The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Tin Kam Ho sits on this spectrum.
This scientist: 137 publications — 21st percentile
21% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 991 publications or more.
The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Tin Kam Ho sits on this spectrum.
This scientist: 37 D-Index — 27th percentile
27% of scientists in this discipline score the same or lower.
The last bar groups every scientist with 131 D-Index or more.
Tin Kam Ho is affiliated with IBM in the United States and specializes in the field of Computer Science, with a particular focus on Artificial Intelligence. Their research spans multiple subfields, including Pollution, Electrical and Electronic Engineering, Molecular Medicine, and Renewable Energy, Sustainability, and the Environment.
The scientist's recent publications cover diverse topics related to machine learning applications, environmental impacts, and antimicrobial resistance. Notable papers include:
Tin Kam Ho's research engages heavily with topics such as Pharmaceutical and Antibiotic Environmental Impacts, Antibiotic Resistance in Bacteria, Energy, Environment, and Transportation Policies, Energy and Environment Impacts, Neural Networks and Applications, Topic Modeling, and Adversarial Robustness in Machine Learning.
Frequent co-authors collaborating with Tin Kam Ho include Liseth Salinas, Thomas VanderYacht, Nikolina Walas, Gabriel Trueba, and Jay P. Graham.
The scientist has contributed multiple papers to prominent publication venues. These venues include:
The combination of Tin Kam Ho's publication record and research topics indicates a multidisciplinary approach bridging computer science methodologies with environmental and medical applications, particularly focusing on machine learning techniques and their explainability within critical real-world domains.
Tin Kam Ho
Tin Kam Ho
Tin Kam Ho;J.J. Hull;S.N. Srihari
Tin Kam Ho;M. Basu
Jiangang Hao;Tin Kam Ho
Tin Kam Ho
Ana C. Lorena;Luís P. F. Garcia;Jens Lehmann;Marcilio C. P. Souto
Tin Kam Ho
Piotr Mirowski;Tin Kam Ho;Saehoon Yi;Michael MacDonald
Tin Kam Ho
Mitra Basu;Tin Kam Ho
Michael Andrews;Tin Ho;Gregory Kochanaki;Louis Lanzerotti
Chun-Nam Yu;Piotr Mirowski;Tin Kam Ho
Tin Kam Ho;E.M. Kleinberg
E. Bernado-Mansilla;Tin Kam Ho
Isabelle Guyon;Kristin Bennett;Gavin Cawley;Hugo Jair Escalante
Tin Kam Ho
Piotr Mirowski;Sining Chen;Tin Kam Ho;Chun-Nam Yu
Tin Kam Ho;H.S. Baird
Tin Kam Ho
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