NYU Center for Data Science’s Kyunghyun Cho selected as 2017 CIFAR Azrieli Global Scholar
Cho is one of fifteen global scholars, and will receive $100,000 to support his groundbreaking research in neural machine translation, which hopes to increase linguistic diversity on the internet
New York, NY — Kyunghyun Cho, an Assistant Professor at NYU’s Center for Data Science (CDS) and the Courant Institute of Mathematical Sciences, has been selected as a 2017 CIFAR Azrieli Global Scholar.
CIFAR is a global research institute that connects leading scholars through interdisciplinary research programs like child and brain development, cosmology, genetic networks, learning machines and brains, and more.
Founded in 1982, as many as 18 Nobel Laureates have since been associated with CIFAR, and its selected researchers and fellows are continually amongst the most highly cited scholars in their fields.
Supported by the Azrieli Foundation, CIFAR’s two year program offers the opportunity for researchers like Cho be mentored by other experts in his field, as well as exchange ideas with industry leaders outside of academia. He will also receive $100,000 to support his work, which has already significant contributions to fields like medicine and neural machine translation.
Cho’s deep engagement with neural machine translation in particular is poised to increase the internet’s linguistic diversity, and overcome information inequality and digital division.
Since publishing his co-authored paper on applying a novel encoder-decoder approach for multi-way, multi-lingual translation with his colleagues and his mentor, Professor Yoshua Bengio, Cho has continued to collaborate with researchers in linguistics and technology to create better approaches to data-driven translation.
“The internet has drastically improved the speed at which information spreads, but has not removed language barriers,” Cho explained. “More than 75% of the content of the internet are in foreign languages to the majority (60%) of internet users.”
“My current research on character-level, larger-context, multilingual neural machine translation that will reduce the language barrier significantly in the long run. Furthermore, the memory efficiency of neural machine translation will facilitate wider deployment of machine translation systems to places with low bandwidth or unstable internet connectivity.”
Outside of his own research, he co-leads the Computational Intelligence, Learning, Vision, and Robotics (CILVR) research collective with CDS’s Founding Director, Yann LeCun, Rob Fergus, Joan Bruna, Sam Bowman, Brenden Lake, and Rajesh Ranganath.