PeopleofSuperintelligence

Researcher

Donnie Henderson

Donnie Henderson is a researcher whose papers apply neural networks to postal digits, handprint characters, and handwritten words. He coauthored a reconfigurable neural-net chip and a globally trained handwritten-word recognizer with Yoshua Bengio and Yann LeCun. His work also addressed recognition-based segmentation for on-line run-on handwriting. In May 2003, he was a Communications Services Researcher at AT&T Labs in New Jersey and built Phone Man, a PC prototype for monitoring and controlling calls.

Timeline

  1. 1988

    A neural net for postal digits

    In 1988, he coauthors a neural-network system that recognizes hand-printed ZIP-code digits. The system trains and tests on real-world data drawn from actual U.S. mail.

  2. 1989

    Back-propagation for postal digits

    In 1989, he coauthors a back-propagation network for handwritten digits with Yann LeCun and AT&T Bell Labs colleagues. It reports a 1% error rate and about a 9% reject rate on USPS ZIP-code digits.

  3. 1990

    A reconfigurable neural-net chip

    In 1990, he coauthors “Reconfigurable Neural Net Chip with 32K Connections” with Hans Peter Graf, R. Janow, and R. Lee.

  4. 1991

    Handprint character recognition

    In 1991, he coauthors “A neural network approach to handprint character recognition” with Lawrence D. Jackel and 15 other researchers.

  5. 1993

    A globally trained word reader

    With Yoshua Bengio and Yann LeCun, he coauthors a globally trained recognizer for on-line mixed-style handwriting in 1993. It encodes words as low-resolution annotated images and uses a spatially replicated convolution network with a hidden Markov model to score words.

  6. 1994

    Segmenting run-on handwriting

    In 1994, he coauthors a study with H. Weissman, Markus Schenkel, Isabelle Guyon, and C. Nohl on recognition-based segmentation for on-line run-on handprinted words.

  7. May 2003

    Phone Man at AT&T Labs

    In May 2003, he is a Communications Services Researcher at AT&T Labs in Florham Park, New Jersey. He builds a Phone Man prototype for remotely controlling and monitoring calls through a PC.

  1. 23% of people shaping AI who left a university for a company later returned to a universityIncludes AT&T. We estimate that 468 of the 2,011 people shaping AI who left a university for a company (23%) later returned to a university after a median of 5 years, and 163 of 829 (20%) made the reverse round trip.
  2. Universities keep AI researchers for a median of 6 years, compared with 2 years at frontier labsIncludes AT&T. Across 15,811 dated work stays (6,529 people), median role tenure runs from 6 years at universities to 2 years at frontier labs. We compare all five organization types, starting decades from the 1970s to the 2020s, and the organizations that keep people longest.
  3. 20% of people at companies who shape AI also hold a university role, led by Google and Stanford UniversityIncludes AT&T. 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.
  4. People shaping AI work at a median of 3 organizations over a career: 3 for university starters and institute and lab starters, versus 2 for company startersIncludes AT&T. Across 6,853 people shaping AI with a dated work role, we estimate a median of 3 parent organizations over a career (mean 3.6), comparing role groups, starting sectors and starting decades from the 1950s to 2020–2026.

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. Neural Network Recognizer for Hand-Written Zip Code Digits proceedings.neurips.cc. Checked 22 October 2026.
  2. Handwritten Digit Recognition with a Back-Propagation Network proceedings.neurips.cc. Checked 22 October 2026.
  3. Yann LeCun Applies Backpropagation to Handwritten ZIP Code Recognition at AT&T Bell Labs (1989) | AI Achievements achievements.ai. Checked 22 October 2026.
  4. Donnie Henderson csauthors.net. Checked 22 October 2026.
  5. Henry S. Baird csauthors.net. Checked 22 October 2026.
  6. Globally Trained Handwritten Word Recognizer Using Spatial Representation, Convolutional Neural Networks, and Hidden Markov Models | ML Anthology mlanthology.org. Checked 22 October 2026.
  7. Markus Schenkel csauthors.net. Checked 22 October 2026.
  8. On The Cutting Edge blackenterprise.com. Checked 22 October 2026.