative of the MIT Schwarzman College of Computing to move the global conversation about the impact of artificial intelligence from principles to practical policy implementation. Formed in late 2020, AIPF brings together leaders in government, business, and academia to develop approaches to address the societal challenges posed by the rapid advances and increasing applicability of AI.

    The co-chairs of the AI Policy Forum are Aleksander Madry, the Cadence Design Systems Professor; Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science; and Luis Videgaray, senior lecturer at MIT Sloan School of Management and director of MIT AI Policy for the World Project. Here, they discuss talk some of the key issues facing the AI policy landscape today and the challenges surrounding the deployment of AI. The three are co-organizers of the upcoming AI Policy Forum Summit on Sept. 28, which will further explore the issues discussed here.

    Q: Can you talk about the ­ongoing work of the AI Policy Forum and the AI policy landscape generally?

    Ozdaglar: There is no shortage of discussion about AI at different venues, but conversations are often high-level, focused on questions of ethics and principles, or on policy problems alone. The approach the AIPF takes to its work is to target specific questions with actionable policy solutions and engage with the stakeholders working directly in these areas. We work “behind the scenes” with smaller focus groups to tackle these challenges and aim to bring visibility to some potential solutions alongside the players working directly on them through larger gatherings.

    Q: AI impacts many sectors, which makes us naturally worry about its trustworthiness. Are there any emerging best practices for development and deployment of trustworthy AI?

    Madry: The most important thing to understand regarding deploying trustworthy AI is that AI technology isn’t some natural, preordained phenomenon. It is something built by people. People who are making certain design decisions.

    We thus need to advance research that can guide these decisions as well as provide more desirable solutions. But we also need to be deliberate and think carefully about the incentives that drive these decisions. 

    Now, these incentives stem largely from the business considerations, but not exclusively so. That is, we should also recognize that proper laws and regulations, as well as establishing thoughtful industry standards have a big role to play here too.

    Indeed, governments can put in place rules that prioritize the value of deploying AI while being keenly aware of the corresponding downsides, pitfalls, and impossibilities. The design of such rules will be an ongoing and evolving process as the technology continues to improve and change, and we need to adapt to socio-political realities as well.

    Q: Perhaps one of the most rapidly evolving domains in AI deployment is in the financial sector. From a policy perspective, how should governments, regulators, and lawmakers make AI work best for consumers in finance?

    Videgaray: The financial sector is seeing a number of trends that present policy challenges at the intersection of AI systems. For one, there is the issue of explainability. By law (in the U.S. and in many other countries), lenders need to provide explanations to customers when they take actions deleterious in whatever way, like denial of a loan, to a customer’s interest. However, as financial services increasingly rely on automated systems and machine learning models, the capacity of banks to unpack the “black box” of machine learning to provide that level of mandated explanation becomes tenuous. So how should the finance industry and its regulators adapt to this advance in technology? Perhaps we need new standards and expectations, as well as tools to meet these legal requirements.

    Meanwhile, economies of scale and data network effects are leading to a proliferation of AI outsourcing, and more broadly, AI-as-a-service is becoming increasingly common in the finance industry. In particular, we are seeing fintech companies provide the tools for underwriting to other financial institutions — be it large banks or small, local credit unions. What does this segmentation of the supply chain mean for the industry? Who is accountable for the potential problems in AI systems deployed through several layers of outsourcing? How can regulators adapt to guarantee their mandates of financial stability, fairness, and other societal standards?

    Q: Social media is one of the most controversial sectors of the economy, resulting in many societal shifts and disruptions around the world. What policies or reforms might be needed to best ensure social media is a force for public good and not public harm?

    Ozdaglar: The role of social media in society is of growing concern to many, but the nature of these concerns can vary quite a bit — with some seeing social media as not doing enough to prevent, for example, misinformation and extremism, and others seeing it as unduly silencing certain viewpoints. This lack of unified view on what the problem is impacts the capacity to enact any change. All of that is additionally coupled with the complexities of the legal framework in the U.S. spanning the First Amendment, Section 230 of the Communications Decency Act, and trade laws.

