Increasing equity through educational technology

Justin Reich was ready to observe a teacher integrating technology into her lesson plan at a school in rural New Hampshire. Her school had bought the laptops, Reich says. She had reserved them. They were charged. All of the kids were logged in. The power was on in the building. The wireless network was working. The projector bulb was working. The screen was working. But when the teacher went to plug the projector into the wall, the electrical socket fell behind the drywall, foiling her attempted lesson plan. “New technologies have tremendous potential to improve student learning,” Reich says, “but many pieces in a complex system need to be working seamlessly to make this happen.”

Reich, an assistant professor in MIT’s Comparative Media Studies/Writing Program (CMS/W), has remained excited about the possibilities that constantly evolving technologies have brought to the learning process over the last few decades. But while many believe that the free and low-cost learning tools becoming available have huge potential to lift up students from low-income families, he’s found that, in truth, this educational technology still benefits the affluent the most.

“I think people underestimate barriers,” Reich says. “Many educators get into the work because they want to create a more equitable world. But educational settings often end up reproducing social inequalities and social hierarchies.”

Through his work as executive director at the MIT Teaching Systems Lab, which now straddles CMS/W and the Office of Digital Learning, Reich works toward finding educational models that incorporate technology in ways that actually will increase quality of education and equity for students.

“All over the world, people are looking to see a shift in classroom teaching practice to more active, engaged, inquiry-based collaborative learning,” he says. “And the only way that will happen is if we can dramatically increase the quantity and quality of teacher learning that’s available.”

Having started off as a wilderness medicine instructor, Reich comes from a hands-on teaching background. Now, he makes sure he and his projects are constantly engaging with real classroom settings. He co-founded EdTechTeacher, a professional learning consultancy which focuses on finding thoughtful ways to use technology in teaching and learning. He also keeps conversations going with classroom instructors through his Education Week-hosted blog, EdTechResearcher.

Reich has also created learning tools for teachers through two online courses, Launching Innovation in Schools, done in collaboration with Peter Senge of the Sloan School of Management; and Design Thinking for Leading and Learning. Both courses were funded by Microsoft with a $650,000 grant.

In CMS/W, he looks to explore the field of learning science and the role that media plays in expanding human capacity, particularly in a civic sense.

“We investigate the complex technology-rich classrooms of the future and the systems that we need to help educators thrive in those settings,” he says.

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Making data centers more energy efficient

Most modern websites store data in databases, and since database queries are relatively slow, most sites also maintain so-called cache servers, which list the results of common queries for faster access. A data center for a major web service such as Google or Facebook might have as many as 1,000 servers dedicated just to caching.

Cache servers generally use random-access memory (RAM), which is fast but expensive and power-hungry. This week, at the International Conference on Very Large Databases, researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) are presenting a new system for data center caching that instead uses flash memory, the kind of memory used in most smartphones.

Per gigabyte of memory, flash consumes about 5 percent as much energy as RAM and costs about one-tenth as much. It also has about 100 times the storage density, meaning that more data can be crammed into a smaller space. In addition to costing less and consuming less power, a flash caching system could dramatically reduce the number of cache servers required by a data center.

The drawback to flash is that it’s much slower than RAM. “That’s where the disbelief comes in,” says Arvind, the Charles and Jennifer Johnson Professor in Computer Science Engineering and senior author on the conference paper. “People say, ‘Really? You can do this with flash memory?’ Access time in flash is 10,000 times longer than in DRAM [dynamic RAM].”

But slow as it is relative to DRAM, flash access is still much faster than human reactions to new sensory stimuli. Users won’t notice the difference between a request that takes .0002 seconds to process — a typical round-trip travel time over the internet — and one that takes .0004 seconds because it involves a flash query.

Keeping pace

The more important concern is keeping up with the requests flooding the data center. The CSAIL researchers’ system, dubbed BlueCache, does that by using the common computer science technique of “pipelining.” Before a flash-based cache server returns the result of the first query to reach it, it can begin executing the next 10,000 queries. The first query might take 200 microseconds to process, but the responses to the succeeding ones will emerge at .02-microsecond intervals.

