Technique helps robots find the front door

In the not too distant future, robots may be dispatched as last-mile delivery vehicles to drop your takeout order, package, or meal-kit subscription at your doorstep — if they can find the door.

Standard approaches for robotic navigation involve mapping an area ahead of time, then using algorithms to guide a robot toward a specific goal or GPS coordinate on the map. While this approach might make sense for exploring specific environments, such as the layout of a particular building or planned obstacle course, it can become unwieldy in the context of last-mile delivery.

Imagine, for instance, having to map in advance every single neighborhood within a robot’s delivery zone, including the configuration of each house within that neighborhood along with the specific coordinates of each house’s front door. Such a task can be difficult to scale to an entire city, particularly as the exteriors of houses often change with the seasons. Mapping every single house could also run into issues of security and privacy.

Now MIT engineers have developed a navigation method that doesn’t require mapping an area in advance. Instead, their approach enables a robot to use clues in its environment to plan out a route to its destination, which can be described in general semantic terms, such as “front door” or “garage,” rather than as coordinates on a map. For example, if a robot is instructed to deliver a package to someone's front door, it might start on the road and see a driveway, which it has been trained to recognize as likely to lead toward a sidewalk, which in turn is likely to lead to the front door.

The new technique can greatly reduce the time a robot spends exploring a property before identifying its target, and it doesn’t rely on maps of specific residences. 

“We wouldn’t want to have to make a map of every building that we’d need to visit,” says Michael Everett, a graduate student in MIT’s Department of Mechanical Engineering. “With this technique, we hope to drop a robot at the end of any driveway and have it find a door.”

Everett will present the group’s results this week at the International Conference on Intelligent Robots and Systems. The paper, which is co-authored by Jonathan How, professor of aeronautics and astronautics at MIT, and Justin Miller of the Ford Motor Company, is a finalist for “Best Paper for Cognitive Robots.”

“A sense of what things are”

In recent years, researchers have worked on introducing natural, semantic language to robotic systems, training robots to recognize objects by their semantic labels, so they can visually process a door as a door, for example, and not simply as a solid, rectangular obstacle.

“Now we have an ability to give robots a sense of what things are, in real-time,” Everett says.

Everett, How, and Miller are using similar semantic techniques as a springboard for their new navigation approach, which leverages pre-existing algorithms that extract features from visual data to generate a new map of the same scene, represented as semantic clues, or context.

In their case, the researchers used an algorithm to build up a map of the environment as the robot moved around, using the semantic labels of each object and a depth image. This algorithm is called semantic SLAM (Simultaneous Localization and Mapping).

While other semantic algorithms have enabled robots to recognize and map objects in their environment for what they are, they haven’t allowed a robot to make decisions in the moment while navigating a new environment, on the most efficient path to take to a semantic destination such as a “front door.”

“Before, exploring was just, plop a robot down and say ‘go,’ and it will move around and eventually get there, but it will be slow,” How says.

The cost to go

The researchers looked to speed up a robot’s path-planning through a semantic, context-colored world. They developed a new “cost-to-go estimator,” an algorithm that converts a semantic map created by preexisting SLAM algorithms into a second map, representing the likelihood of any given location being close to the goal.

“This was inspired by image-to-image translation, where you take a picture of a cat and make it look like a dog,” Everett says. “The same type of idea happens here where you take one image that looks like a map of the world, and turn it into this other image that looks like the map of the world but now is colored based on how close different points of the map are to the end goal.”

This cost-to-go map is colorized, in gray-scale, to represent darker regions as locations far from a goal, and lighter regions as areas that are close to the goal. For instance, the sidewalk, coded in yellow in a semantic map, might be translated by the cost-to-go algorithm as a darker region in the new map, compared with a driveway, which is progressively lighter as it approaches the front door — the lightest region in the new map.

The researchers trained this new algorithm on satellite images from Bing Maps containing 77 houses from one urban and three suburban neighborhoods. The system converted a semantic map into a cost-to-go map, and mapped out the most efficient path, following lighter regions in the map, to the end goal. For each satellite image, Everett assigned semantic labels and colors to context features in a typical front yard, such as grey for a front door, blue for a driveway, and green for a hedge.

During this training process, the team also applied masks to each image to mimic the partial view that a robot’s camera would likely have as it traverses a yard.

