Her simple water pump could transform the lives of millions of farmers in India.
Irrigation shouldn’t be a problem for the 30 million small farms in the water-rich Ganges River basin in eastern India. But today most farmers have to choose between cultivating a single crop each year during the monsoon rains and spending up to 90 percent of their profits to hire diesel or kerosene pumps during the dry seasons to access the plentiful, shallow groundwater.
Most plots stay uncultivated; to make up the income, farmers often resort to dangerous and demeaning migratory labor in diamond mines or clothing factories, leaving their families for months at a time.
This is what motivated engineer Katherine Taylor to uproot her life in the U.S. and move to India to found Khethworks, which builds an affordable solar-powered irrigation system that lets farmers cultivate year-round.
“Sometimes I get asked if I would have wanted a job at a high-tech company instead. But this was never a sacrifice for me, it was always the goal,” says Taylor. “The potential for keeping families together, for having people doing work they feel is dignified—it’s those kinds of stories we want to enable.”
Originally, as part of the mechanical engineering master’s program at MIT, Taylor focused on developing low-pressure drip irrigation systems, but during a visit to India, farmers helped her spot the real gap in the market. “They said, look, drip is great, but what we need is an affordable pump,” she says. “Who cares about drip if we can’t afford to irrigate year-round?”
In response, she and Khethworks cofounders Victor Lesniewski and Kevin Simon designed a centrifugal pump with triple the efficiency of similar-size pumps. That meant it could be powered by one-third as many photovoltaic panels—by far the most expensive component. This reduces the cost and makes the system portable so farmers can rent it out.
Taylor and Lesniewski moved to Pune in 2016 and will ship their first commercial product next spring.
Not that it’s been easy. Endless red tape has been frustrating, she says, and they’ve had to adapt to a business culture with a different attitude toward deadlines. “The most important thing is having a good sense of humor,” she says. But Taylor nevertheless believes it’s “absurd” that bigger players haven’t been designing for these farmers.
Going after these customers means Taylor and her cofounders haven’t been able to stick to the standard advice for startups to focus on core competencies. It’s likely they’ll have to do everything from engineering to developing distribution models. “You don’t necessarily have the luxury of doing exactly what you think you’re best at,” says Taylor.
Rescuing endangered civilians in Syria, using local materials.
In the video, two flat black bags resembling large hot-water bottles expand slowly, gradually lifting a collapsed concrete-and-rebar wall and creating a space between the wall and a mound of rocks beneath. The film shows a test of a design by Eyad Janneh and his team at nonprofit Field Ready that is now being deployed in Syria, where it is used to lift heavy debris during searches for civilians following bomb attacks.
Janneh was raised in Syria but left in 2010 and now works in Istanbul. His team designs and tests tools that can be made locally from available materials. The airbags, for example, are made from a polyester fabric with a rubber sheet cover and some binding accessories—repurposing materials already being used as covers for cargo trucks. In April one of these airbags was used in Syria to help rescue two people trapped in rubble.
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.