Even using pipelining, however, the CSAIL researchers had to deploy some clever engineering tricks to make flash caching competitive with DRAM caching. In tests, they compared BlueCache to what might be called the default implementation of a flash-based cache server, which is simply a data-center database server configured for caching. (Although slow compared to DRAM, flash is much faster than magnetic hard drives, which it has all but replaced in data centers.) BlueCache was 4.2 times as fast as the default implementation.

Joining Arvind on the paper are first author Shuotao Xu and his fellow MIT graduate student in electrical engineering and computer science Sang-Woo Jun; Ming Liu, who was an MIT graduate student when the work was done and is now at Microsoft Research; Sungjin Lee, an assistant professor of computer science and engineering at the Daegu Gyeongbuk Institute of Science and Technology in Korea, who worked on the project as a postdoc in Arvind’s lab; and Jamey Hicks, a freelance software architect and MIT affiliate who runs the software consultancy Accelerated Tech.

The researchers’ first trick is to add a little DRAM to every BlueCache flash cache — a few megabytes per million megabytes of flash. The DRAM stores a table which pairs a database query with the flash-memory address of the corresponding query result. That doesn’t make cache lookups any faster, but it makes the detection of cache misses — the identification of data not yet imported into the cache — much more efficient.

That little bit of DRAM doesn’t compromise the system’s energy savings. Indeed, because of all of its added efficiencies, BlueCache consumes only 4 percent as much power as the default implementation.

Engineered efficiencies

Ordinarily, a cache system has only three operations: reading a value from the cache, writing a new value to the cache, and deleting a value from the cache. Rather than rely on software to execute these operations, as the default implementation does, Xu developed a special-purpose hardware circuit for each of them, increasing speed and lowering power consumption.

Inside a BlueCache server, the flash memory is connected to the central processor by a wire known as a “bus,” which, like any data connection, has a maximum capacity. BlueCache amasses enough queries to exhaust that capacity before sending them to memory, ensuring that the system is always using communication bandwidth as efficiently as possible.

With all these optimizations, BlueCache is able to perform write operations as efficiently as a DRAM-based system. Provided that each of the query results it’s retrieving is at least eight kilobytes, it’s as efficient at read operations, as well. (Because flash memory returns at least eight kilobytes of data for any request, it’s efficiency falls off for really small query results.)

BlueCache, like most data-center caching systems, is a so-called key-value store, or KV store. In this case, the key is the database query and the value is the response.

"The flash-based KV store architecture developed by Arvind and his MIT team resolves many of the issues that limit the ability of today's enterprise systems to harness the full potential of flash,” says Vijay Balakrishnan, director of the Data Center Performance and Ecosystem program at Samsung Semiconductor’s Memory Solutions Lab. “The viability of this type of system extends beyond caching, since many data-intensive applications use a KV-based software stack, which the MIT team has proven can now be eliminated. By integrating programmable chips with flash and rewriting the software stack, they have demonstrated that a fully scalable, performance-enhancing storage technology, like the one described in the paper, can greatly improve upon prevailing architectures.”

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Robotic system monitors specific neurons

Recording electrical signals from inside a neuron in the living brain can reveal a great deal of information about that neuron’s function and how it coordinates with other cells in the brain. However, performing this kind of recording is extremely difficult, so only a handful of neuroscience labs around the world do it.

To make this technique more widely available, MIT engineers have now devised a way to automate the process, using a computer algorithm that analyzes microscope images and guides a robotic arm to the target cell.

This technology could allow more scientists to study single neurons and learn how they interact with other cells to enable cognition, sensory perception, and other brain functions. Researchers could also use it to learn more about how neural circuits are affected by brain disorders.

