Elevoc Co-Founder DeLiang Wang Ranked Among Top 0.05% of Global Scholars

May 7, 2026
Elevoc Technology announced that its co-founder, Professor DeLiang Wang, has been ranked in the top 0.05% of scholars worldwide by ScholarGPS, adding to a series of major academic honors recognizing his contributions to neural networks and speech separation research.

Elevoc Technology announced in a press release that its co-founder, Professor DeLiang Wang, has been ranked in the top 0.05 percent of scholars worldwide in the 2025 ScholarGPS Highly Ranked Scholars list. The recognition follows his receipt of the 2025 IEEE Neural Networks Pioneer Award and his inclusion in the 2025 World's Top 2% Scientists list compiled by Stanford University and Elsevier.

Professor Wang serves as a Presidential Chair Professor at The Chinese University of Hong Kong, Shenzhen, and is an elected Fellow of the IEEE, ISCA, and AAIA. His research on oscillatory correlation theory and speech separation established key foundations for machine hearing. Wang is credited with introducing deep learning to speech separation, helping address the long-standing "Cocktail Party Problem" of isolating voices in noisy environments.

In 2017, Wang co-founded Elevoc, which achieved the first commercial implementation of AI-based noise reduction the following year. Elevoc's speech separation technology is now integrated into over one billion devices, including smartphones, earphones, and vehicles from brands such as Lenovo, Xiaomi, OPPO, and Li Auto. The company continues to apply deep learning and Computational Auditory Scene Analysis to enhance voice interaction across consumer and automotive sectors.

We hope you enjoyed this article

Consider subscribing to one of our newsletters like Daily AI Brief.

Also, consider following us on social media:

Free newsletter

Daily AI Brief

Daily report covering major AI developments and industry news, with both top stories and complete market updates

Whitepaper

Tensordyne Napier: What If One Rack Could Do the Work of Nine?

Tensordyne

This Tensordyne whitepaper presents Napier, an inference-focused AI processor and rack-scale system based on the company’s TDN Math logarithmic number system. It examines infrastructure requirements for large mixture-of-experts and agentic models, compares major inference architecture approaches, and details the TDN AIP processor, TDN72 pod, TDN Link fabric, and Napier Ultra configuration. The paper reports simulation-based performance, cost, and accuracy-validation results, including Tensordyne’s projected comparison of one Napier rack with a nine-rack Nvidia Rubin plus Groq deployment; the chip is reported as taped out and in fabrication.

Read more
Free, six days a week

Daily AI Brief: the AI news that matters, in your inbox.