I value practical and reproducible research. This page highlights open-source libraries, benchmarks, and system tools built by my group and collaborators. Many of these projects support AI risk audit and control across the deployment stack: anomaly and out-of-distribution detection in data, trust and robustness evaluation of foundation models, and runtime audit and control of deployed agent systems, with applications in science and high-stakes domains. Several of these projects have been accepted into the Anthropic Claude for Open Source Program. For all repositories, see my GitHub profile.
| Type | Evidence | Source |
|---|---|---|
| AI Lab | OpenAI Careers names PyOD as expected operational tooling in the Technical Intelligence Analyst job posting Qualifications block: "Have experience with anomaly detection tools, such as PyOD, and discovery processes for surfacing novel or low-prevalence patterns." | OpenAI · snapshot |
| Space Agency | Selected by ESA for OPS-SAT spacecraft telemetry benchmark (all 30 algorithms) | Nature Sci. Data |
| U.S. DoD | CDAO Generative AI Responsible AI Toolkit lists PyOD as a Production / High-maturity OOD-detection tool (entry p.49, embedded in Stage 3.1.10 assessment workflow) | ai.mil PDF |
| EU Project | SEDIMARK Horizon Europe D3.1 (p.18) names PyOD and TODS in the outlier-detection module of the EU data-space toolbox | SEDIMARK D3.1 |
| Gov / Labs | Cited in research papers by authors affiliated with Deutsche Bundesbank, NIH, CDC, RAND, NASA JPL, German DLR and DESY, and the Sandia, Brookhaven, and Argonne national labs, plus multiple Fraunhofer institutes (citing ECOD, COPOD, PyOD, ADBench, TODS, and LSCP) | Audit details |
| Platform | Apache Software Foundation / Apache Beam (8.5K+ stars) ships a first-class PyOD ModelHandler at sdks/python/apache_beam/ml/anomaly/detectors/pyod_adapter.py; Apache Beam underlies Google Cloud Dataflow |
apache/beam |
| Enterprise | PostHog (34K+ stars, YC unicorn product analytics) runs a multi-detector PyOD subsystem at posthog/tasks/alerts/detectors/pyod_detectors/ for live-traffic alerting (eight algorithm wrappers: KNN, IForest, COPOD, ECOD, OCSVM, LOF, PCA, HBOS) |
PostHog/posthog |
| Platform | MLflow (25.8K+ stars) official community-flavor docs list PyOD as the canonical anomaly-detection flavor with worked KNN-detector example via mlflavors |
mlflow/mlflow |
| Pharma | Genentech (Roche) Data Detective embeds PyOD/ADBench in its drug-discovery validator factories (adbench_validator_method_factory.py, adbench_multimodal, adbench_ood_inference) |
Genentech/data-detective |
| Enterprise | Walmart real-time pricing anomaly detection (1M+ daily updates) | KDD 2019 |
| Enterprise | Databricks Kakapo framework for unsupervised outlier detection | Databricks Blog |
| Enterprise | IQVIA healthcare fraud detection (123K+ pharmacy claims) | SUOD Paper |
| Enterprise | Ericsson Anomaly Detection Framework (E-ADF) built on PyOD | Ericsson Blog |
| Patents | 12 patents cite PyOD/COPOD/ECOD/LSCP/SUOD (WIPO x2, EU x2, US x4, China x3, Slovakia x1); recent additions include Actimize US20230267468A1 (fraud / anomalous-transaction ML) and Slovak utility model SK2042023U1 (full PyOD detector suite) | Ericsson · Actimize · SK |
| Encyclopedia | Wikipedia "Anomaly detection" Software section names PyOD; reference list cites Zhao, Nasrullah, Li 2019 JMLR | Wikipedia |
| Education | Featured in 5 books (Manning, O'Reilly, Apress, Routledge, IntechOpen) | Manning |
| Education | DataCamp course with dedicated chapter (19M+ platform learners) | DataCamp |
| Type | Evidence | Source |
|---|---|---|
| Consulting | Deloitte Germany cites ADBench in an AIxAML anti-money-laundering transaction-monitoring solution | Deloitte PDF |
| Enterprise | Cited in papers with authors affiliated with Microsoft Research, Tencent, Amazon, BlackRock, Visa, Bosch, Siemens, and Ericsson | Audit details |
| Pharma | Genentech (Roche) Data Detective uses ADBench in adbench_validator_method_factory.py, adbench_multimodal, and adbench_ood_inference validator factories for drug-discovery data validation |
Genentech/data-detective |
| Type | Evidence | Source |
|---|---|---|
| U.S. Senate | Cited in HSGAC "Hedge Fund Use of Artificial Intelligence" report (footnote 119) | Senate PDF |
| U.S. DoD | Listed in CDAO Generative AI Responsible AI Toolkit | ai.mil PDF |
| NIST | Named in NIST AI 100-2e2025 Section 3.6 "Benchmarks for AML Vulnerabilities" | NIST PDF |
| Policy | Official benchmark in all 3 editions of the FLI AI Safety Index (2024, 2025 x2) | FLI Report · Indicator Sheet |
| National Lab | Lawrence Livermore National Laboratory feature article; LLNL/DOE SafeAI report cites TrustLLM | LLNL · SafeAI PDF |
| International | Cited in International AI Safety Report 2026 (citation #881; led by Yoshua Bengio, 100+ experts, 30+ countries) | Report |
| Media | Featured by 机器之心 (Jiqizhixin) and 澎湃新闻 (The Paper) | 机器之心 · 澎湃 |
| Enterprise Editorial | Samsung SDS Insights treats TrustLLM as a flagship LLM trustworthiness evaluation framework in its Korean enterprise editorial; reference list cites arXiv:2401.05561 | Samsung SDS |
| Type | Evidence | Source |
|---|---|---|
| Institute | Vector Institute highlights TrustGen in its ICLR 2026 research roundup | Vector |
| Industry Lab | Adobe Research lists FigEdit ("Charts Are Not Images") and the benchmark release | Adobe Research |
| Type | Evidence | Source |
|---|---|---|
| Journal | Published in Nature Chemical Biology (2022) | Nature Chem. Bio. |
| University | Harvard Medical School feature: "Can AI transform drug discovery?" | HMS News |
| Science Press | Phys.org syndication of Harvard article | Phys.org |
| Industry | Amazon Science feature article | Amazon Science |
| Pharma | Cited by researchers at AstraZeneca, Pfizer, Roche, Novartis, Merck, Sanofi, Eli Lilly | Audit details |
| Labs | Cited in papers by researchers at Los Alamos and Brookhaven national labs (cheminformatics, model uncertainty) and OpenAI (biomedical reasoning) | Audit details |
| Type | Evidence | Source |
|---|---|---|
| Policy | Cited by Privacy International in "Nowhere to Hide? Privacy Risks and Policy Implications of AI Geolocation" (p.28, footnote 56) | Report |
| Chinese Media | 机器之心Pro reporting via Sina names "南加州大学教授赵越（Yue Zhao）团队", paper title "Doxing via the Lens", and the arXiv link | Sina |