“Part of the trick to our approach was [giving the system] lots of partial images,” How explains. “So it really had to figure out how all this stuff was interrelated. That’s part of what makes this work robustly.”

The researchers then tested their approach in a simulation of an image of an entirely new house, outside of the training dataset, first using the preexisting SLAM algorithm to generate a semantic map, then applying their new cost-to-go estimator to generate a second map, and path to a goal, in this case, the front door.

The group’s new cost-to-go technique found the front door 189 percent faster than classical navigation algorithms, which do not take context or semantics into account, and instead spend excessive steps exploring areas that are unlikely to be near their goal.

Everett says the results illustrate how robots can use context to efficiently locate a goal, even in unfamiliar, unmapped environments.

“Even if a robot is delivering a package to an environment it’s never been to, there might be clues that will be the same as other places it’s seen,” Everett says. “So the world may be laid out a little differently, but there’s probably some things in common.”

This research is supported, in part, by the Ford Motor Company.

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What makes an image memorable? Ask a computer

From the "Mona Lisa" to the "Girl with a Pearl Earring," some images linger in the mind long after others have faded. Ask an artist why, and you might hear some generally-accepted principles for making memorable art. Now there’s an easier way to learn: ask an artificial intelligence model to draw an example. 

A new study using machine learning to generate images ranging from a memorable cheeseburger to a forgettable cup of coffee shows in close detail what makes a portrait or scene stand out. The images that human subjects in the study remembered best featured bright colors, simple backgrounds, and subjects that were centered prominently in the frame. Results were presented this week at the International Conference on Computer Vision. 

“A picture is worth a thousand words,” says the study’s co-senior author Phillip Isola, the Bonnie and Marty (1964) Tenenbaum CD Assistant Professor of Electrical Engineering and Computer Science at MIT. “A lot has been written about memorability, but this method lets us actually visualize what memorability looks like. It gives us a visual definition for something that’s hard to put into words."

The work builds on an earlier model, MemNet, which rates the memorability of an image and highlights the features in the picture influencing its decision. MemNet’s predictions are based on the results of an online study in which 60,000 images were shown to human subjects and ranked by how easily they were remembered.

The model in the current study, GANalyze, uses a machine learning technique called generative adversarial networks, or GANs, to visualize a single image as it inches its way from "meh" to memorable. GANalyze lets viewers visualize the incremental transformation of, say, a blurry panda lost in the bamboo into a panda that dominates the frame, its black eyes, ears, and paws contrasting sharply and adorably with its white mug.

The image-riffing GAN has three modules. An assessor, based on MemNet, turns the memorability knob on a target image and calculates how to achieve the desired effect. A transformer executes its instructions, and a generator outputs the final image. 

The progression has the dramatic feel of a time-lapse image. A cheeseburger shifted to the far end of the memorability scale looks fatter, brighter, and, as the authors note, “tastier,” than its earlier incarnations. A ladybug looks shinier and more purposeful. In an unexpected twist, a pepper on the vine turns chameleon-like from green to red. 

The researchers also looked at which features influence memorability most. In online experiments, human subjects were shown images of varying memorability and asked to flag any repeats. The duplicates that were stickiest, it turns out, featured subjects closer up, making animals or objects in the frame appear larger. The next most important factors were brightness, having the subject centered in the frame, and in a square or circular shape.

“The human brain evolved to focus most on these features, and that’s what the GAN picks up on,” says study co-author Lore Goetschalckx, a visiting graduate student from Katholieke Universiteit Leuven in Belgium.

The researchers also reconfigured GANanalyze to generate images of varying aesthetic and emotional appeal. They found that images rated higher on aesthetic and emotional grounds were brighter, more colorful, and had a shallow depth of field that blurred the background, much like the most memorable pictures. However, the most aesthetic images were not always memorable.

GANalyze has a number of potential applications, the researchers say. It could be used to detect, and even treat, memory loss by enhancing objects in an augmented reality system. 

“Instead of using a drug to enhance memory, you might enhance the world through an augmented-reality device to make easily misplaced items like keys stand out,” says study co-senior author Aude Oliva, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and executive director of the MIT Quest for Intelligence.  

