A new twist on old-school animation

It’s another case of a class project that turned into a bit more than the typical assignment. 

The story began last fall in the MIT course 6.810 (Engineering Interactive Technologies) taught by Stefanie Mueller, an associate professor in MIT’s Department of Electrical Engineering and Computer Science. The students, who were mostly undergraduates, were asked to do a final group project, and they were assisted in this effort by graduate students who were helping Mueller teach the course. 

Now, the product of this effort, along with an accompanying paper, will be presented in August in Vancouver, Canada, at SIGGRAPH 2022, “the world’s largest, most influential annual conference in computer graphics and interactive techniques.”

Ticha Sethapakdi, a third-year PhD student based at MIT’s Computer Science and Artificial Intelligence Laboratory, thought hard about a task that could be accomplished in a couple of months and would also enable students to utilize the skills they’d learned in class. Perhaps they could develop an instant camera, she thought, but instead of following the Polaroid example, it might be fun to make a camera that produced moving images. A cheap and portable device like this, Sethapakdi mused, could be a great icebreaker in social situations, and might, more importantly, open the door to other applications that were not immediately obvious to members of their research team.

To keep within the modest budgets allotted to them, Sethapakdi recognized that they’d have to adopt an easy, low-tech approach that would not require costly and sophisticated equipment. She hit upon a technique called kinegrams — which were invented in the late-1800s, just before the introduction of the first animated movies — and she decided this might be a good way to go.

Here, in short, is how a kinegram works. First, you start with several images or frames — let’s say three — of the same object, such as a butterfly, in different poses. You then cut each image into horizontal strips, perhaps 10 in all, of a uniform width. Next, you take these three images and combine them onto a page to create a single “interlaced image” consisting of 30 horizontal strips altogether — starting with the top strips from images one, two, and three and continuing to alternate in that fashion until the last three strips are sequentially laid out. Next you take a “striped overlay,” a separate sheet that has transparent strips — again, 10 in all — of the same width as the strips on the interlaced images. Each transparent strip, in turn, is interspersed with 10 opaque strips that are twice as wide as the transparent ones.

If you then lined up the two sheets, placed the striped overlay on top of the interlaced image, and moved the overlay down quickly, you would see the butterfly moving between its different poses. It’s a rather crude form of animation, though somewhat more complex than the flipbook cartoons that grade-schoolers can make.

The challenge, however, was to find a way of producing kinegrams quickly and easily with a lightweight, portable device. And that’s how the KineCAM came into being. All its components, save for the batteries, fit into a box of dimensions 3.5” x 5.6” x 1.7”. The pieces include a small computer called a Raspberry Pi, an attached video camera (comparable to those found in cellphones), a thermal printer (which is similar to that used to print receipts), a camera shutter button, and an LED indicator light — all of which can be bought, “off the shelf,” for less than $100 altogether.

As Sethapakdi and her coauthors — Mueller, postdoc Mackenzie Leake, and two undergraduates, Catalina Monsalve Rodriguez and Miranda J. Cai — explain in their paper: “to create the kinegram, our system records the video for a fixed unit of time and selects n frames from it. It then decomposes the frames into strips of width w pixels, interlaces them, and composites them into a single image.”

“We have to program the system ourselves,” Sethapakdi notes. “The software takes a few frames of videos, slices up the images, and sends the sliced-up version to the printer.” The turnaround time is remarkably brief, taking only about 16 seconds to go from shooting the pictures to printing. Everything is done with the KineCAM itself, except for creating the striped overlays, which are printed in advance on transparent film using a conventional inkjet printer.

One advantage of the approach, says Sethapakdi, “is that you can go out into the world and, on the spot, make these animated photographs.” One can, of course, take pictures with a cellphone, she adds. “But there’s something very appealing and intimate about having an actual physical receipt — a one-of-a-kind copy of some experience.” And a moving picture might be able to capture aspects of that experience that a still photo cannot.

The MIT team believes the KineCAM has applications in computer graphics and “rotoscoping,” a traditional animation technique. Sethapakdi considers it likely that artists and researchers will come up with some interesting ideas of their own. “We are open-sourcing this project so that others can modify the camera and adapt the code and design it to do anything they want it to do. We’re happy to let others explore the possibilities.”

She’ll soon be speaking to a target audience at SIGGRAPH 2022. Someone attending that conference may well have plans for KineCAM that had not yet occurred to its inventors — and that’s a prospect Sethapakdi is actively looking forward to.

