Tin Kam Ho


Ho Tin-kam is an American computer scientist at IBM Watson Health with contributions to machine learning, data mining, and classification. Ho is noted for introducing random decision forests in 1995, and for her pioneering work in ensemble learning and data complexity analysis. She is an IEEE fellow and IAPR fellow.

Education

Ho completed her undergraduate education at the Chinese University of Hong Kong in 1984. She received a Ph.D. in Computer Science from State University of New York at Buffalo in 1992.

Career and research

Since 2014, Ho has been a research scientist in Artificial Intelligence at IBM, working primarily on semantic analysis in natural language processing. Previously she led the Statistics and Learning Research Department of Bell Labs at Murray Hill, NJ. She pioneered research in multiple classifier systems, random decision forests, and data complexity analysis, and pursued applications of automatic learning in reading systems and many areas of science and engineering. She also led major efforts on modeling and monitoring large-scale optical transmission systems. Later she worked on wireless geo-location, video surveillance, smart grid data mining, user profiling, customer experience modeling, and analysis of diagnostic processes. Her contributions were recognized by a Bell Labs President's Gold Award and two Bell Labs Teamwork Awards, a Young Scientist Award from ICDAR in 1999, and the 2008 Pierre Devijver Award for Statistical Pattern Recognition. She served as Editor-in-chief of the journal Pattern Recognition Letters in 2004-2010, and in earlier years as Associate Editor for PAMI, Pattern Recognition, Editor for Int. J. on Document Analysis and Recognition, as well as guest editors for other publications.