GANalyze could also be used to create unforgettable graphics to help readers retain information. “It could revolutionize education,” says Oliva. Finally, GANs are already starting to be used to generate synthetic, realistic images of the world to help train automated systems to recognize places and objects they are unlikely to encounter in real life. 

Generative models offer new, creative ways for humans and machines to collaborate. Study co-author Alex Andonian, a graduate student at MIT’s Department of Electrical Engineering and Computer Science, says that's why he has chosen to make them the focus of his PhD.

“Design software lets you adjust the brightness of an image, but not its overall memorability or aesthetic appeal — GANs let you do that,” he says. “We’re just starting to scratch the surface of what these models can do.”   

The study was funded by the U.S. National Science Foundation.

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What makes an image memorable? Ask a computer scientist

From the "Mona Lisa" to the "Girl with a Pearl Earring," some images linger in the mind long after others have faded. Ask an artist why, and you might hear some generally-accepted principles for making memorable art. Now there’s an easier way to learn: ask an artificial intelligence model to draw an example. 

A new study using machine learning to generate images ranging from a memorable cheeseburger to a forgettable cup of coffee shows in close detail what makes a portrait or scene stand out. The images that human subjects in the study remembered best featured bright colors, simple backgrounds, and subjects that were centered prominently in the frame. Results were presented this week at the International Conference on Computer Vision. 

“A picture is worth a thousand words,” says the study’s co-senior author Phillip Isola, the Bonnie and Marty (1964) Tenenbaum CD Assistant Professor of Electrical Engineering and Computer Science at MIT. “A lot has been written about memorability, but this method lets us actually visualize what memorability looks like. It gives us a visual definition for something that’s hard to put into words."

The work builds on an earlier model, MemNet, which rates the memorability of an image and highlights the features in the picture influencing its decision. MemNet’s predictions are based on the results of an online study in which 60,000 images were shown to human subjects and ranked by how easily they were remembered.

The model in the current study, GANalyze, uses a machine learning technique called generative adversarial networks, or GANs, to visualize a single image as it inches its way from "meh" to memorable. GANalyze lets viewers visualize the incremental transformation of, say, a blurry panda lost in the bamboo into a panda that dominates the frame, its black eyes, ears, and paws contrasting sharply and adorably with its white mug.

The image-riffing GAN has three modules. An assessor, based on MemNet, turns the memorability knob on a target image and calculates how to achieve the desired effect. A transformer executes its instructions, and a generator outputs the final image. 

The progression has the dramatic feel of a time-lapse image. A cheeseburger shifted to the far end of the memorability scale looks fatter, brighter, and, as the authors note, “tastier,” than its earlier incarnations. A ladybug looks shinier and more purposeful. In an unexpected twist, a pepper on the vine turns chameleon-like from green to red. 

The researchers also looked at which features influence memorability most. In online experiments, human subjects were shown images of varying memorability and asked to flag any repeats. The duplicates that were stickiest, it turns out, featured subjects closer up, making animals or objects in the frame appear larger. The next most important factors were brightness, having the subject centered in the frame, and in a square or circular shape.

“The human brain evolved to focus most on these features, and that’s what the GAN picks up on,” says study co-author Lore Goetschalckx, a graduate student at Katholieke Universiteit Leuven in Belgium.

The researchers also reconfigured GANanalyze to generate images of varying aesthetic and emotional appeal. They found that images rated higher on aesthetic and emotional grounds were brighter, more colorful, and had a shallow depth of field that blurred the background, much like the most memorable pictures. However, the most aesthetic images were not always memorable.

GANalyze has a number of potential applications, the researchers say. It could be used to detect, and even treat, memory loss by enhancing objects in an augmented reality system. 

“Instead of using a drug to enhance memory, you might enhance the world through an augmented-reality device to make easily misplaced items like keys stand out,” says study co-author Aude Oliva, a principal research scientist at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) and executive director of the MIT Quest for Intelligence.  

GANalyze could also be used to create unforgettable graphics to help readers retain information. “It could revolutionize education,” says Oliva. Finally, GANs are already starting to be used to generate synthetic, realistic images of the world to help train automated systems to recognize places and objects they are unlikely to encounter in real life. 

Generative models offer new, creative ways for humans and machines to collaborate. Study co-author Alex Andonian, a graduate student at MIT’s Department of Electrical Engineering and Computer Science, says that's why he has chosen to make them the focus of his PhD.