    However, these difficulties in regulating social media do not mean that there is nothing to be done. Indeed, regulators have begun to tighten their control over social media companies, both in the United States and abroad, be it through antitrust procedures or other means. In particular, Ofcom in the U.K. and the European Union is already introducing new layers of oversight to platforms. Additionally, some have proposed taxes on online advertising to address the negative externalities caused by current social media business model. So, the policy tools are there, if the political will and proper guidance exists to implement them. More

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    New leadership at MIT’s Center for Biomedical Innovation

    As it continues in its mission to improve global health through the development and implementation of biomedical innovation, the MIT Center for Biomedical Innovation (CBI) today announced changes to its leadership team: Stacy Springs has been named executive director, and Professor Richard Braatz has joined as the center’s new associate faculty director.

    The change in leadership comes at a time of rapid development in new therapeutic modalities, growing concern over global access to biologic medicines and healthy food, and widespread interest in applying computational tools and multi-disciplinary approaches to address long-standing biomedical challenges.

    “This marks an exciting new chapter for the CBI,” says faculty director Anthony J. Sinskey, professor of biology, who cofounded CBI in 2005. “As I look back at almost 20 years of CBI history, I see an exponential growth in our activities, educational offerings, and impact.”

    The center’s collaborative research model accelerates innovation in biotechnology and biomedical research, drawing on the expertise of faculty and researchers in MIT’s schools of Engineering and Science, the MIT Schwarzman College of Computing, and the MIT Sloan School of Management.

    Springs steps into the role of executive director having previously served as senior director of programs for CBI and as executive director of CBI’s Biomanufacturing Program and its Consortium on Adventitious Agent Contamination in Biomanufacturing (CAACB). She succeeds Gigi Hirsch, who founded the NEW Drug Development ParadIGmS (NEWDIGS) Initiative at CBI in 2009. Hirsch and NEWDIGS have now moved to Tufts Medical Center, establishing a headquarters at the new Center for Biomedical System Design within the Institute for Clinical Research and Health Policy Studies there.

    Braatz, a chemical engineer whose work is informed by mathematical modeling and computational techniques, conducts research in process data analytics, design, and control of advanced manufacturing systems.

    “It’s been great to interact with faculty from across the Institute who have complementary expertise,” says Braatz, the Edwin R. Gilliland Professor in the Department of Chemical Engineering. “Participating in CBI’s workshops has led to fruitful partnerships with companies in tackling industry-wide challenges.”

    CBI is housed under the Institute for Data Systems and Society and, specifically, the Sociotechnical Systems Research Center in the MIT Schwarzman College of Computing. CBI is home to two biomanufacturing consortia: the CAACB and the Biomanufacturing Consortium (BioMAN). Through these precompetitive collaborations, CBI researchers work with biomanufacturers and regulators to advance shared interests in biomanufacturing.

    In addition, CBI researchers are engaged in several sponsored research programs focused on integrated continuous biomanufacturing capabilities for monoclonal antibodies and vaccines, analytical technologies to measure quality and safety attributes of a variety of biologics, including gene and cell therapies, and rapid-cycle development of virus-like particle vaccines for SARS-CoV-2.

    In another significant initiative, CBI researchers are applying data analytics strategies to biomanufacturing problems. “In our smart data analytics project, we are creating new decision support tools and algorithms for biomanufacturing process control and plant-level decision-making. Further, we are leveraging machine learning and natural language processing to improve post-market surveillance studies,” says Springs.

    CBI is also working on advanced manufacturing for cell and gene therapies, among other new modalities, and is a part of the Singapore-MIT Alliance for Research and Technology – Critical Analytics for Manufacturing Personalized-Medicine (SMART CAMP). SMART CAMP is an international research effort focused on developing the analytical tools and biological understanding of critical quality attributes that will enable the manufacture and delivery of improved cell therapies to patients.

    “This is a crucial time for biomanufacturing and for innovation across the health-care value chain. The collaborative efforts of MIT researchers and consortia members will drive fundamental discovery and inform much-needed progress in industry,” says MIT Vice President for Research Maria Zuber.

    “CBI has a track record of engaging with health-care ecosystem challenges. I am confident that under the new leadership, it will continue to inspire MIT, the United States, and the entire world to improve the health of all people,” adds Daniel Huttenlocher, dean of the MIT Schwarzman College of Computing. More

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    Exploring emerging topics in artificial intelligence policy

    Members of the public sector, private sector, and academia convened for the second AI Policy Forum Symposium last month to explore critical directions and questions posed by artificial intelligence in our economies and societies.

