Bill Liu

His flexible components could change the way people use electronics.

Bill Liu thinks he can do something Samsung, LG, and Lenovo can’t: manufacture affordable, flexible electronics that can be bent, folded, or rolled up into a tube.

Other researchers and companies have had similar ideas, but Liu moved fast to commercialize his vision. In 2012, he founded a startup called Royole, and in 2014 the company—under his leadership as CEO—unveiled the world’s thinnest flexible display. Compared with rival technologies that can be curved into a fixed shape but aren’t completely pliable, Royole’s displays are as thin as an onion skin and can be rolled tightly around a pen. They can also be fabricated using simpler manufacturing processes, at lower temperatures, which allows Royole to make them at lower cost than competing versions. The company operates its own factory in Shenzhen, China, and is finishing construction on a 1.1-million-square-foot campus nearby. Once complete, the facility will produce 50 million flexible panels a year, says Royole.

Liu dreams of creating an all-in-one computing device that would combine the benefits of a watch, smartphone, tablet, and TV. “I think our flexible displays and sensors will eventually make that possible,” he says. For now, users will have to settle for a $799 headset that they can don like goggles to watch movies and video games in 3-D.

Anca Dragan

Ensuring that robots and humans work and play well together.

Anca Dragan, an assistant professor of electrical engineering and computer science at UC Berkeley, is working to distill complicated or vague human behavior into simple mathematical models that robots can understand. She says many conflicts that arise when humans and robots try to work together come from a lack of transparency about each other’s intentions. Teaching a robot to understand how it might influence a person’s behavior could solve that. One pressing application for this work is in helping self-driving cars and human-driven cars to anticipate each other’s next moves.

Suchi Saria

Putting existing medical data to work to predict sepsis risk.

Problem: Sometimes the difference between life and death is a quick and accurate diagnosis. With sepsis, a life-threatening reaction to an infection, there’s no definitive single test doctors can use to diagnose the condition.

Solution: Suchi Saria, an assistant professor at Johns Hopkins University, wondered: what if existing medical information could be used to predict which patients would be most at risk for sepsis? Algorithms that she subsequently created to analyze patient data correctly predicted septic shock in 85 percent of cases, by an average of more than a day before onset. That is a 60 percent improvement over existing screening tests.

Radha Boya

Beneath a microscope in Radha Boya’s lab, a thin sheet of carbon has an almost imperceptible channel cutting through its center, the depth of a single molecule of water. “I wanted to create the most ultimately small fluidic channels possible,” explains Boya. Her solution: identify the best building blocks to reliably and repeatedly build a structure containing unimaginably narrow capillaries. She settled on graphene, a form of carbon that is a single atom thick.

She positions two sheets of graphene (a single sheet is just 0.3 nanometers thick) next to each other with a small lateral gap between them. That is sandwiched on both sides with slabs of graphite, a material made of many layers of graphene stacked on top of each other. The result is a channel 0.3 nanometers deep and 100 nanometers wide, cutting through a block of graphite. By adding extra layers of graphene, she can tune the size of the channel in 0.3-nanometer increments.

But what fits through something so narrow? A water molecule—which itself measures around 0.3 nanometers across—can’t pass through the channel without application of pressure. But with two layers of graphene, and a 0.6-nanometer gap, water passes through at one meter per second. “The surface of graphene is slightly hydrophobic, so the water molecules stick to themselves rather than the walls,” says Boya. That helps the liquid slide through easily.

Because the gaps are so consistently sized, they could be used to build precisely tuned filtration systems. Boya has performed experiments that show her channels could filter salt ions from water, or separate large volatile organic compounds from smaller gas molecules. Because of the size consistency, her technology can filter more efficiently than others.

Boya currently works at the University of Manchester’s Graphene Research Institute in the U.K.—a monolithic black slab of a building that opened in 2015 to industrialize basic research on the material. It brands itself as the “home of graphene,” which seems appropriate given that Boya’s office is on the same corridor as those of Andre Geim and Kostya Novoselov, who won a Nobel Prize for discovering the material.

Hanqing Wu

A cheaper solution for devastating hacking attacks.

During a distributed denial of service (DDoS) attack, an attacker overwhelms a domain-name server with traffic until it collapses. The traditional way of fending off an attack like this is to pile up bandwidth so the server under attack always has more than enough volume to handle what the attacker has released. But as hackers become capable of attacks with bigger and bigger data volumes, this is no longer feasible.

Since the target of DDoS attacks is a website’s IP address, Hanqing Wu, the chief security scientist at Alibaba Cloud, devised a defense mechanism through which one Web address can be translated into thousands of IP addresses. This “elastic security network” can quickly divert all benign traffic to a new IP address in the face of a DDoS attack. And by eliminating the need to pile up bandwidth, this system would greatly reduce the cost of keeping the Internet safe.

Angela Schoellig

Her algorithms are helping self-driving and self-flying vehicles get around more safely.

Safety never used to be much of a concern with machine-learning systems. Any goof made in image labeling or speech recognition might be annoying, but it wouldn’t put anybody’s life at risk. But autonomous cars, drones, and manufacturing robots have raised the stakes.

