2026 Weng-Keen Wong: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	43	7958	7731	3427	3298	98	8223

Research.com Recognitions

2007 - Fellow of the American Statistical Association (ASA)

Overview

Weng-Keen Wong is affiliated with Oregon State University in the United States and has made contributions primarily in the field of Computer Science. Their research encompasses diverse subfields including Artificial Intelligence, Nuclear and High Energy Physics, Computational Theory and Mathematics, Economics and Econometrics, and Radiation.

The scientist has published extensively in various venues, with frequent publications appearing in:

arXiv (Cornell University)
Artificial Intelligence
Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
ACM Transactions on Knowledge Discovery from Data
IEEE Access

Weng-Keen Wong's research topics cover a broad array of areas, including:

Anomaly Detection Techniques and Applications
Wireless Signal Modulation Classification
Laser-Plasma Interactions and Diagnostics
Explainable Artificial Intelligence (XAI)
Reinforcement Learning in Robotics
Sports Analytics and Performance
Nuclear Physics and Applications

Some of the recent published papers include:

"Counterfactual state explanations for reinforcement learning agents via generative deep learning", 2021, Artificial Intelligence
"Isotope identification using deep learning: An explanation", 2020, Nuclear Instruments and Methods in Physics Research Section A Accelerators Spectrometers Detectors and Associated Equipment
"Discovering Anomalies by Incorporating Feedback from an Expert", 2020, ACM Transactions on Knowledge Discovery from Data
"BioNeMo Framework: a modular, high-performance library for AI model development in drug discovery", 2024, arXiv (Cornell University)
"Deep Learning Model Portability for Domain-Agnostic Device Fingerprinting", 2023, IEEE Access

Frequent co-authors collaborating with Weng-Keen Wong include:

Matthew Olson
Fuxin Li
Bechir Hamdaoui
Shusen Liu
Jayaraman J. Thiagarajan

Wong's work bridges theoretical and applied aspects of artificial intelligence, with notable contributions to explainable AI and anomaly detection. Their statistical expertise was recognized with election as a Fellow of the American Statistical Association in 2007.

Best Publications

The eBird enterprise: An integrated approach to development and application of citizen science

Brian L. Sullivan;Jocelyn L. Aycrigg;Jessie H. Barry;Rick E. Bonney

1164
Citations
Principles of Explanatory Debugging to Personalize Interactive Machine Learning

Todd Kulesza;Margaret Burnett;Weng-Keen Wong;Simone Stumpf

596
Citations
Data-intensive science applied to broad-scale citizen science

Wesley M. Hochachka;Daniel Fink;Rebecca A. Hutchinson;Daniel Sheldon

498
Citations
Open Set Learning with Counterfactual Images.

Lawrence Neal;Matthew L. Olson;Xiaoli Z. Fern;Weng-Keen Wong

472
Citations
Too much, too little, or just right? Ways explanations impact end users' mental models

Todd Kulesza;Simone Stumpf;Margaret Burnett;Sherry Yang

391
Citations
Bayesian network anomaly pattern detection for disease outbreaks

Weng-Keen Wong;Andrew Moore;Gregory Cooper;Michael Wagner

269
Citations
Interacting meaningfully with machine learning systems: Three experiments

Simone Stumpf;Vidya Rajaram;Lida Li;Weng-Keen Wong

249
Citations
Machine learning for activity recognition: hip versus wrist data

Stewart G Trost;Yonglei Zheng;Weng-Keen Wong

217
Citations
Distributed Value Functions

Jeff G. Schneider;Weng-Keen Wong;Andrew W. Moore;Martin A. Riedmiller

197
Citations
Rule-based anomaly pattern detection for detecting disease outbreaks

Weng-Keen Wong;Andrew Moore;Gregory Cooper;Michael Wagner

184
Citations
Detecting insider threats in a real corporate database of computer usage activity

Ted E. Senator;Henry G. Goldberg;Alex Memory;William T. Young

180
Citations
Artificial Neural Networks to Predict Activity Type and Energy Expenditure in Youth

Stewart G. Trost;Weng Keen Wong;Karen A. Pfeiffer;Yonglei Zheng

180
Citations
Taming compiler fuzzers

Yang Chen;Alex Groce;Chaoqiang Zhang;Weng-Keen Wong

179
Citations
Incorporating Expert Feedback into Active Anomaly Discovery

Shubhomoy Das;Weng-Keen Wong;Thomas Dietterich;Alan Fern

162
Citations
Optimal reinsertion: a new search operator for accelerated and more accurate Bayesian network structure learning

Andrew Moore;Weng-Keen Wong

148
Citations
Systematic construction of anomaly detection benchmarks from real data

Andrew F. Emmott;Shubhomoy Das;Thomas Dietterich;Alan Fern

141
Citations
Issues in applied statistics for public health bioterrorism surveillance using multiple data streams: research needs.

Henry Rolka;Howard Burkom;Gregory F. Cooper;Martin Kulldorff

122
Citations
Explanatory Debugging: Supporting End-User Debugging of Machine-Learned Programs

Todd Kulesza;Simone Stumpf;Margaret Burnett;Weng-Keen Wong

122
Citations
Physical activity recognition from accelerometer data using a multi-scale ensemble method

Yonglei Zheng;Weng-Keen Wong;Xinze Guan;Stewart Trost

115
Citations
Computational sustainability: computing for a better world and a sustainable future

Carla Gomes;Thomas Dietterich;Christopher Barrett;Jon Conrad

114
Citations
WSARE: What's Strange About Recent Events?

Weng-Keen Wong;Andrew Moore;Gregory Cooper;Michael Wagner

113
Citations

Frequent Co-Authors

Margaret Burnett Oregon State University

Thomas G. Dietterich Oregon State University

Alan Fern Oregon State University

Andrew W. Moore Carnegie Mellon University

Gregory F. Cooper University of Pittsburgh

Carl Lagoze University of Michigan–Ann Arbor

Alex Groce Northern Arizona University

Carla P. Gomes Cornell University

Todd C. Mockler Donald Danforth Plant Science Center

Prasad Tadepalli Oregon State University

External Links

Personal website of Weng-Keen Wong Google Scholar page

If you think any of the details on this page are incorrect, let us know.

Related Online Degrees & Career Pathways

Expanding your expertise in Computer Science doesn’t just happen at the undergraduate level. Many students start with the shortest associate degree program to quickly enter the tech workforce or build a foundation for further study. This option is ideal for those seeking a fast, flexible route into IT careers.

For professionals aiming to stand out, pursuing an affordable master degree online can help you gain advanced technical skills and leadership abilities while saving on tuition. This path suits those balancing career and education through a flexible schedule.

Individuals interested in leadership and educational roles within technology-driven organizations might consider other pathways. The doctorate organizational leadership online is tailored for those seeking high-level positions that blend organizational strategy and innovation. Similarly, educators and administrators can benefit from edd programs online, which emphasize leadership in educational environments—even for those with a technical background.

No matter your starting point, exploring these online program options can open doors to a variety of rewarding tech and leadership careers across the USA.