“Knowing how neurons communicate is fundamental to basic and clinical neuroscience. Our hope is this technology will allow you to look at what’s happening inside a cell, in terms of neural computation, or in a disease state,” says Ed Boyden, an associate professor of biological engineering and brain and cognitive sciences at MIT, and a member of MIT’s Media Lab and McGovern Institute for Brain Research.

Boyden is the senior author of the paper, which appears in the Aug. 30 issue of Neuron. The paper’s lead author is MIT graduate student Ho-Jun Suk.

Precision guidance

For more than 30 years, neuroscientists have been using a technique known as patch clamping to record the electrical activity of cells. This method, which involves bringing a tiny, hollow glass pipette in contact with the cell membrane of a neuron, then opening up a small pore in the membrane, usually takes a graduate student or postdoc several months to learn. Learning to perform this on neurons in the living mammalian brain is even more difficult.

There are two types of patch clamping: a “blind” (not image-guided) method, which is limited because researchers cannot see where the cells are and can only record from whatever cell the pipette encounters first, and an image-guided version that allows a specific cell to be targeted.

Five years ago, Boyden and colleagues at MIT and Georgia Tech, including co-author Craig Forest, devised a way to automate the blind version of patch clamping. They created a computer algorithm that could guide the pipette to a cell based on measurements of a property called electrical impedance — which reflects how difficult it is for electricity to flow out of the pipette. If there are no cells around, electricity flows and impedance is low. When the tip hits a cell, electricity can’t flow as well and impedance goes up.

Once the pipette detects a cell, it can stop moving instantly, preventing it from poking through the membrane. A vacuum pump then applies suction to form a seal with the cell’s membrane. Then, the electrode can break through the membrane to record the cell’s internal electrical activity.

The researchers achieved very high accuracy using this technique, but it still could not be used to target a specific cell. For most studies, neuroscientists have a particular cell type they would like to learn about, Boyden says.

“It might be a cell that is compromised in autism, or is altered in schizophrenia, or a cell that is active when a memory is stored. That’s the cell that you want to know about,” he says. “You don’t want to patch a thousand cells until you find the one that is interesting.”

To enable this kind of precise targeting, the researchers set out to automate image-guided patch clamping. This technique is difficult to perform manually because, although the scientist can see the target neuron and the pipette through a microscope, he or she must compensate for the fact that nearby cells will move as the pipette enters the brain.

“It’s almost like trying to hit a moving target inside the brain, which is a delicate tissue,” Suk says. “For machines it’s easier because they can keep track of where the cell is, they can automatically move the focus of the microscope, and they can automatically move the pipette.”

By combining several imaging processing techniques, the researchers came up with an algorithm that guides the pipette to within about 25 microns of the target cell. At that point, the system begins to rely on a combination of imagery and impedance, which is more accurate at detecting contact between the pipette and the target cell than either signal alone.

The researchers imaged the cells with two-photon microscopy, a commonly used technique that uses a pulsed laser to send infrared light into the brain, lighting up cells that have been engineered to express a fluorescent protein.

Using this automated approach, the researchers were able to successfully target and record from two types of cells — a class of interneurons, which relay messages between other neurons, and a set of excitatory neurons known as pyramidal cells. They achieved a success rate of about 20 percent, which is comparable to the performance of highly trained scientists performing the process manually.

Unraveling circuits

This technology paves the way for in-depth studies of the behavior of specific neurons, which could shed light on both their normal functions and how they go awry in diseases such as Alzheimer’s or schizophrenia. For example, the interneurons that the researchers studied in this paper have been previously linked with Alzheimer’s. In a recent study of mice, led by Li-Huei Tsai, director of MIT’s Picower Institute for Learning and Memory, and conducted in collaboration with Boyden, it was reported that inducing a specific frequency of brain wave oscillation in interneurons in the hippocampus could help to clear amyloid plaques similar to those found in Alzheimer’s patients.

“You really would love to know what’s happening in those cells,” Boyden says. “Are they signaling to specific downstream cells, which then contribute to the therapeutic result? The brain is a circuit, and to understand how a circuit works, you have to be able to monitor the components of the circuit while they are in action.”

