PeopleofSuperintelligence

Researcher

Chee‐Kheong Siew

Also known as Chee-Kheong Siew, Chee Kheong Siew, Chee-kheong Siew

Chee‐Kheong Siew is a researcher whose published work spans extreme learning machines for neural networks and network traffic control. He coauthored studies of RBF networks in 2004 and 2005, then papers on ELM theory, real-time learning, threshold training and universal approximation in 2006. In 2013, he and three collaborators proposed the recurrent leaky bucket, which adds a recurring interval for cyclical traffic such as variable-bit-rate video.

Timeline

  1. 2004

    ELM RBF network case

    In 2004, he coauthors “Extreme learning machine: RBF network case” with Guang-Bin Huang for the Eighth International Conference on Control, Automation, Robotics and Vision.

  2. 2005

    Randomly assigned RBF kernels

    In 2005, he coauthors “Extreme learning machine with randomly assigned RBF kernels” with Guang-Bin Huang in the International Journal of Information Technology.

  3. 2006

    ELM theory and applications

    In 2006, he coauthors “Extreme learning machine: Theory and applications” with Guang-Bin Huang and Qinyu Zhu in Neurocomputing.

  4. 2006

    Real-time learning

    In 2006, he coauthors “Real-time learning capability of neural networks” with Guang-Bin Huang and Qin-Yu Zhu. The method selects neural quantizers and analytically determines network weights and bias in one step.

  5. 2006

    Training threshold networks

    In 2006, he coauthors “Can Threshold Networks Be Trained Directly?” with Guang-Bin Huang, Qin-Yu Zhu, K. Z. Mao, P. Saratchandran, and N. Sundararajan.

  6. 2006

    Universal approximation with random nodes

    In 2006, he coauthors “Universal approximation using incremental constructive feedforward networks with random hidden nodes” with Guang-Bin Huang and Lei Chen.

  7. 2013

    Recurrent leaky bucket

    In 2013, he and Jingjin Wu, Chuan Heng Foh, and Moshe Zukerman coauthor “Recurrent leaky bucket” in IEEE Signal Processing Letters. The method adds a recurring interval to the leaky bucket for repeating traffic such as variable-bit-rate video.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.
  2. The United States employs an estimated 48% of people shaping AIWe estimate where the people shaping AI work today, how universities, companies and research institutes split across countries, and how country shares have changed since the 1960s.
  3. 43% of living people shaping AI work outside their country of birthWe estimate that 43% of living people shaping AI work outside their country of birth (520 of 1,198 people with known birth and employment countries).
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionWe estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

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  1. The multi-source signal fusion reciprocating compressor fault diagnosis method gxbwk.njournal.sdu.edu.cn. Checked 22 October 2026.
  2. Extreme learning machine: Theory and applications elibrary.unn.edu.ng. Checked 22 October 2026.
  3. Real-time learning capability of neural networks lacuna.tiptreesystems.com. Checked 22 October 2026.
  4. Extreme learning machine en.wikipedia.org. Checked 22 October 2026.
  5. Recurrent leaky bucket scholars.cityu.edu.hk. Checked 22 October 2026.