PeopleofSuperintelligence

Researcher

Zhiheng Huang

Zhiheng Huang is an AI researcher whose work spans fuzzy systems, natural language processing, computer vision and enterprise AI. His 2006 University of Edinburgh PhD thesis proposed a method to simplify fuzzy rule bases and a family of fuzzy interpolation methods. He coauthored a bidirectional LSTM-CRF for sequence tagging in 2015, CNN-RNN for multi-label image classification in 2016, and a noise-invariant speech-recognition study in 2018. He joined AWS as a principal in 2017 and led development and launch of Amazon Kendra and Amazon Q; in 2020, he coauthored research on relative position embeddings for Transformers. As of October 2026, he is founder and CEO of Denser AI.

Now at
Denser AI
Works
78
Citations
45,465
h-index
28
Badges

Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

  1. 2006

    Fuzzy-rule simplification

    Rule Model Simplification, his 2006 University of Edinburgh PhD thesis, proposes a method to simplify fuzzy rule bases and a family of fuzzy interpolation methods.

  2. 2014

    CNTK at Interspeech

    At Interspeech 2014, he coauthors an invited talk introducing the computational network toolkit (CNTK), a general-purpose C++ implementation of computational networks.

  3. 2015

    LSTM-CRF sequence tagging

    In 2015, he coauthors a bidirectional LSTM-CRF model for sequence tagging. The paper reports accuracy results on part-of-speech tagging, chunking and named-entity recognition.

  4. 2016

    CNN-RNN image classification

    In 2016, he coauthors CNN-RNN, which combines convolutional and recurrent networks to model label dependencies in multi-label image classification.

  5. 2017

    Amazon Kendra and Q

    He joins AWS as a principal in 2017. At AWS, he leads development and launch of Amazon Kendra and Amazon Q.

  6. 2018

    Noise-invariant speech recognition

    In 2018, he coauthors Learning Noise-Invariant Representations for Robust Speech Recognition, a study that proposes invariant representation learning to make speech models less sensitive to noise.

  7. 2020

    Transformer position embeddings

    In 2020, he coauthors a study on relative position embeddings for Transformers that reports improved SQuAD1.1 results over earlier position-embedding approaches.

  8. As of October 2026

    Founder and CEO at Denser

    As of October 2026, he is founder and CEO of Denser AI.

Career

  1. NowDenser AIFounder and CEO
  2. 2022–2024Sun Yat-sen University
  3. 2017–2023AmazonPrincipal
  4. 2013–2015Stanford University
  5. 2007–2009University of California, Berkeley

Education

  1. 2006University of EdinburghPhD

From the biography and OpenAlex affiliations (the years of papers that list the organization), so some years are approximate.

  1. Among employers with 30+ full-time cases, OpenAI leads primary technical role H-1B pay at $310,000, and Amazon, Google and Microsoft file 57% of all casesIncludes Amazon. Across 440,669 certified US H-1B filings from 44 employers (FY2020–FY2026), OpenAI leads primary technical role base pay at $310,000 and xAI leads overall base pay at $290,000.
  2. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes Stanford University. We estimate that 539 of the 6,766 people with a known current organization (8%) hold both a university role and a company role, including 20% of people at companies and 13% of people at universities.
  3. Google alumni have founded or led 133 other organizations, more than any other AI labIncludes Amazon. We estimate that alumni of 17 major AI labs went on to found or lead 350 other organizations, including 226 they founded, and we trace 472 career moves directly between 23 labs since 2010.
  4. Google had the most people shaping AI every year from 2014 to 2025Includes Stanford University. Our estimates of which organizations employed the most people shaping AI each year from 1955 to 2025, tracing the shift from university labs to companies.

Colleagues

People with dated positions at the same organization in the same years.

Sources and credits

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  1. Rule model simplification era.ed.ac.uk. Checked 22 October 2026.
  2. An introduction to computational networks and the computational network toolkit (invited talk) isca-archive.org. Checked 22 October 2026.
  3. Bidirectional LSTM-CRF Models for Sequence Tagging arxiv.org. Checked 22 October 2026.
  4. CNN-RNN: A Unified Framework for Multi-Label Image Classification openaccess.thecvf.com. Checked 22 October 2026.
  5. Denser AI vs Other AI Chatbot Platforms: An Enterprise Selection Guide denser.ai. Checked 22 October 2026.
  6. Learning noise-invariant representations for robust speech recognition amazon.science. Checked 22 October 2026.
  7. Improve transformer models with better relative position embeddings amazon.science. Checked 22 October 2026.