    The virtual event, hosted by the AI Policy Forum (AIPF) — an undertaking by the MIT Schwarzman College of Computing to bridge high-level principles of AI policy with the practices and trade-offs of governing — brought together an array of distinguished panelists to delve into four cross-cutting topics: law, auditing, health care, and mobility.

    In the last year there have been substantial changes in the regulatory and policy landscape around AI in several countries — most notably in Europe with the development of the European Union Artificial Intelligence Act, the first attempt by a major regulator to propose a law on artificial intelligence. In the United States, the National AI Initiative Act of 2020, which became law in January 2021, is providing a coordinated program across federal government to accelerate AI research and application for economic prosperity and security gains. Finally, China recently advanced several new regulations of its own.

    Each of these developments represents a different approach to legislating AI, but what makes a good AI law? And when should AI legislation be based on binding rules with penalties versus establishing voluntary guidelines?

    Jonathan Zittrain, professor of international law at Harvard Law School and director of the Berkman Klein Center for Internet and Society, says the self-regulatory approach taken during the expansion of the internet had its limitations with companies struggling to balance their interests with those of their industry and the public.

    “One lesson might be that actually having representative government take an active role early on is a good idea,” he says. “It’s just that they’re challenged by the fact that there appears to be two phases in this environment of regulation. One, too early to tell, and two, too late to do anything about it. In AI I think a lot of people would say we’re still in the ‘too early to tell’ stage but given that there’s no middle zone before it’s too late, it might still call for some regulation.”

    A theme that came up repeatedly throughout the first panel on AI laws — a conversation moderated by Dan Huttenlocher, dean of the MIT Schwarzman College of Computing and chair of the AI Policy Forum — was the notion of trust. “If you told me the truth consistently, I would say you are an honest person. If AI could provide something similar, something that I can say is consistent and is the same, then I would say it’s trusted AI,” says Bitange Ndemo, professor of entrepreneurship at the University of Nairobi and the former permanent secretary of Kenya’s Ministry of Information and Communication.

    Eva Kaili, vice president of the European Parliament, adds that “In Europe, whenever you use something, like any medication, you know that it has been checked. You know you can trust it. You know the controls are there. We have to achieve the same with AI.” Kalli further stresses that building trust in AI systems will not only lead to people using more applications in a safe manner, but that AI itself will reap benefits as greater amounts of data will be generated as a result.

    The rapidly increasing applicability of AI across fields has prompted the need to address both the opportunities and challenges of emerging technologies and the impact they have on social and ethical issues such as privacy, fairness, bias, transparency, and accountability. In health care, for example, new techniques in machine learning have shown enormous promise for improving quality and efficiency, but questions of equity, data access and privacy, safety and reliability, and immunology and global health surveillance remain at large.

    MIT’s Marzyeh Ghassemi, an assistant professor in the Department of Electrical Engineering and Computer Science and the Institute for Medical Engineering and Science, and David Sontag, an associate professor of electrical engineering and computer science, collaborated with Ziad Obermeyer, an associate professor of health policy and management at the University of California Berkeley School of Public Health, to organize AIPF Health Wide Reach, a series of sessions to discuss issues of data sharing and privacy in clinical AI. The organizers assembled experts devoted to AI, policy, and health from around the world with the goal of understanding what can be done to decrease barriers to access to high-quality health data to advance more innovative, robust, and inclusive research results while being respectful of patient privacy.

    Over the course of the series, members of the group presented on a topic of expertise and were tasked with proposing concrete policy approaches to the challenge discussed. Drawing on these wide-ranging conversations, participants unveiled their findings during the symposium, covering nonprofit and government success stories and limited access models; upside demonstrations; legal frameworks, regulation, and funding; technical approaches to privacy; and infrastructure and data sharing. The group then discussed some of their recommendations that are summarized in a report that will be released soon.

    One of the findings calls for the need to make more data available for research use. Recommendations that stem from this finding include updating regulations to promote data sharing to enable easier access to safe harbors such as the Health Insurance Portability and Accountability Act (HIPAA) has for de-identification, as well as expanding funding for private health institutions to curate datasets, amongst others. Another finding, to remove barriers to data for researchers, supports a recommendation to decrease obstacles to research and development on federally created health data. “If this is data that should be accessible because it’s funded by some federal entity, we should easily establish the steps that are going to be part of gaining access to that so that it’s a more inclusive and equitable set of research opportunities for all,” says Ghassemi. The group also recommends taking a careful look at the ethical principles that govern data sharing. While there are already many principles proposed around this, Ghassemi says that “obviously you can’t satisfy all levers or buttons at once, but we think that this is a trade-off that’s very important to think through intelligently.”