“Design software lets you adjust the brightness of an image, but not its overall memorability or aesthetic appeal — GANs let you do that,” he says. “We’re just starting to scratch the surface of what these models can do.”   

The study was funded by the U.S. National Science Foundation.

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From 3D-printed limbs to semi-autonomous race cars

In early October, the MIT International Design Center and the MIT Edgerton Center hosted a panel discussion on “Envisioning the Future of Technology-Enabled Mobility.”

Moderated by Edgerton Center Director and Professor of Ocean and Mechanical Engineering J. Kim Vandiver, panelists included Robert Bond, chief technology officer of MIT Lincoln Laboratory; Dan Frey, professor of mechanical engineering and MIT D-Lab faculty research director; Neville Hogan, the Sun Jae Professor of Mechanical Engineering; and Jaya Narain, PhD candidate in mechanical engineering at the Fluid Interfaces Group in the MIT Media Lab.

Also on the panel was Sam Schmidt, a professional IndyCar driver paralyzed from the shoulders down after a racing crash. He thought he’d never drive again. But he did.

Schmidt attained a top speed of 192 mph on a jet runway driving a modified Chevrolet Corvette, the SAM car (Semi-Autonomous Motorcar), developed by Arrow Electronics. Wearing a headset connected to infrared cameras that detected his head rotation, Schmidt steered. He used a sip-and-puff device to accelerate and brake.

Harmonizing human and robotic movement

According to Bond, one of the exoskeleton technology breakthroughs may soon be the integration of machine learning and microelectronics. “But it's also going to require a new actuator and new sensing technologies so that we can use machine learning to anticipate motion of the human, and then have the exoskeleton move in harmony with the human,” Bond said.

“Earlier skeletal types of technologies were very awkward for people to use. And they almost worked against the human as they were trying to use it,” Bond added.

One of the challenges “in developing these technologies — for example, with exoskeletal devices — they have to respect what the human does, the natural cadences of human movement,” said Hogan. Exoskeletal devices, for instance, need to get the right mix of technology and human movement. To demonstrate, Hogan asked the audience to move their arm from point A to point B in exactly 60 seconds, essentially “roboticizing” their arm movement. It was impossible.

Meaningful connections

Narain, who as an undergraduate co-founded ATHack, a two-week annual hackathon focused on assistive technologies, noted that the movement toward “do-it-yourself” technologies has played a valuable role in creating solutions for very specific needs.

Simple projects — rearview cameras for electric wheelchairs, braking mechanisms for walkers — have been built in hackathons, but “with 3D printers and Arduinos and things like Google's core app for machine learning, brain-computer interfaces, I think it's going to become a lot more feasible for people to kind of start taking technology and developing it for themselves and people they know,” said Narain.

What Narain finds inspiring is when students build relationships with the assistive-technology user. Students meet co-designers who propose a project. Students visit them at home, at work, where they're going to use the technology. “Maybe it's a basketball court. Maybe it's work. And when they have that rapport and that emotional connection, we found that those are the students who tend to stay in the space and continue with the project and other similar projects,” Narain said.

Frey confirmed the sentiment. “If you present problems to students that are technologically challenging and socially relevant, the rest takes care of itself,” he said.

“Basically, you have to beat them off with sticks if there's social relevance there … there's no problem attracting students,” said Hogan.

Extreme affordability

Schmidt notes that “maybe only 10 percent or 15 percent of the population of people with disabilities can afford a $60,000 [semi-autonomous] minivan … There's a lot of people not getting out of their houses because of the limitations.”

Vandiver, who had visited Jaipur Foot in India, a maker of prostheses with a reported 1.78 million beneficiaries, asked, “How do we see that people who live on the extreme affordability side of the world benefit from some of the things that we're thinking about here?”

Frey pointed out “that a huge proportion of this planet cannot pay a lot for the technologies. And the vast majority of all commercial engineering is focused on relatively few people.”

One of the Lincoln Laboratory projects was challenging people to build prosthetics using 3D printers, “feet and hands and things of that sort. And what they kind of stumbled onto was, for young children who are growing up that need a limb, basically, they're growing and growing and growing. And they can't afford to continually replace that limb. But if you can codify a scalable and quickly manufacturable [one] with a 3D-printed prosthetic, they can just go print a new one a month later that fits them again,” said Bond.