This technique could also enable studies of fundamental questions in neuroscience, such as how individual neurons interact with each other as the brain makes a decision or recalls a memory.

Bernardo Sabatini, a professor of neurobiology at Harvard Medical School, says he is interested in adapting this technique to use in his lab, where students spend a great deal of time recording electrical activity from neurons growing in a lab dish.

“It’s silly to have amazingly intelligent students doing tedious tasks that could be done by robots,” says Sabatini, who was not involved in this study. “I would be happy to have robots do more of the experimentation so we can focus on the design and interpretation of the experiments.”

To help other labs adopt the new technology, the researchers plan to put the details of their approach on their web site, autopatcher.org.

Other co-authors include Ingrid van Welie, Suhasa Kodandaramaiah, and Brian Allen. The research was funded by Jeremy and Joyce Wertheimer, the National Institutes of Health (including the NIH Single Cell Initiative and the NIH Director’s Pioneer Award), the HHMI-Simons Faculty Scholars Program, and the New York Stem Cell Foundation-Robertson Award.

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Robot learns to follow orders like Alexa

Despite what you might see in movies, today’s robots are still very limited in what they can do. They can be great for many repetitive tasks, but their inability to understand the nuances of human language makes them mostly useless for more complicated requests.

For example, if you put a specific tool in a toolbox and ask a robot to “pick it up,” it would be completely lost. Picking it up means being able to see and identify objects, understand commands, recognize that the “it” in question is the tool you put down, go back in time to remember the moment when you put down the tool, and distinguish the tool you put down from other ones of similar shapes and sizes.

Recently researchers from MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have gotten closer to making this type of request easier: In a new paper, they present an Alexa-like system that allows robots to understand a wide range of commands that require contextual knowledge about objects and their environments. They've dubbed the system “ComText,” for “commands in context.”

The toolbox situation above was among the types of tasks that ComText can handle. If you tell the system that “the tool I put down is my tool,” it adds that fact to its knowledge base. You can then update the robot with more information about other objects and have it execute a range of tasks like picking up different sets of objects based on different commands.

“Where humans understand the world as a collection of objects and people and abstract concepts, machines view it as pixels, point-clouds, and 3-D maps generated from sensors,” says CSAIL postdoc Rohan Paul, one of the lead authors of the paper. “This semantic gap means that, for robots to understand what we want them to do, they need a much richer representation of what we do and say.”

The team tested ComText on Baxter, a two-armed humanoid robot developed for Rethink Robotics by former CSAIL director Rodney Brooks.

The project was co-led by research scientist Andrei Barbu, alongside research scientist Sue Felshin, senior research scientist Boris Katz, and Professor Nicholas Roy. They presented the paper at last week’s International Joint Conference on Artificial Intelligence (IJCAI) in Australia.

How it works

Things like dates, birthdays, and facts are forms of “declarative memory.” There are two kinds of declarative memory: semantic memory, which is based on general facts like the “sky is blue,” and episodic memory, which is based on personal facts, like remembering what happened at a party.

Most approaches to robot learning have focused only on semantic memory, which obviously leaves a big knowledge gap about events or facts that may be relevant context for future actions. ComText, meanwhile, can observe a range of visuals and natural language to glean “episodic memory” about an object’s size, shape, position, type and even if it belongs to somebody. From this knowledge base, it can then reason, infer meaning and respond to commands.

“The main contribution is this idea that robots should have different kinds of memory, just like people,” says Barbu. “We have the first mathematical formulation to address this issue, and we’re exploring how these two types of memory play and work off of each other.”

With ComText, Baxter was successful in executing the right command about 90 percent of the time. In the future, the team hopes to enable robots to understand more complicated information, such as multi-step commands, the intent of actions, and using properties about objects to interact with them more naturally.