    In addition to law and health care, other facets of AI policy explored during the event included auditing and monitoring AI systems at scale, and the role AI plays in mobility and the range of technical, business, and policy challenges for autonomous vehicles in particular.

    The AI Policy Forum Symposium was an effort to bring together communities of practice with the shared aim of designing the next chapter of AI. In his closing remarks, Aleksander Madry, the Cadence Designs Systems Professor of Computing at MIT and faculty co-lead of the AI Policy Forum, emphasized the importance of collaboration and the need for different communities to communicate with each other in order to truly make an impact in the AI policy space.

    “The dream here is that we all can meet together — researchers, industry, policymakers, and other stakeholders — and really talk to each other, understand each other’s concerns, and think together about solutions,” Madry said. “This is the mission of the AI Policy Forum and this is what we want to enable.” More

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    Companies use MIT research to identify and respond to supply chain risks

    In February 2020, MIT professor David Simchi-Levi predicted the future. In an article in Harvard Business Review, he and his colleague warned that the new coronavirus outbreak would throttle supply chains and shutter tens of thousands of businesses across North America and Europe by mid-March.

    For Simchi-Levi, who had developed new models of supply chain resiliency and advised major companies on how to best shield themselves from supply chain woes, the signs of disruption were plain to see. Two years later, the professor of engineering systems at the MIT Schwarzman College of Computing and the Department of Civil and Environmental Engineering, and director of the MIT Data Science Lab has found a “flood of interest” from companies anxious to apply his Risk Exposure Index (REI) research to identify and respond to hidden risks in their own supply chains.

    His work on “stress tests” for critical supply chains and ways to guide global supply chain recovery were included in the 2022 Economic Report of the President presented to the U.S. Congress in April.

    It is rare that data science research can influence policy at the highest levels, Simchi-Levi says, but his models reflect something that business needs now: a new world of continuing global crisis, without relying on historical precedent.

    “What the last two years showed is that you cannot plan just based on what happened last year or the last two years,” Simchi-Levi says.

    He recalled the famous quote, sometimes attributed to hockey great Wayne Gretzsky, that good players don’t skate to where the puck is, but where the puck is going to be. “We are not focusing on the state of the supply chain right now, but what may happen six weeks from now, eight weeks from now, to prepare ourselves today to prevent the problems of the future.”

    Finding hidden risks

    At the heart of REI is a mathematical model of the supply chain that focuses on potential failures at different supply chain nodes — a flood at a supplier’s factory, or a shortage of raw materials at another factory, for instance. By calculating variables such as “time-to-recover” (TTR), which measures how long it will take a particular node to be back at full function, and time-to-survive (TTS), which identifies the maximum duration that the supply chain can match supply with demand after a disruption, the model focuses on the impact of disruption on the supply chain, rather than the cause of disruption.

    Even before the pandemic, catastrophic events such as the 2010 Iceland volcanic eruption and the 2011 Tohoku earthquake and tsunami in Japan were threatening these nodes. “For many years, companies from a variety of industries focused mostly on efficiency, cutting costs as much as possible, using strategies like outsourcing and offshoring,” Simchi-Levi says. “They were very successful doing this, but it has dramatically increased their exposure to risk.”

    Using their model, Simchi-Levi and colleagues began working with Ford Motor Company in 2013 to improve the company’s supply chain resiliency. The partnership uncovered some surprising hidden risks.

    To begin with, the researchers found out that Ford’s “strategic suppliers” — the nodes of the supply chain where the company spent large amount of money each year — had only moderate exposure to risk. Instead, the biggest risk “tended to come from tiny suppliers that provide Ford with components that cost about 10 cents,” says Simchi-Levi.

    The analysis also found that risky suppliers are everywhere across the globe. “There is this idea that if you just move suppliers closer to market, to demand, to North America or to Mexico, you increase the resiliency of your supply chain. That is not supported by our data,” he says.