Angela Schoellig, who leads the Dynamic Systems Lab at the University of Toronto, has developed learning algorithms that allow robots to learn together and from each other in order to ensure that, for example, a flying robot never crashes into a wall while navigating an unknown place, or that a self-driving vehicle never leaves its lane when driving in a new city. Her work has demonstrably extended the capabilities of today’s robots, enabling self-flying and self-driving vehicles to fly or drive along a predefined path despite uncertainties such as wind, changing payloads, or unknown road conditions.

As a PhD student at the Swiss Federal Institute of Technology in Zurich, Schoellig worked with others to develop the Flying Machine Arena, a 10-cubic-meter space for training drones to fly together in an enclosed area. In 2010, she created a performance in which a fleet of UAVs flew synchronously to music. The “dancing quadrocopter” project, as it became known, used algorithms that allowed the drones to adapt their movements to match the music’s tempo and character and coordinate to avoid collision, without the need for researchers to manually control their flight paths. Her setup decoupled two essential, usually intertwined components of autonomous systems—perception and action—by placing, at the center of the space, a high-precision overhead motion-capture system that can perfectly locate multiple objects at rates exceeding 200 frames per second. This external system enabled the team to concentrate resources on the vehicle-control algorithms.

Michael Saliba

Finding ways to make promising perovskite-based solar cells practical.

Crystalline-silicon panels—which make up about 90 percent of deployed photovoltaics—are expensive, and they’re already bumping up against efficiency limits in converting sunlight to electricity. So a few years ago, Michael S­aliba, a researcher at the Swiss Federal Institute of Technology in Lausanne, set out to investigate a new type of solar cell based on a family of materials known as perovskites. The first so-called perovskite solar cells, built in 2009, promised a cheaper, easier-to-process technology. But those early perovskite-based cells converted only about 4 percent of sunlight into electricity.

Saliba improved performance by adding positively charged ions to the known perovskites. He has since pushed solar cells built of the stuff to over 21 percent efficiency and shown the way to versions with far higher potential.

Gene Berdichevsky

As employee number seven at Tesla, Gene ­Berdichevsky was instrumental in solving one of its earliest challenges: the thousands of lithium-­ion batteries the company planned to pack into its electric sports car caught fire far more often than manufacturers claimed. His solution: a combination of heat transfer materials, cooling channels, and battery arrangements that ensured any fire would be self-contained.

Now Berdichevsky has cofounded Sila Nanotechnologies, which aims to make better lithium-ion batteries. The company has developed silicon-based nanoparticles that can form a high-capacity anode. Silicon has almost 10 times the theoretical capacity of the material most often used in lithium-ion batteries, but it tends to swell during charging, causing damage. Sila’s particles are robust yet porous enough to accommodate that swelling, promising longer-lasting batteries.

Olga Russakovsky

Employed crowdsourcing to vastly improve computer-vision system.

“It’s hard to navigate a human environment without seeing,” says Olga Russakovsky, an assistant professor at Princeton who is working to create artificial-intelligence systems that have a better understanding of what they’re looking at.

A few years ago, machines were capable of spotting only about 20 objects—a list that included people, airplanes, and chairs. Russakovsky devised a method, based partly on crowdsourcing the identification of objects in photos, that has led to AI systems capable of detecting 200 objects, including accordions and waffle irons.

Russakovsky ultimately expects AI to power robots or smart cameras that allow older people to remain at home, or autonomous vehicles that can confidently detect a person or a trash can in the road. “We’re not there yet,” she says, “and one of the big reasons is because the vision technology is just not there yet.”

A woman in a field dominated by men, Russakovsky started AI4ALL, a group that pushes for greater diversity among those working in artificial intelligence. While she wants greater ethnic and gender diversity, she also wants diversity of thought. “We are bringing the same kind of people over and over into the field,” she says. “And I think that’s actually going to harm us very seriously down the line.”

If robotics are to become integral and integrated into our lives, she reasons, why shouldn’t there be people of varying professional backgrounds creating them, and helping them become attuned to what all types of people need?

Russakovsky took a rather conventional path from studying mathematics as an undergrad at Stanford, where she also earned a PhD in computer science, to a postdoc at Carnegie Mellon. But, she suggests, “We also need many others: biologists who are maybe not great at coding but can bring that expertise. We need psychologists—the diversity of thought really injects creativity into the field and allows us to think very broadly about what we should be doing and what type of problems we should be tackling, rather than just coming at it from one particular angle.”

Svenja Hinderer

A design for a heart valve that’s biodegradable—potentially eliminating the need for repeat surgeries.

Problem: Over 85,000 Americans receive artificial heart valves, but such valves don’t last forever, and replacing them involves a costly and invasive surgery. In children, they must be replaced repeatedly.

Solution: Svenja Hinderer, who leads a research group at the Fraunhofer Institute in Stuttgart, Germany, has created a biodegradable heart valve that studies strongly suggest will be replaced over time by a patient’s own cells.

To accomplish this, Hinderer created a scaffolding of biodegradable fibers that mimic the elastic properties of healthy tissues. To it she attaches proteins with the power to attract the stem cells that naturally circulate in the blood. The idea is that once implanted, her heart valve would be colonized and then replaced by a patient’s own cells within two to three years.