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When cells’ tiny differences have far-reaching implications

Within a given tissue or organ, cells may appear very similar or even identical. But at the molecular level, these cells can have small differences that lead to wide variations in their functions.

Alex K. Shalek, an MIT associate professor of chemistry, relishes the challenge of uncovering those small distinctions. In his lab, researchers develop and deploy technologies such as single-cell RNA-sequencing, which lets them analyze differences in gene expression patterns and allows them to figure out how each cell contributes to a tissue’s function.

“Single-cell RNA-sequencing is an incredibly powerful way to examine what cells are doing at a given moment. By looking at associations among the different mRNAs that cells express, we can identify really important features of a tissue — like what cells are present and what are those cells trying to do,” says Shalek, who is also a core member of MIT’s Institute for Medical Engineering and Science and an extramural member of the Koch Institute for Integrative Cancer Research, as well as a member of the Ragon Institute of MGH, MIT and Harvard and an institute member of the Broad Institute of Harvard and MIT.

While his work focuses on identifying small-scale differences, he hopes that it will have large-scale implications, as he seeks to better understand globally important diseases such as HIV, tuberculosis, and cancer.

“A lot of what we do now is global collaborative work that really focuses on understanding the cellular and molecular basis of human diseases — partnering with people in over 30 countries on six continents,” he says. “I love fundamental work and the precision possible in model systems, but I’ve always been very motivated to connect our science to human health, and to understand what’s happening in different diseases so we can develop better preventions and cures.”

Exploring the physical world

As a student at Columbia University, Shalek bounced between a few different majors before settling on chemical physics. He started out in physics because he wanted to understand the fundamental laws of how the physical world works. However, as he got farther along, he realized that most of the research opportunities available involved detection of high-energy particles, which didn’t appeal to him.

He then took some math courses but didn’t feel a real connection to the material, so he switched to chemistry, where he encountered a course that resonated with him: statistical mechanics, which involves using statistical methods to describe the behavior of large numbers of atoms or molecules.

“I loved it because it helped me understand how all these rules that I’d learned in physics about microscopic particles actually translated to macroscopic things in the world around me,” Shalek says.

Torn as to what he wanted to do after graduating from college, he decided to go to graduate school. At Harvard University, where he earned a PhD in chemical physics, he ended up working with Hongkun Park, a professor of chemistry and of physics. Park, who had just received tenure for his work measuring the optical and electronic properties of single molecules and nanomaterials, was in the midst of building a new program to study the brain. Specifically, he wanted to find ways to make high-precision electrical measurements of many neurons at once.

As the first to join the new effort, Shalek found himself responsible for figuring out how to create computational models, fabricate devices, write software to control the electronics, analyze the data, and many other things that he didn’t know how to do, on top of learning neurobiology.

“It was challenging, to say the least. I got a crash course in how to do a bunch of different things,” he recalls. “It was a very humbling experience, but I learned a lot. By begging my way into various labs around town at Harvard and MIT, I was able to pick things up faster. I got very comfortable taking up new subjects and tackling hard problems by leaning on others and learning from them.”

His efforts led to the development of several new technologies, including arrays of nanowires that could be used to record neuron activity as well as to inject molecules into individual cells without harming them and to remove some of the contents of the cells. This proved especially useful for studying immune cells, which usually resist other delivery methods such as viruses.

An individual approach

Shalek’s work in graduate school stimulated his interest in systems biology, which involves comprehensively measuring many aspects of a biological system using genomics and other techniques, then building models that account for the observed measurements, and finally testing the models in living cells using perturbation techniques. However, to his frustration, he often found that when he tried to test a prediction of a model, not all of the cells in the system would show the expected outcome.

“There was a lot of variability,” he says. “I’d see differences in the level of mRNAs, or in the expression or activity of proteins, or sometimes all my cells wouldn’t differentiate into the same thing.”

He began to wonder if it would be worthwhile to try to study each individual cell within a system, instead of the traditional approach of doing pooled sequencing of their mRNA. During his postdoc, he worked with Park and Aviv Regev, an MIT professor of biology and member of the Broad Institute, to develop technologies for sequencing all of the mRNA found in large sets of individual cells. This information can then be used to classify cells into distinct types and reveal the state they’re in at a given moment in time.