    Rewards of resiliency

    By creating a virtual representation, or “digital twin,” of the Ford supply chain, the researchers were able to test out strategies at each node to see what would increase supply chain resiliency. Should the company invest in more warehouses to store a key component? Should it shift production of a component to another factory?

    Companies are sometimes reluctant to invest in supply chain resiliency, Simchi-Levi says, but the analysis isn’t just about risk. “It’s also going to help you identify savings opportunities. The company may be building a lot of misplaced, costly inventory, for instance, and our method helps them to identify these inefficiencies and cut costs.”

    Since working with Ford, Simchi-Levi and colleagues have collaborated with many other companies, including a partnership with Accenture, to scale the REI technology to a variety of industries including high-tech, industrial equipment, home improvement retailers, fashion retailers, and consumer packaged goods.

    Annette Clayton, the CEO of Schneider Electric North America and previously its chief supply chain officer, has worked with Simchi-Levi for 17 years. “When I first went to work for Schneider, I asked David and his team to help us look at resiliency and inventory positioning in order to make the best cost, delivery, flexibility, and speed trade-offs for the North American supply chain,” she says. “As the pandemic unfolded, the very learnings in supply chain resiliency we had worked on before became even more important and we partnered with David and his team again,”

    “We have used TTR and TTS to determine places where we need to develop and duplicate supplier capability, from raw materials to assembled parts. We increased inventories where our time-to-recover because of extended logistics times exceeded our time-to-survive,” Clayton adds. “We have used TTR and TTS to prioritize our workload in supplier development, procurement and expanding our own manufacturing capacity.”

    The REI approach can even be applied to an entire country’s economy, as the U.N. Office for Disaster Risk Reduction has done for developing countries such as Thailand in the wake of disastrous flooding in 2011.

    Simchi-Levi and colleagues have been motivated by the pandemic to enhance the REI model with new features. “Because we have started collaborating with more companies, we have realized some interesting, company-specific business constraints,” he says, which are leading to more efficient ways of calculating hidden risk. More

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    Zero-trust architecture may hold the answer to cybersecurity insider threats

    For years, organizations have taken a defensive “castle-and-moat” approach to cybersecurity, seeking to secure the perimeters of their networks to block out any malicious actors. Individuals with the right credentials were assumed to be trustworthy and allowed access to a network’s systems and data without having to reauthorize themselves at each access attempt. However, organizations today increasingly store data in the cloud and allow employees to connect to the network remotely, both of which create vulnerabilities to this traditional approach. A more secure future may require a “zero-trust architecture,” in which users must prove their authenticity each time they access a network application or data.

    In May 2021, President Joe Biden’s Executive Order on Improving the Nation’s Cybersecurity outlined a goal for federal agencies to implement zero-trust security. Since then, MIT Lincoln Laboratory has been performing a study on zero-trust architectures, with the goals of reviewing their implementation in government and industry, identifying technical gaps and opportunities, and developing a set of recommendations for the United States’ approach to a zero-trust system.

    The study team’s first step was to define the term “zero trust” and understand the misperceptions in the field surrounding the concept. Some of these misperceptions suggest that a zero-trust architecture requires entirely new equipment to implement, or that it makes systems so “locked down” they’re not usable. 

    “Part of the reason why there is a lot of confusion about what zero trust is, is because it takes what the cybersecurity world has known about for many years and applies it in a different way,” says Jeffrey Gottschalk, the assistant head of Lincoln Laboratory’s Cyber Security and Information Sciences Division and study’s co-lead. “It is a paradigm shift in terms of how to think about security, but holistically it takes a lot of things that we already know how to do — such as multi-factor authentication, encryption, and software-defined networking­ — and combines them in different ways.”

    Play video

    Presentation: Overview of Zero Trust Architectures

    Recent high-profile cybersecurity incidents — such as those involving the National Security Agency, the U.S. Office of Personnel Management, Colonial Pipeline, SolarWinds, and Sony Pictures — highlight the vulnerability of systems and the need to rethink cybersecurity approaches.

    The study team reviewed recent, impactful cybersecurity incidents to identify which security principles were most responsible for the scale and impact of the attack. “We noticed that while a number of these attacks exploited previously unknown implementation vulnerabilities (also known as ‘zero-days’), the vast majority actually were due to the exploitation of operational security principles,” says Christopher Roeser, study co-lead and the assistant head of the Homeland Protection and Air Traffic Control Division, “that is, the gaining of individuals’ credentials, and the movement within a well-connected network that allows users to gather a significant amount of information or have very widespread effects.”