For example, if you tell a robot that one box on a table has crackers, and one box has sugar, and then ask the robot to “pick up the snack,” the hope is that the robot could deduce that sugar is a raw material and therefore unlikely to be somebody’s “snack.”

By creating much less constrained interactions, this line of research could enable better communications for a range of robotic systems, from self-driving cars to household helpers.

“This work is a nice step towards building robots that can interact much more naturally with people,” says Luke Zettlemoyer, an associate professor of computer science at the University of Washington who was not involved in the research. “In particular, it will help robots better understand the names that are used to identify objects in the world, and interpret instructions that use those names to better do what users ask.”

The work was funded, in part, by the Toyota Research Institute, the National Science Foundation, the Robotics Collaborative Technology Alliance of the U.S. Army, and the Air Force Research Laboratory.

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New robot rolls with the rules of pedestrian conduct

Just as drivers observe the rules of the road, most pedestrians follow certain social codes when navigating a hallway or a crowded thoroughfare: Keep to the right, pass on the left, maintain a respectable berth, and be ready to weave or change course to avoid oncoming obstacles while keeping up a steady walking pace.

Now engineers at MIT have designed an autonomous robot with “socially aware navigation,” that can keep pace with foot traffic while observing these general codes of pedestrian conduct.

In drive tests performed inside MIT’s Stata Center, the robot, which resembles a knee-high kiosk on wheels, successfully avoided collisions while keeping up with the average flow of pedestrians. The researchers have detailed their robotic design in a paper that they will present at the IEEE Conference on Intelligent Robots and Systems in September.

“Socially aware navigation is a central capability for mobile robots operating in environments that require frequent interactions with pedestrians,” says Yu Fan “Steven” Chen, who led the work as a former MIT graduate student and is the lead author of the study. “For instance, small robots could operate on sidewalks for package and food delivery. Similarly, personal mobility devices could transport people in large, crowded spaces, such as shopping malls, airports, and hospitals.”

Chen’s co-authors are graduate student Michael Everett, former postdoc Miao Liu, and Jonathan How, the Richard Cockburn Maclaurin Professor of Aeronautics and Astronautics at MIT.

Social drive

In order for a robot to make its way autonomously through a heavily trafficked environment, it must solve four main challenges: localization (knowing where it is in the world), perception (recognizing its surroundings), motion planning (identifying the optimal path to a given destination), and control (physically executing its desired path).

Chen and his colleagues used standard approaches to solve the problems of localization and perception. For the latter, they outfitted the robot with off-the-shelf sensors, such as webcams, a depth sensor, and a high-resolution lidar sensor. For the problem of localization, they used open-source algorithms to map the robot’s environment and determine its position. To control the robot, they employed standard methods used to drive autonomous ground vehicles.

“The part of the field that we thought we needed to innovate on was motion planning,” Everett says. “Once you figure out where you are in the world, and know how to follow trajectories, which trajectories should you be following?”

That’s a tricky problem, particularly in pedestrian-heavy environments, where individual paths are often difficult to predict. As a solution, roboticists sometimes take a trajectory-based approach, in which they program a robot to compute an optimal path that accounts for everyone's desired trajectories. These trajectories must be inferred from sensor data, because people don't explicitly tell the robot where they are trying to go. 

“But this takes forever to compute. Your robot is just going to be parked, figuring out what to do next, and meanwhile the person’s already moved way past it before it decides ‘I should probably go to the right,’” Everett says. “So that approach is not very realistic, especially if you want to drive faster.”

Others have used faster, “reactive-based” approaches, in which a robot is programmed with a simple model, using geometry or physics, to quickly compute a path that avoids collisions.

The problem with reactive-based approaches, Everett says, is the unpredictability of human nature — people rarely stick to a straight, geometric path, but rather weave and wander, veering off to greet a friend or grab a coffee. In such an unpredictable environment, such robots tend to collide with people or look like they are being pushed around by avoiding people excessively.