In his lab at MIT, Shalek now uses improvements he’s helped make to this approach to analyze many types of cells and tissues, and to study how their identities are shaped by their environments. His recent work has included studies of how cancer cell state impacts response to chemotherapy, the cellular targets of the SARS-CoV-2 virus, analysis of cell types involved in lactation, and identification of T cells primed to produce inflammation during allergic responses.

An overarching theme of this work is how cells maintain homeostasis, or the steady state of physical and chemical conditions within living organisms.

“We know how important homeostasis is because we know that imbalances can lead to autoimmune diseases and immunodeficiencies, or to the growth of cancers,” Shalek says. “We want to really define at a cellular level, what is balance, how do you maintain balance, and how do various environmental factors like exposures to different infections or diets alter that balance?”

Shalek says he appreciates the many opportunities he has to work with other researchers around MIT and the Boston area, in addition to his many international collaborators. As his lab works on problems of human disease, he makes sure to help nurture the next generation of scientists, the same way that he was able to receive training and mentoring as a graduate student and postdoc.

“If you put together the collective brain trust of this community, as well as partner with people all around the world, you can do incredible things,” Shalek says. “My experience taught me the importance of supporting and empowering scientists and of trying to uplift the community, which is a lot of what I’ve focused on. I recognize that a lot of my success has depended upon people opening their labs and giving me time and supporting me, and so I’ve tried to pay that forward.”

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A technique to improve both fairness and accuracy in artificial intelligence

For workers who use machine-learning models to help them make decisions, knowing when to trust a model’s predictions is not always an easy task, especially since these models are often so complex that their inner workings remain a mystery.

Users sometimes employ a technique, known as selective regression, in which the model estimates its confidence level for each prediction and will reject predictions when its confidence is too low. Then a human can examine those cases, gather additional information, and make a decision about each one manually.

But while selective regression has been shown to improve the overall performance of a model, researchers at MIT and the MIT-IBM Watson AI Lab have discovered that the technique can have the opposite effect for underrepresented groups of people in a dataset. As the model’s confidence increases with selective regression, its chance of making the right prediction also increases, but this does not always happen for all subgroups.

For instance, a model suggesting loan approvals might make fewer errors on average, but it may actually make more wrong predictions for Black or female applicants. One reason this can occur is due to the fact that the model’s confidence measure is trained using overrepresented groups and may not be accurate for these underrepresented groups.

Once they had identified this problem, the MIT researchers developed two algorithms that can remedy the issue. Using real-world datasets, they show that the algorithms reduce performance disparities that had affected marginalized subgroups.

“Ultimately, this is about being more intelligent about which samples you hand off to a human to deal with. Rather than just minimizing some broad error rate for the model, we want to make sure the error rate across groups is taken into account in a smart way,” says senior MIT author Greg Wornell, the Sumitomo Professor in Engineering in the Department of Electrical Engineering and Computer Science (EECS) who leads the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE) and is a member of the MIT-IBM Watson AI Lab.

Joining Wornell on the paper are co-lead authors Abhin Shah, an EECS graduate student, and Yuheng Bu, a postdoc in RLE; as well as Joshua Ka-Wing Lee SM ’17, ScD ’21 and Subhro Das, Rameswar Panda, and Prasanna Sattigeri, research staff members at the MIT-IBM Watson AI Lab. The paper will be presented this month at the International Conference on Machine Learning.

To predict or not to predict

Regression is a technique that estimates the relationship between a dependent variable and independent variables. In machine learning, regression analysis is commonly used for prediction tasks, such as predicting the price of a home given its features (number of bedrooms, square footage, etc.) With selective regression, the machine-learning model can make one of two choices for each input — it can make a prediction or abstain from a prediction if it doesn’t have enough confidence in its decision.

When the model abstains, it reduces the fraction of samples it is making predictions on, which is known as coverage. By only making predictions on inputs that it is highly confident about, the overall performance of the model should improve. But this can also amplify biases that exist in a dataset, which occur when the model does not have sufficient data from certain subgroups. This can lead to errors or bad predictions for underrepresented individuals.

The MIT researchers aimed to ensure that, as the overall error rate for the model improves with selective regression, the performance for every subgroup also improves. They call this monotonic selective risk.

“It was challenging to come up with the right notion of fairness for this particular problem. But by enforcing this criteria, monotonic selective risk, we can make sure the model performance is actually getting better across all subgroups when you reduce the coverage,” says Shah.

