World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
24305
World Ranking
10426
National Ranking
4351

Overview

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:

  • "Rapid MOSFET Contact Resistance Extraction From Circuit Using SPICE-Augmented Machine Learning Without Feature Extraction," 2021, published in IEEE Transactions on Electron Devices
  • "What Makes You Hold on to That Old Car? Joint Insights From Machine Learning and Multinomial Logit on Vehicle-Level Transaction Decisions," 2022, Frontiers in Future Transportation
  • "Interpretability, Reproducibility, and Replicability [From the Guest Editors]," 2022, IEEE Signal Processing Magazine
  • "Analysis of Antibiotic Resistance Genes (ARGs) across Diverse Bacterial Species in Shrimp Aquaculture," 2024, Antibiotics
  • "Explainability of Methods for Critical Information Extraction From Clinical Documents: A survey of representative works," 2022, IEEE Signal Processing Magazine

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:

  • IEEE Signal Processing Magazine
  • arXiv (Cornell University)
  • Antibiotics
  • IEEE Transactions on Electron Devices
  • Frontiers in Future Transportation

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.

Best Publications

  • The random subspace method for constructing decision forests

    Tin Kam Ho

  • Random decision forests

    Tin Kam Ho

  • Decision combination in multiple classifier systems

    Tin Kam Ho;J.J. Hull;S.N. Srihari

  • Complexity measures of supervised classification problems

    Tin Kam Ho;M. Basu

  • Machine Learning Made Easy: A Review of "Scikit-learn" Package in Python Programming Language.

    Jiangang Hao;Tin Kam Ho

  • Nearest Neighbors in Random Subspaces

    Tin Kam Ho

  • How Complex Is Your Classification Problem?: A Survey on Measuring Classification Complexity

    Ana C. Lorena;Luís P. F. Garcia;Jens Lehmann;Marcilio C. P. Souto

  • A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors

    Tin Kam Ho

  • SignalSLAM: Simultaneous localization and mapping with mixed WiFi, Bluetooth, LTE and magnetic signals

    Piotr Mirowski;Tin Kam Ho;Saehoon Yi;Michael MacDonald

  • MULTIPLE CLASSIFIER COMBINATION: LESSONS AND NEXT STEPS

    Tin Kam Ho

  • Data Complexity in Pattern Recognition

    Mitra Basu;Tin Kam Ho

  • Methods and apparatus for location determination based on dispersed radio frequency tags

    Michael Andrews;Tin Ho;Gregory Kochanaki;Louis Lanzerotti

  • A Sparse Coding Approach to Household Electricity Demand Forecasting in Smart Grids

    Chun-Nam Yu;Piotr Mirowski;Tin Kam Ho

  • Building projectable classifiers of arbitrary complexity

    Tin Kam Ho;E.M. Kleinberg

  • Domain of competence of XCS classifier system in complexity measurement space

    E. Bernado-Mansilla;Tin Kam Ho

  • Design of the 2015 ChaLearn AutoML challenge

    Isabelle Guyon;Kristin Bennett;Gavin Cawley;Hugo Jair Escalante

  • Classification technique using random decision forests

    Tin Kam Ho

  • Demand forecasting in smart grids

    Piotr Mirowski;Sining Chen;Tin Kam Ho;Chun-Nam Yu

  • Large-scale simulation studies in image pattern recognition

    Tin Kam Ho;H.S. Baird

  • Complexity of Classification Problems and Comparative Advantages of Combined Classifiers

    Tin Kam Ho

Frequent Co-Authors

Jonathan J. Hull
Jonathan J. Hull Independent Scientist / Consultant, US
Sargur N. Srihari
Sargur N. Srihari University at Buffalo, State University of New York
Henry S. Baird
Henry S. Baird Lehigh University
Philip Whiting
Philip Whiting Macquarie University
Richard Hull
Richard Hull Spring Hills Foundation
Lawrence O'Gorman
Lawrence O'Gorman Nokia (United States)
George Nagy
George Nagy Rensselaer Polytechnic Institute
Jens Lehmann
Jens Lehmann University of Bonn
Edwin R. Hancock
Edwin R. Hancock University of York
Rachel K. E. Bellamy
Rachel K. E. Bellamy IBM (United States)

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