 “The knock on robots in real situations is that they might be too cautious or aggressive,” Everett says. “People don’t find them to fit into the socially accepted rules, like giving people enough space or driving at acceptable speeds, and they get more in the way than they help.”

Training days

The team found a way around such limitations, enabling the robot to adapt to unpredictable pedestrian behavior while continuously moving with the flow and following typical social codes of pedestrian conduct.

They used reinforcement learning, a type of machine learning approach, in which they performed computer simulations to train a robot to take certain paths, given the speed and trajectory of other objects in the environment. The team also incorporated social norms into this offline training phase, in which they encouraged the robot in simulations to pass on the right, and penalized the robot when it passed on the left.

“We want it to be traveling naturally among people and not be intrusive,” Everett says. “We want it to be following the same rules as everyone else.”

The advantage to reinforcement learning is that the researchers can perform these training scenarios, which take extensive time and computing power, offline. Once the robot is trained in simulation, the researchers can program it to carry out the optimal paths, identified in the simulations, when the robot recognizes a similar scenario in the real world.

The researchers enabled the robot to assess its environment and adjust its path, every one-tenth of a second. In this way, the robot can continue rolling through a hallway at a typical walking speed of 1.2 meters per second, without pausing to reprogram its route.

“We’re not planning an entire path to the goal — it doesn’t make sense to do that anymore, especially if you’re assuming the world is changing,” Everett says. “We just look at what we see, choose a velocity, do that for a tenth of a second, then look at the world again, choose another velocity, and go again. This way, we think our robot looks more natural, and is anticipating what people are doing.”

Crowd control

Everett and his colleagues test-drove the robot in the busy, winding halls of MIT’s Stata Building, where the robot was able to drive autonomously for 20 minutes at a time. It rolled smoothly with the pedestrian flow, generally keeping to the right of hallways, occasionally passing people on the left, and avoiding any collisions.

“We wanted to bring it somewhere where people were doing their everyday things, going to class, getting food, and we showed we were pretty robust to all that,” Everett says. “One time there was even a tour group, and it perfectly avoided them.”

Everett says going forward, he plans to explore how robots might handle crowds in a pedestrian environment.

“Crowds have a different dynamic than individual people, and you may have to learn something totally different if you see five people walking together,” Everett says. “There may be a social rule of, ‘Don’t move through people, don’t split people up, treat them as one mass.’ That’s something we’re looking at in the future.”

This research was funded by Ford Motor Company.  

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Back to school special

As part of this year’s freshman orientation at MIT, new students encountered the typical lineup of takeaways: booklets and brochures, a list of 101 things to do before they graduate, lots of T-shirts, pens, etc. For the first time, however, they were also given a completely new version of the old campus staple: the backpack.

Heaped into an uneven pyramid in the Coffeehouse, a room on the third floor of the Stratton Student Center that serves as orientation headquarters, there were dozens of bags — all with a seemingly identical black, white, and grey plaid design. They looked unassuming until Yoel Fink, professor of materials science, started talking to students about them: “These bags are the world’s first programmable backpacks!” he effused. The students leaned in closer, intrigued.

“We express our identity through the fabrics we wear,” said Fink. “And while each one of us is truly unique, the stuff we wear is certainly not,” he added. What if it were? What if our fabrics — say, the ones making up our backpacks — could communicate?

Thanks to Fink, now they can. A unique code is woven into the fabric material of the backpack given to each first-year student. Unlike a QR code, this fabric-based coding system is subtle to the eye but immediately recognizable by an app called AFFOA LOOKS. The owner can link his or her backpack to their mobile device and program it to display a song, a cause, or anything the owner chooses to share. Anyone with the app can scan or “look” the bag and receive this information (in Fink’s case, it’s his business card and a customized song of the day).

Fink is a co-inventor of the tech behind the bag and the CEO of Advanced Functional Fabrics of America (AFFOA). Located close to the MIT campus, the nonprofit institute was recently created through a $300 million proposal backed by federal and state governments, as well as academic and corporate partners, with the mission of creating functional fabrics that deliver value-added services while facilitating domestic manufacturing and economic growth in this area.