Focus on fairness

The team developed two neural network algorithms that impose this fairness criteria to solve the problem.

One algorithm guarantees that the features the model uses to make predictions contain all information about the sensitive attributes in the dataset, such as race and sex, that is relevant to the target variable of interest. Sensitive attributes are features that may not be used for decisions, often due to laws or organizational policies. The second algorithm employs a calibration technique to ensure the model makes the same prediction for an input, regardless of whether any sensitive attributes are added to that input.

The researchers tested these algorithms by applying them to real-world datasets that could be used in high-stakes decision making. One, an insurance dataset, is used to predict total annual medical expenses charged to patients using demographic statistics; another, a crime dataset, is used to predict the number of violent crimes in communities using socioeconomic information. Both datasets contain sensitive attributes for individuals.

When they implemented their algorithms on top of a standard machine-learning method for selective regression, they were able to reduce disparities by achieving lower error rates for the minority subgroups in each dataset. Moreover, this was accomplished without significantly impacting the overall error rate.

“We see that if we don’t impose certain constraints, in cases where the model is really confident, it could actually be making more errors, which could be very costly in some applications, like health care. So if we reverse the trend and make it more intuitive, we will catch a lot of these errors. A major goal of this work is to avoid errors going silently undetected,” Sattigeri says.

The researchers plan to apply their solutions to other applications, such as predicting house prices, student GPA, or loan interest rate, to see if the algorithms need to be calibrated for those tasks, says Shah. They also want to explore techniques that use less sensitive information during the model training process to avoid privacy issues.

And they hope to improve the confidence estimates in selective regression to prevent situations where the model’s confidence is low, but its prediction is correct. This could reduce the workload on humans and further streamline the decision-making process, Sattigeri says.

This research was funded, in part, by the MIT-IBM Watson AI Lab and its member companies Boston Scientific, Samsung, and Wells Fargo, and by the National Science Foundation.

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Silk offers an alternative to some microplastics

Microplastics, tiny particles of plastic that are now found worldwide in the air, water, and soil, are increasingly recognized as a serious pollution threat, and have been found in the bloodstream of animals and people around the world.

Some of these microplastics are intentionally added to a variety of products, including agricultural chemicals, paints, cosmetics, and detergents — amounting to an estimated 50,000 tons a year in the European Union alone, according to the European Chemicals Agency. The EU has already declared that these added, nonbiodegradable microplastics must be eliminated by 2025, so the search is on for suitable replacements, which do not currently exist.

Now, a team of scientists at MIT and elsewhere has developed a system based on silk that could provide an inexpensive and easily manufactured substitute. The new process is described in a paper in the journal Small, written by MIT postdoc Muchun Liu, MIT professor of civil and environmental engineering Benedetto Marelli, and five others at the chemical company BASF in Germany and the U.S.

The microplastics widely used in industrial products generally protect some specific active ingredient (or ingredients) from being degraded by exposure to air or moisture, until the time they are needed. They provide a slow release of the active ingredient for a targeted period of time and minimize adverse effects to its surroundings. For example, vitamins are often delivered in the form of microcapsules packed into a pill or capsule, and pesticides and herbicides are similarly enveloped. But the materials used today for such microencapsulation are plastics that persist in the environment for a long time. Until now, there has been no practical, economical substitute available that would biodegrade naturally.

Much of the burden of environmental microplastics comes from other sources, such as the degradation over time of larger plastic objects such as bottles and packaging, and from the wear of car tires. Each of these sources may require its own kind of solutions for reducing its spread, Marelli says. The European Chemical Agency has estimated that the intentionally added microplastics represent approximately 10-15 percent of the total amount in the environment, but this source may be relatively easy to address using this nature-based biodegradable replacement, he says.

“We cannot solve the whole microplastics problem with one solution that fits them all,” he says. “Ten percent of a big number is still a big number. … We’ll solve climate change and pollution of the world one percent at a time.”

Unlike the high-quality silk threads used for fine fabrics, the silk protein used in the new alternative material is widely available and less expensive, Liu says. While silkworm cocoons must be painstakingly unwound to produce the fine threads needed for fabric, for this use, non-textile-quality cocoons can be used, and the silk fibers can simply be dissolved using a scalable water-based process. The processing is so simple and tunable that the resulting material can be adapted to work on existing manufacturing equipment, potentially providing a simple “drop in” solution using existing factories.