“It's not the best. It doesn't perform as well as the really high-tech ones,” Bond added. “But you get to refit it every month if you need to. So we should be thinking about how these new manufacturing technologies can just help us in doing things that might seem rather simple, but I think could have huge impact.”

Toward the future

Frey suggested using a related technology with a large market, such as cellphones, as the core of assistive technology. “As in, find something that already has a big market and kind of piggyback onto it,” he said.

The event gave everyone an opportunity to network and consider ways to collaborate further; many numbers were exchanged. And, at the end, everyone had the chance to look under the hood of semi-autonomous technology in action — the SAM car parked in the Edgerton Center’s Area 51 garage.

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3 Questions: How to control biofilms in space

Researchers from MIT will be collaborating with colleagues at the University of Colorado at Boulder on an experiment scheduled to be sent to the International Space Station (ISS) on Nov. 2. The experiment is looking for ways to address the formation of biofilms on surfaces within the space station. These hard-to-kill communities of bacteria or fungi can cause equipment malfunctions and make astronauts sick. MIT News asked professor of mechanical engineering Kripa Varanasi and doctoral student Samantha McBride to describe the planned experiments and their goals.

Q: For starters, tell us about the problem that this research aims to address.

Varanasi: Biofilms grow on surfaces in space stations, which initially was a surprise to me. Why would they grow in space? But it’s an issue that can jeopardize the key equipment — space suits, water recycling units, radiators, navigation windows, and so on — and can also lead to human illness. It therefore needs to be understood and characterized, especially for long-duration space missions.

In some of the early space station missions like Mir and Skylab, there were astronauts who were getting sick in space. I don’t know if we can say for sure it’s due to these biofilms, but we do know that there have been equipment failures due to biofilm growth, such as clogged valves.

In the past there have been studies that show the biofilms actually grow and accumulate more in space than on Earth, which is kind of surprising. They grow thicker; they have different forms. The goal of this project is to study how biofilms grow in space. Why do they get all these different morphologies? Essentially, it’s the absence of gravity and probably other driving forces, convection for example.

We also want to think about remediation approaches. How could you solve this problem? In our current collaboration with Luis Zea at UC Boulder, we are looking at biofilm growth on engineered substrates in the presence and absence of gravity. We make different surfaces for these biofilms to grow on, and we apply some of our technologies developed in this lab, including liquid impregnated surfaces [LIS] and superhydrophobic nanotextured surfaces, and we looked at how biofilms grow on them. We found that after a year’s worth of experiments, here on Earth, the LIS surfaces did really well: There was no biofilm growth, compared to many other state of the art substrates.

Q: So what will you be looking for in this new experiment to be flown on the ISS?

McBride: There are signs indicating that bacteria might actually increase their virulence in space, and so astronauts are more likely to get sick. This is interesting because usually when you think of bacteria, you’re thinking of something that’s so small that gravity shouldn’t play that big a role.

Professor Cynthia Collin’s group at RPI [Rensselaer Polytechnic Institute] did a previous experiment on the ISS showing that when you have normal gravity, the bacteria are able to move around and form these mushroom-like shapes, versus in microgravity mobile bacteria form this kind of canopy shape of biofilm. So basically, they’re not as constrained any more and they can start to grow outward in this unusual morphology.

Our current work is a collaboration with UC Boulder and Luis Zea as the principal investigator. So now instead of just looking at how bacteria respond to microgravity versus gravity on Earth, we’re also looking at how they grow on different engineered substrates. And also, more fundamentally, we can see why bacteria biofilms form the way that they do on Earth, just by taking away that one variable of having the gravity.

There are two different experiments, one with bacterial biofilms and one with fungal biofilms. Zea and his group have been growing these organisms in a test media in the presence of those surfaces, and then characterizing them by the biofilm mass, the thickness, morphology, and then the gene expression. These samples will now be sent to the space station to see how they grow there.

Q: So based on the earlier tests, what are you expecting to see when the samples come back to Earth after two months?