“The fabrics we wear have been functionally the same for centuries,” Fink explained to a packed house in Kresge Auditorium later in the day. “What we wanted to create was a fabric that is as unique as you are.” The manufacturing process employs special looms and materials, he explained. And the bags themselves are exclusive — not sold anywhere. They were made by Inman Mills in South Carolina just for the members of MIT’s Class of 2021.

The plan to give out the backpacks was first proposed by Katharina Ribbeck, a professor in the Department of Biological Engineering, who pointed out that the pack could help facilitate interactions and learning among incoming students. Her proposal was supported by Ian A. Waitz, MIT’s newly appointed vice chancellor and former dean of engineering, who saw it as an opportunity to give new students a way to directly engage with novel technology and each another (and a free place to store their gear and books). There are already plans for a hack-the-pack event during January’s Independent Activities Period.

For Fink, the functional aspect of the backpack is social in another way. Every first-year student he speaks with leaves with a broader understanding of the term “software” (as in soft wear). He wants incoming students to glean that manufacturing is undergoing a transformation; it’s as high-tech and as hot as coding, artificial intelligence, gene editing, and autonomy. It’s an option, a pursuit, a place for passion and a way for self-expression and creativity.

“If you are coming to MIT for the fist time,” he said, waving at the pile of coded bags behind him, “this is what is the place is all about. It’s about innovation and making a difference.” 

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Celebrating Walker Memorial’s 100th year

Labor Day Weekend of 1917 marked the opening of MIT’s new student center, Walker Memorial — although not for its intended purpose. As part of the Institute’s contribution to the World War I war effort, 400 naval aviation students moved into the new building, taking over the gymnasium and balconies of the big hall for dormitory space, as well as the rooms on the second and third floors that had been built for student and faculty recreational use.

The building’s namesake, former MIT President Francis Amasa Walker, is still the only MIT president to have served as a military general, so he likely would have approved. As The Tech of the day reported: “the building erected in memory of him will be devoted to military purposes before becoming what it is destined to be, the social center of Technology.”

A hub for campus activities was considered the greatest tribute to President Walker, who was beloved by both students and alumni for his efforts to improve student life on MIT’s cramped Boston campus. But making that ideal student center a reality took two decades.

When Walker died in 1897, the Alumni Association formed a committee to plan and fund the project, and, by 1902, the funds and land had been set aside. The project was postponed, though, when MIT announced plans to relocate from Boston. It wasn't until the Institute’s move to Cambridge 14 years later that construction on Walker Memorial finally became possible.

It became a landmark for MIT students began even before it was finished. On Feb. 9, 1917, the Class of 1918 gathered for “the first Class Photograph ever taken on the steps of Walker Memorial ... this spot will probably be chosen as a place to take all class pictures in the future,” the 1918 edition of Technique reported. The tradition holds generations later: Walker's steps are still used for alumni group portraits, most notably that of the 50th reunion class before they march in the Commencement procession as official Cardinal and Gray Society members in their distinctive red jackets.

After the Army and Navy aviation cadets moved out in January of 1919, the building was formally inaugurated as a student center. Henry A. Morss, Class of 1893 and then president of the Alumni Association, formally presented Walker Memorial to MIT “for the students that the student body would thereby be united and the Technology spirit be fostered.” 

For many of those who have passed through Walker Memorial over the past 100 years, the most enduring images remain the murals in Morss Hall, which were painted by Edwin Howland Blashfield of the Class of 1869. Created and installed between 1923 and 1930, their allegories of alma mater receiving homage from scientific and academic disciplines have watched over countless MIT community functions, from dining hall breakfasts to the Assembly Ball and more.

For most MIT alumni and students, Walker Memorial holds indelible memories. A century after its completion, the tribute to President Walker has been realized in the best possible way — with the building continuing to serve as a community gathering place.

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