Silk is recognized as safe for food or medical use, as it is nontoxic and degrades naturally in the body. In lab tests, the researchers demonstrated that the silk-based coating material could be used in existing, standard spray-based manufacturing equipment to make a standard water-soluble microencapsulated herbicide product, which was then tested in a greenhouse on a corn crop. The test showed it worked even better than an existing commercial product, inflicting less damage to the plants, Liu says.

While other groups have proposed degradable encapsulation materials that may work at a small laboratory scale, Marelli says, “there is a strong need to achieve encapsulation of high-content actives to open the door to commercial use. The only way to have an impact is where we can not only replace a synthetic polymer with a biodegradable counterpart, but also achieve performance that is the same, if not better.”

The secret to making the material compatible with existing equipment, Liu explains, is in the tunability of the silk material. By precisely adjusting the polymer chain arrangements of silk materials and addition of a surfactant, it is possible to fine-tune the properties of the resulting coatings once they dry out and harden. The material can be hydrophobic (water-repelling) even though it is made and processed in a water solution, or it can be hydrophilic (water-attracting), or anywhere in between, and for a given application it can be made to match the characteristics of the material it is being used to replace.

In order to arrive at a practical solution, Liu had to develop a way of freezing the forming droplets of encapsulated materials as they were forming, to study the formation process in detail. She did this using a special spray-freezing system, and was able to observe exactly how the encapsulation works in order to control it better. Some of the encapsulated “payload” materials, whether they be pesticides or nutrients or enzymes, are water-soluble and some are not, and they interact in different ways with the coating material.

“To encapsulate different materials, we have to study how the polymer chains interact and whether they are compatible with different active materials in suspension,” she says. The payload material and the coating material are mixed together in a solution and then sprayed. As droplets form, the payload tends to be embedded in a shell of the coating material, whether that’s the original synthetic plastic or the new silk material.

The new method can make use of low-grade silk that is unusable for fabrics, and large quantities of which are currently discarded because they have no significant uses, Liu says. It can also use used, discarded silk fabric, diverting that material from being disposed of in landfills.

Currently, 90 percent of the world’s silk production takes place in China, Marelli says, but that’s largely because China has perfected the production of the high-quality silk threads needed for fabrics. But because this process uses bulk silk and has no need for that level of quality, production could easily be ramped up in other parts of the world to meet local demand if this process becomes widely used, he says.

"This elegant and clever study describes a sustainable and biodegradable silk-based replacement for microplastic encapsulants, which are a pressing environmental challenge,” says Alon Gorodetsky, an associate professor of chemical and biomolecular engineering at the University of California at Irvine, who was not associated with this research. “The modularity of the described materials and the scalability of the manufacturing processes are key advantages that portend well for translation to real-world applications.”

This process “represents a potentially highly significant advance in active ingredient delivery for a range of industries, particularly agriculture,” says Jason White, director of the Connecticut Agricultural Experiment Station, who also was not associated with this work. “Given the current and future challenges related to food insecurity, agricultural production, and a changing climate, novel strategies such as this are greatly needed.”

The research team also included Pierre-Eric Millard, Ophelie Zeyons, Henning Urch, Douglas Findley and Rupert Konradi from the BASF corporation, in Germany and in the U.S. The work was supported by BASF through the Northeast Research Alliance (NORA).

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Fusion’s newest ambassador

When high school senior Tuba Balta emailed MIT Plasma Science and Fusion Center (PSFC) Director Dennis Whyte in February, she was not certain she would get a response. As part of her final semester at BASIS Charter School, in Washington, she had been searching unsuccessfully for someone to sponsor an internship in fusion energy, a topic that had recently begun to fascinate her because “it’s not figured out yet.” Time was running out if she was to include the internship as part of her senior project.

“I never say ‘no’ to a student,” says Whyte, who felt she could provide a youthful perspective on communicating the science of fusion to the general public.

Posters explaining the basics of fusion science were being considered for the walls of a PSFC lounge area, a space used to welcome visitors who might not know much about the center’s focus: What is fusion? What is plasma? What is magnetic confinement fusion? What is a tokamak?

Why couldn’t Balta be tasked with coming up with text for these posters, written specifically to be understandable, even intriguing, to her peers?

Meeting the team

Although most of the internship would be virtual, Balta visited MIT to meet Whyte and others who would guide her progress. A tour of the center showed her the past and future of the PSFC, one lab area revealing on her left the remains of the decades-long Alcator C-Mod tokamak and on her right the testing area for new superconducting magnets crucial to SPARC, designed in collaboration with MIT spinoff Commonwealth Fusion Systems.