Varanasi: What we’ve found so far is that, interestingly, a great deal of biomass grows on superhydrophobic surfaces, which is usually thought to be antifouling. In contrast, on the liquid-impregnated surfaces, the technology behind Liquiglide, there was basically no biomass growth. This produced the same result as the negative control, where there were no bacteria.

We also did some control tests to confirm that the oil used on the liquid impregnated surfaces is not biocidal. So we’re not just killing the bacteria, they’re actually just not adhering to the substrate, and they’re not growing there.

McBride: For the LIS surfaces, we’ll be looking at whether biofilms form on them or not. I think both results would be really interesting. If biofilms grow on these surfaces in space, but not on the ground, I think that’s going to tell us something very interesting about the behavior of these organisms. And of course, if biofilms don’t form and the surfaces prevent formation like they do on on the ground, then that’s also great, because now we have a mechanism to prevent biofilm formation on some of the equipment in the space station. 

So we would be happy with either result, but if the LIS does perform as well as it did on the ground, I think it’s going to have a huge impact on future missions in terms of preventing biofilms and not getting people sick. 

Fundamentally, from a science point of view, we want to understand the growth of these films and understand all of the biomechanical, biophysical, and biochemical mechanisms behind the growth. By adding the surface morphology, texture, and other properties like the liquid-impregnated surfaces, we may see new phenomena in the growth and evolution of these films, and maybe actually come up with a solution to fix the problem.

Varanasi: And then that can lead to designing new equipment or even space suits that have these features. So that’s where I think we would like to learn from this and then propose solutions.

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Joe Higgins named vice president for campus services and stewardship

Director of Campus Operations Joe Higgins has been named vice president for campus services and stewardship, effective Nov. 1. In this new role, Higgins will oversee the Office of Sustainability, Environment Health and Safety, the Office of Campus Planning, and MIT Facilities, including Campus Construction.

Executive Vice President and Treasurer Israel Ruiz announced the appointment today in an email to MIT faculty and staff.

“Joe joined MIT in 2016, and has since assumed increased responsibilities for a diverse portfolio of infrastructure functions and facilities operations,” Ruiz wrote. “Joe takes a highly collaborative approach to problem-solving, is a strategic thinker, and combines exceptional leadership skills with an amiable management style.”

Higgins’ new role comes as part of a reorganization to reassign the responsibilities of Deputy Executive Vice President Tony Sharon, who is retiring at the end of the calendar year.

“I am excited to build upon the strength and momentum created by Tony,” Higgins says. “We are fortunate to have so many dedicated and creative people thinking about ways to continually transform our campus, our buildings, and our infrastructure. Creating spaces that enable our students, faculty, and staff to thrive while they tackle the great challenges in our world is paramount to us all. We are the stewards of a tremendous physical asset.”

Since 2016, Higgins has managed critical aspects of MIT’s facilities, including finance and administration, procurement, communications, and customer engagement. In 2018, he assumed responsibility for management of maintenance, utilities, and facilities engineering. Higgins and his 800-member team are responsible for safely and sustainably planning, designing, constructing, maintaining, and powering the MIT campus, with buildings totaling over 12 million gross square feet.

Working with Director of Sustainability Julie Newman, Higgins led MIT’s alliance with Boston Medical Center and Post Office Square, announced in October 2016, to create a 60-megawatt, 650-acre solar power installation, adding carbon-free energy to the grid. MIT’s purchase of power from this North Carolina facility’s 255,000 solar panels was equivalent to 40 percent of the Institute’s electricity use at the time, neutralizing 17 percent of MIT’s carbon emissions.

Prior to joining MIT, Higgins served as vice president and head of engineering for Fidelity Investments. In his 10 years at Fidelity, he was responsible for the infrastructure systems across a global real estate portfolio including key data centers, trading floors, call centers, and corporate office campuses. He also served as Fidelity’s first corporate sustainability officer, providing leadership, coordination, and reporting for a broad array of sustainability initiatives across worldwide locations. 

Earlier in his career, Higgins served for 14 years as director of engineering and then as executive director of strategic and technical services at a Connecticut-based facility management company. In these roles, he provided strategic and engineering services to over 100 academic and nonprofit institutions on their building projects and broader campus initiatives.

A registered professional engineer, Higgins holds a BS in engineering and a BA in economics from Swarthmore College, and an MSc in education research from Oxford University.

