Moore’s Law, which has powered the drive toward ever-smaller, more powerful computers, has led to the likes of AI, cloud computing, and autonomous driving. But the law is reaching its limits because the billions of devices squeezed onto a silicon chip can only get so small before the laws of physics intervene. Xu Zhang, 34, has approached the problem by developing a kind of two-dimensional semiconductor that’s just atoms thick. “By transforming semiconductors from 3D to 2D, it is possible to truly push computing technologies to the ultimate atomic limit and enable a future of ubiquitous computing and ambient intelligence,” Zhang says.
We live in a world of devices that are rarely or never turned off—hearing aids, various types of sensors, augmented-reality devices, smartphones. But batteries don’t stay charged forever, so it’s critical to minimize devices’ power consumption. Hongjie Liu, 34, has created novel ultra-low-power chip designs that can extend battery life more than 10 times by more efficiently processing analog signals and digital data. “My innovation is analog preprocessing combined with mixed-signal computing, a novel processing architecture that mimics some of the principles of the human brain,” she says.
It’s easy to change the appearance of a digital photo by applying a digital filter. Now imagine the same principle applied to physical objects—clothing that could change its appearance daily or even hourly. Stefanie Mueller, 34, is developing a way to reprogram the appearance of objects using photochromic dyes with fine control over each color channel. “Developing this method required me to leverage knowledge from optics, materials science, hardware engineering, and computational optimization algorithms,” she says. Mueller thinks her innovation could be useful in product design. Instead of just buying a shirt, she says, you might buy a subscription that gives that shirt a new look every day.
People coming out of prison often struggle to put their lives back together, but research has shown that family contact and access to education can dramatically improve their prospects. Uzoma Orchingwa, 31, CEO and cofounder of Ameelio, wants to offer those benefits to prisoners via a free communication and education platform. A big part of the problem, in Orchingwa’s view, is the $3 billion prison telecommunication industry, dominated by just two companies. “Families spend up to $500 a month to stay connected with incarcerated loved ones,” he says. His goal is to disrupt that industry and, in the process, help prisoners to earn degrees and jobs, thus reducing recidivism and incarceration.
Sara Wahedi, 27, came up with the idea for Ehtesab following a suicide explosion that occurred near her home in Kabul, Afghanistan. As she searched for information after the tragedy, she wondered why a city like Kabul did not have a verified, monitored platform for emergency information—a situation that has become worse under the Taliban regime. Wahedi’s app, called Ehtesab, provides real-time alerts to Kabul residents on everything from electricity outages to explosions and gunfire. The app maps these incidents and updates them; alerts are sent straight to a user’s phone after they have been robustly verified.
Setor Zilevu, 27, is working at the intersection of human-computer interaction and machine learning to create semi-automated, in-home therapy for stroke patients. After his father suffered a stroke, Zilevu wanted to understand how to integrate those two fields in a way that would enable patients at home to get the same type of therapy, including high-quality feedback, that they might get in a hospital. The semi-automated human-computer interaction, which Zilevu calls the “tacit computable empower” method, can be applied to other domains both within and outside health care, he says.
In 2015, Maayan Ziv, now 31, created a mobile app called AccessNow in response to her frustration trying to navigate inaccessible places in her wheelchair. Users can search for, review, and discover locations that meet their needs according to over 25 criteria, including step-free entrances and accessible parking and bathrooms. In 2021, AccessNow filed a patent application regarding its development of technology to detect accessibility features based on patterns “observed” in the built environment and collect and share that information. Using deep learning, AccessNow is training a data model that’s designed to deliver increasingly accurate, personalized accessibility information autonomously.