With Whyte, graduate student Rachel Bielajew, and Outreach Coordinator Paul Rivenberg guiding her content and style, Balta focused on one of eight posters each week. Her school also required her to keep a weekly blog of her progress, detailing what she was learning in the process of creating the posters.

Finding her voice

Balta admits that she was not looking forward to this part of the school assignment. But she decided to have fun with it, adopting an enthusiastic and conversational tone, as if she were sitting with friends around a lunch table. Each week, she was able to work out what she was composing for her posters and her final project by trying it out on her friends in the blog.

Her posts won praise from her schoolmates for their clarity, as when in Week 3 she explained the concept of turbulence as it relates to fusion research, sending her readers to their kitchen faucets to experiment with the pressure and velocity of running tap water.

The voice she found through her blog served her well during her final presentation about fusion at a school expo for classmates, parents, and the general public.

“Most people are intimidated by the topic, which they shouldn't be,” says Balta. “And it just made me happy to help other people understand it.”

Her favorite part of the internship? “Getting to talk to people whose papers I was reading and ask them questions. Because when it comes to fusion, you can’t just look it up on Google.”

Awaiting her first year at the University of Chicago, Balta reflects on the team spirit she experienced in communicating with researchers at the PSFC.

“I think that was one of my big takeaways,” she says, “that you have to work together. And you should, because you're always going to be missing some piece of information; but there's always going to be somebody else who has that piece, and we can all help each other out.”

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School of Engineering Awards for 2022

The MIT School of Engineering recently announced its 2022 awards, honoring outstanding faculty, graduate, and undergraduate students.

The Bose Award for Excellence in Teaching, given to a faculty member whose contributions have been characterized by dedication, care, and creativity, was presented to Michael Short, Class of ’42 Associate Professor in Nuclear Science and Engineering.

The Junior Bose Award, for an outstanding contributor to education from among the junior faculty of the School of Engineering, went to Irmgard Bischofberger, the Class of 1942 Career Development Professor in Mechanical Engineering.

Ruth and Joel Spira Awards for Excellence in Teaching are awarded annually to one faculty member in each of three departments — Electrical Engineering and Computer Science, Mechanical Engineering, and Nuclear Science and Engineering — to acknowledge “the tradition of high-quality engineering education at MIT.” A fourth award rotates among the School of Engineering’s five other academic departments. This year's recipients were:

• George Barbastathis, Singapore Research Professor of Optics and professor of mechanical engineering
   
• Phillip Isola, Bonnie and Marty (1964) Tenenbaum Career Development Assistant Professor
   
• Nuno Loureiro, professor of nuclear science and engineering and professor of physics
   
• Kevin O’Brien, Emanuel E. Landsman (1958) Career Development Professor and assistant professor

The Barry M. Goldwater Scholarship, given to students who exhibit an outstanding potential and intend to pursue careers in mathematics, the natural sciences, or engineering disciplines that contribute significantly to technological advances in the United States, was awarded to engineering students Zoë Marschner and Charlotte Wickert. 

The Henry Ford II Award, presented to a senior engineering student who has maintained a cumulative average of 5.0 at the end of their seventh term and who has exceptional potential for leadership in the profession of engineering and in society, was presented to Sreya Vangara '22, who double majored in mechanical engineering and electrical engineering and computer science.

The Capers and Marion McDonald Award for Excellence in Mentoring and Advising, awarded to a faculty member who has demonstrated a lasting commitment to the personal and professional development of others, was presented to Colette Heald, The Germeshausen Professor in the Department of Civil and Environmental Engineering.

The Graduate Student Extraordinary Teaching and Mentoring Award, given to a graduate student in the School of Engineering who has demonstrated extraordinary teaching and mentoring as a teaching or research assistant, was presented to Keegan Mendez. 

The newly launched School of Engineering Distinguished Educator Award recognizing outstanding contributions to undergraduate and/or graduate education by members of its faculty and teaching staff (lecturer or instructor), was awarded to Barbara Hughey.

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MIT Governance Lab hosts speaker series on governance innovation

Late this spring, the MIT Governance Lab (MIT GOV/LAB) hosted a pair of online conversations between public service leaders about governance innovation. Discussion topics included how governments can be motivated to innovate and what role design can play in reforming government and public services. 