    In other words, the malicious actor had “breached the moat” and effectively became an insider.

    Zero-trust security principles could protect against this type of insider threat by treating every component, service, and user of a system as continuously exposed to and potentially compromised by a malicious actor. A user’s identity is verified each time that they request to access a new resource, and every access is mediated, logged, and analyzed. It’s like putting trip wires all over the inside of a network system, says Gottschalk. “So, when an adversary trips over that trip wire, you’ll get a signal and can validate that signal and see what’s going on.”

    In practice, a zero-trust approach could look like replacing a single-sign-on system, which lets users sign in just once for access to multiple applications, with a cloud-based identity that is known and verified. “Today, a lot of organizations have different ways that people authenticate and log onto systems, and many of those have been aggregated for expediency into single-sign-on capabilities, just to make it easier for people to log onto their systems. But we envision a future state that embraces zero trust, where identity verification is enabled by cloud-based identity that’s portable and ubiquitous, and very secure itself.”

    While conducting their study, the team spoke to approximately 10 companies and government organizations that have adopted zero-trust implementations — either through cloud services, in-house management, or a combination of both. They found the hybrid approach to be a good model for government organizations to adopt. They also found that the implementation could take from three to five years. “We talked to organizations that have actually done implementations of zero trust, and all of them have indicated that significant organizational commitment and change was required to be able to implement them,” Gottschalk says.

    But a key takeaway from the study is that there isn’t a one-size-fits-all approach to zero trust. “It’s why we think that having test-bed and pilot efforts are going to be very important to balance out zero-trust security with the mission needs of those systems,” Gottschalk says. The team also recognizes the importance of conducting ongoing research and development beyond initial zero-trust implementations, to continue to address evolving threats.

    Lincoln Laboratory will present further findings from the study at its upcoming Cyber Technology for National Security conference, which will be held June 28-29. The conference will also offer a short course for attendees to learn more about the benefits and implementations of zero-trust architectures.  More

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    MIT announces five flagship projects in first-ever Climate Grand Challenges competition

    MIT today announced the five flagship projects selected in its first-ever Climate Grand Challenges competition. These multiyear projects will define a dynamic research agenda focused on unraveling some of the toughest unsolved climate problems and bringing high-impact, science-based solutions to the world on an accelerated basis.

    Representing the most promising concepts to emerge from the two-year competition, the five flagship projects will receive additional funding and resources from MIT and others to develop their ideas and swiftly transform them into practical solutions at scale.

    “Climate Grand Challenges represents a whole-of-MIT drive to develop game-changing advances to confront the escalating climate crisis, in time to make a difference,” says MIT President L. Rafael Reif. “We are inspired by the creativity and boldness of the flagship ideas and by their potential to make a significant contribution to the global climate response. But given the planet-wide scale of the challenge, success depends on partnership. We are eager to work with visionary leaders in every sector to accelerate this impact-oriented research, implement serious solutions at scale, and inspire others to join us in confronting this urgent challenge for humankind.”

    Brief descriptions of the five Climate Grand Challenges flagship projects are provided below.

    Bringing Computation to the Climate Challenge

    This project leverages advances in artificial intelligence, machine learning, and data sciences to improve the accuracy of climate models and make them more useful to a variety of stakeholders — from communities to industry. The team is developing a digital twin of the Earth that harnesses more data than ever before to reduce and quantify uncertainties in climate projections.

    Research leads: Raffaele Ferrari, the Cecil and Ida Green Professor of Oceanography in the Department of Earth, Atmospheric and Planetary Sciences, and director of the Program in Atmospheres, Oceans, and Climate; and Noelle Eckley Selin, director of the Technology and Policy Program and professor with a joint appointment in the Institute for Data, Systems, and Society and the Department of Earth, Atmospheric and Planetary Sciences

    Center for Electrification and Decarbonization of Industry

    This project seeks to reinvent and electrify the processes and materials behind hard-to-decarbonize industries like steel, cement, ammonia, and ethylene production. A new innovation hub will perform targeted fundamental research and engineering with urgency, pushing the technological envelope on electricity-driven chemical transformations.

