Tin Kam Ho
Also known as Tin Ho
Tin Kam Ho is a computer scientist whose work spans multiple classifier systems, random decision forests, and data complexity analysis. After graduating from the Chinese University of Hong Kong in 1984, she earns a computer science PhD at SUNY Buffalo in 1992; her thesis studies classifier systems and visual word recognition. At Bell Labs she heads Statistics and Learning Research and edits Pattern Recognition Letters; IAPR names her a Fellow in 2004. IAPR awards her the 2024 King-Sun Fu Prize for work on multi-classifier systems, random decision forests, and data complexity analysis. In 2026 she coauthors a study of developers' experiences with enterprise AI coding assistants, and as of October 2026 she is affiliated with IBM Research.
- Now at
- IBM Research
- Works
- 110
- Citations
- 17,783
- h-index
- 31
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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1984–1992
A thesis on visual words
She graduates from the Chinese University of Hong Kong in 1984 and earns a computer science PhD at SUNY Buffalo in 1992; her thesis studies multiple classifier systems and visual word recognition.
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1995
Random decision forests
She creates the first algorithm for random decision forests.
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2004
Editing a pattern journal
She takes over as editor-in-chief of Pattern Recognition Letters and handles its regular issues.
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2004
An IAPR fellowship
IAPR elects her a Fellow for contributions to multiple classifier systems, statistical pattern recognition, and document image analysis and recognition, as well as service to IAPR.
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By 2008
Leading a Bell Labs group
She heads Bell Labs' Statistics and Learning Research Department.
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2024
A King-Sun Fu Prize
IAPR awards her the King-Sun Fu Prize for contributions to multi-classifier systems, random decision forests, and data complexity analysis.
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2026
Surveying AI coding assistants
She coauthors a study that surveys developers on their experiences with AI coding assistants in enterprise software work.
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As of October 2026
IBM Research affiliation
She is affiliated with IBM Research.
Sources and credits
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