MIT GOV/LAB is an applied research group directed by Lily L. Tsai, Ford Professor of Political Science and chair of the MIT faculty. The speaker series is part of a larger initiative with the goal of using theories from behavioral political science, experimental social science, design thinking, and evaluation to design governance solutions that are more responsive to citizens' needs. 

As part of this initiative, MIT GOV/LAB, in collaboration with local partners, organized governance innovation boot camps in Sierra Leone and Nigeria in 2021. Following the boot camp in Ekiti state, Nigeria, the state’s ministry of health has been developing a solution to improve the quality of health care, with guidance from MIT GOV/LAB and local partners. 

The speaker sessions were attended by academics and students from MIT and other institutions, as well as members of civil society organizations and government officials from around the world. “We’re really trying to start a conversation about innovation in governance … that we hope will carry into ministries and design rooms, into academia and civil society organizations,” said Carlos Centeno, series moderator and MIT GOV/LAB associate director of innovation, during one of the sessions.  

Starting innovation by building trust

The first conversation, which took place on May 25, featured Jumoke Oduwole, special advisor to the president on ease of doing business in Nigeria, and Ademide Adefarasin, design director for IDEO.org, a global nonprofit design studio. 

“For me, governance innovation is finding creative, pragmatic ways to achieve set objectives, and also identify problems within government … particularly, solutions that can deliver the highest possible impact,” Oduwole said during the discussion. 

Oduwole is the executive secretary of the Presidential Enabling Business Environment Council (PEBEC), which aims to reduce the time and money needed for small and medium-sized businesses to work with the government, as well as increase government transparency. She said that in order to start innovating, governments need to have strong relationships with the private sector, and she listed understanding power dynamics, coalition building, and listening and empathizing as strategies for building these relationships.

PEBEC is comprised of ministers and legislators, as well as members of the judiciary, private sector, and state governments. Oduwole said that because the council has been a collaborative effort, they’ve “been able to start denting some of the trust deficit” between the private sector and government. 

Bringing a design approach to the government

Adefarasin shared thoughts on what governments can learn from the type of design work she does in the private sector. She emphasized that governments need to be able to accept failure. In order to make failure more palatable, she suggested “breaking down your solution into the smallest elements and figuring out what is a low-fidelity way that I can test this out.”

Fernando Ma, design director for La Victoria Lab in Peru, echoed this sentiment during the second session, held on June 2, saying that if governments are afraid of failure, they can divide the problem into “smaller bets that have lower risk.”

“Ultimately, it seems it comes down to a phased approach where we design for smaller problems with the biggest learnings so we can continue to iterate and bring the best possible solution to citizens,” Tsai said to speakers prior to the session. 

Arvind Gupta, founder of the Digital India Foundation and former CEO of MyGov India, a government-run citizen engagement platform, said during the second session that small victories can be won by incorporating citizens in the decision-making process. “When people come together to solve problems, what happens is you get global feedback at scale,” creating more stakeholders in the project.  

What does it take to sustain innovation in governments and communities?

Once this innovation gets started, how is it sustained, particularly when there’s opposition to the innovation? In Adefarasin’s experience, people push back against an innovation when they’re covering up a fear of something else. “In order to sustain that innovation, it’s really getting to the core of what it is they’re afraid of,” she said. 

Centeno said during the second session that in order for bureaucrats to be motivated to innovate, the project has to have value not just for the citizens, but the people doing the work in the government as well. Ma said that part of convincing governments to take on a project is showing them its potential economic impact, such as how it might save the government money. But he added that it’s also important to focus on the political benefits — how the project might improve the government’s public image. 

Gupta said that MyGov India was able to successfully sustain the impact of their Covid-19 vaccination platform by making it open and accessible to other countries and by transforming it into a platform for general vaccinations. “Innovation now is ‘how do you reprovision current technology for other purposes,’” he said. 

These government services need to not only survive over time but also across transitions of power. Gupta said that the projects that survive transitions are the ones with a user base of citizens dependent on it. Centeno concluded, “governments should be creating products that if they disappeared, people would really miss them.” 

In the next phase of its governance innovation initiative, MIT GOV/LAB is training two design researchers. One will be embedded in Nigeria with PEBEC, the other in Sierra Leone with the Freetown City Council. Another team will be developing research with the country’s Directorate of Science, Innovation and Technology. 

de MIT News https://ift.tt/bXu3AFi