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Drawn to open-ended problems

Vilhelm Lee Andersen Woltz, who goes by Billy, sits by the outdoor track at MIT on a New England fall day. It’s cold, gray, and misty, but it’s nothing compared to the weather during his most cherished personal cross country memory last year.

“The weather was terrible. It was pouring rain and with massive puddles. My heels were numb by the end of the race,” he recalls. The plan was to start out slow then speed up and obliterate the other team. Once Woltz came down a hill, he saw that the field to the finish line was one massive puddle. 

“I couldn’t see the ground or any rocks and was so worried about falling,” he laughs. “I had put in all this work. Like, if I fall now, then first of all, I’d lose the race, so that would suck. But I would also just be cold.” But he plunged in and placed first. 

Woltz, an MIT senior majoring in physics and in electrical engineering and computer science, is a distance runner for the Institute’s varsity track and cross country team. He dedicates at least 20 hours a week to the sport, and he can recall all of his meets in college.

Woltz takes an analytical approach to his running: “I think, ‘I want to win this race, what do I need to do to get there?’ It’s kind of like an open-ended problem and involves research and conducting my own experiments. I really like that kind of tinkering approach to my training,” he says.

Drawn to open-ended scientific questions as well, Woltz works in the lab of Professor William Oliver in the Research Laboratory of Electronics, on research to advance the cutting-edge field of quantum computing. In principle, ultrapowerful quantum computers could solve problems that are intractably complex for classical computers, but the field is still in very early stages.

When he took his first class on the subject, he was fascinated by the theory but skeptical about whether quantum computing could work on a large scale. But then he took Oliver’s graduate applied physics class, and that sucked him in.

“I was convinced,” he says. “I thought, okay, this might work. And I knew I wanted to work on it.”

Science, not football

Even though physics wasn’t offered at his high school in Logan, Ohio, a tiny town in the southeastern part of the state, Woltz always knew he wanted to do something related to science. 

“My grandma was fond of reminding me that when she would ask me what I wanted to be when I was older, I’d say ‘a scientist.’ And she was like, ‘That’s not what little kids say. Why not a professional football player?’” he says. 

Woltz, whose parents were both helicopter pilots in the U.S. Army, went to the same high school his father went to 30 years ago, which is the same school his grandmother went to 30 years before that. Logan is a football town — kids grow up dreaming of being professional football players, Woltz says. The town has a population of around 7,000, and about 25 percent of his graduating class went to college. 

Woltz’s background has given him a clear-eyed view of what public education is like for many in America. He grew up on a 64-acre farm and before going to school, he would wake up early to go break the ice that had frozen over the horses’ drinking water during the night. He lived far from the town and only had limited options for internet — none of which had high-speed internet that could handle online gaming or even streaming movies. 

Bringing coding to rural schools

Even though he now studies computer science, Woltz didn’t learn any computer programing before college. It wasn’t offered at his high school, so he asked a friend to show him the ropes when he came to MIT. 

“Computer programming should be a basic skill for the American population. It’s so useful,” he says. “To not cover it at all, that seems outdated.”

Because he did not have the chance to learn programming in Logan, he wanted to create that opportunity for people in his hometown. After his sophomore year at MIT, Woltz decided to start a one-week summer camp for kids in Logan and the surrounding areas to learn how to program. His old high school gave him a classroom, and the first year 15 students joined the free program. By the end of the week, the students were able to program their own tic-tac-toe game using Python. 

After that pilot year, the program grew. This past summer, he taught four courses with increasing difficulty levels. He also got in touch with Fugees Family, a nonprofit organization devoted to working with child survivors of war, and he taught 25 middle-school-age refugees from Syria, Bengal, and Bosnia, in Columbus, Ohio.

In total, Woltz taught close to 70 students and hopes to keep the program growing. He wants to teach the teachers computer programming so it can be sustainable and implemented across the school system. 

After he graduates, he wants to get a PhD in Physics and continue working on quantum technologies. He’s currently in the process of obtaining a scuba diving license.

“I like going to places where humans don’t belong but where we build technology that lets us go there,” he says. “I’m just curious. I like to explore and figure out what is going on in the world around me, which is probably why I’m so interested in physics and science.”

de MIT News https://ift.tt/2q9QMrJ