    Research leads: Yet-Ming Chiang, the Kyocera Professor of Materials Science and Engineering, and Bilge Yıldız, the Breene M. Kerr Professor in the Department of Nuclear Science and Engineering and professor in the Department of Materials Science and Engineering

    Preparing for a new world of weather and climate extremes

    This project addresses key gaps in knowledge about intensifying extreme events such as floods, hurricanes, and heat waves, and quantifies their long-term risk in a changing climate. The team is developing a scalable climate-change adaptation toolkit to help vulnerable communities and low-carbon energy providers prepare for these extreme weather events.

    Research leads: Kerry Emanuel, the Cecil and Ida Green Professor of Atmospheric Science in the Department of Earth, Atmospheric and Planetary Sciences and co-director of the MIT Lorenz Center; Miho Mazereeuw, associate professor of architecture and urbanism in the Department of Architecture and director of the Urban Risk Lab; and Paul O’Gorman, professor in the Program in Atmospheres, Oceans, and Climate in the Department of Earth, Atmospheric and Planetary Sciences

    The Climate Resilience Early Warning System

    The CREWSnet project seeks to reinvent climate change adaptation with a novel forecasting system that empowers underserved communities to interpret local climate risk, proactively plan for their futures incorporating resilience strategies, and minimize losses. CREWSnet will initially be demonstrated in southwestern Bangladesh, serving as a model for similarly threatened regions around the world.

    Research leads: John Aldridge, assistant leader of the Humanitarian Assistance and Disaster Relief Systems Group at MIT Lincoln Laboratory, and Elfatih Eltahir, the H.M. King Bhumibol Professor of Hydrology and Climate in the Department of Civil and Environmental Engineering

    Revolutionizing agriculture with low-emissions, resilient crops

    This project works to revolutionize the agricultural sector with climate-resilient crops and fertilizers that have the ability to dramatically reduce greenhouse gas emissions from food production.

    Research lead: Christopher Voigt, the Daniel I.C. Wang Professor in the Department of Biological Engineering

    “As one of the world’s leading institutions of research and innovation, it is incumbent upon MIT to draw on our depth of knowledge, ingenuity, and ambition to tackle the hard climate problems now confronting the world,” says Richard Lester, MIT associate provost for international activities. “Together with collaborators across industry, finance, community, and government, the Climate Grand Challenges teams are looking to develop and implement high-impact, path-breaking climate solutions rapidly and at a grand scale.”

    The initial call for ideas in 2020 yielded nearly 100 letters of interest from almost 400 faculty members and senior researchers, representing 90 percent of MIT departments. After an extensive evaluation, 27 finalist teams received a total of $2.7 million to develop comprehensive research and innovation plans. The projects address four broad research themes:

    To select the winning projects, research plans were reviewed by panels of international experts representing relevant scientific and technical domains as well as experts in processes and policies for innovation and scalability.

    “In response to climate change, the world really needs to do two things quickly: deploy the solutions we already have much more widely, and develop new solutions that are urgently needed to tackle this intensifying threat,” says Maria Zuber, MIT vice president for research. “These five flagship projects exemplify MIT’s strong determination to bring its knowledge and expertise to bear in generating new ideas and solutions that will help solve the climate problem.”

    “The Climate Grand Challenges flagship projects set a new standard for inclusive climate solutions that can be adapted and implemented across the globe,” says MIT Chancellor Melissa Nobles. “This competition propels the entire MIT research community — faculty, students, postdocs, and staff — to act with urgency around a worsening climate crisis, and I look forward to seeing the difference these projects can make.”

    “MIT’s efforts on climate research amid the climate crisis was a primary reason that I chose to attend MIT, and remains a reason that I view the Institute favorably. MIT has a clear opportunity to be a thought leader in the climate space in our own MIT way, which is why CGC fits in so well,” says senior Megan Xu, who served on the Climate Grand Challenges student committee and is studying ways to make the food system more sustainable.

    The Climate Grand Challenges competition is a key initiative of “Fast Forward: MIT’s Climate Action Plan for the Decade,” which the Institute published in May 2021. Fast Forward outlines MIT’s comprehensive plan for helping the world address the climate crisis. It consists of five broad areas of action: sparking innovation, educating future generations, informing and leveraging government action, reducing MIT’s own climate impact, and uniting and coordinating all of MIT’s climate efforts. More