# Citation Affiliation Audit

*Generated: 2026-04-13 via OpenAlex API*

**What this is:** Papers that cite your work, where at least one author is affiliated with a notable institution.
This means "researchers AT [institution] cited your tool" -- not "[institution] officially endorses your tool."
Surveys excluded. 44 papers with citations found on OpenAlex (out of 99 non-survey papers). 1595 unique citing papers analyzed.

## Tier 0: Government, Space Agencies, National Labs, Defense, Foundation Model Cos

**24 entries**

| Category | Institution | Country | Your Work Cited | Citing Paper | Year |
|----------|-----------|---------|----------------|-------------|------|
| Central Bank | Deutsche Bundesbank | DE | ECOD: Unsupervised Outlier Detectio | Diffusion-Scheduled Denoising Autoencoders for Anomaly Detec | 2025 |
| Central Bank | Deutsche Bundesbank | DE | The Need for Unsupervised Outlier M | RECol: Reconstruction Error Columns for Outlier Detection | 2023 |
| Foundation Model Co | OpenAI (United States) | US | Therapeutics Data Commons: Machine  | RL-Finetuning of OpenAI o1-mini to Enhance Biomedical Reason | 2025 |
| Foundation Model Co | OpenAI (United States) | US | ADBench: Anomaly Detection Benchmar | Diffusion Models: A Comprehensive Survey of Methods and Appl | 2023 |
| Foundation Model Co | Google DeepMind (United Kingdom) | GB | Artificial Intelligence Foundation  | Scientific discovery in the age of artificial intelligence | 2023 |
| International Lab | Deutsches Elektronen-Synchrotron DESY | DE | ECOD: Unsupervised Outlier Detectio | Data-Based Condition Monitoring and Disturbance Classificati | 2024 |
| National Lab | Argonne National Laboratory | US | TODS: An Automated Time Series Outl | A novel sensor-driven framework for preemptive failure detec | 2025 |
| National Lab | Los Alamos National Laboratory | US | Therapeutics Data Commons: Machine  | Linear graphlet models for accurate and interpretable chemin | 2024 |
| National Lab | Los Alamos National Laboratory | US | Therapeutics Data Commons: Machine  | Linear Graphlet Models for Accurate and Interpretable Chemin | 2024 |
| National Lab | Brookhaven National Laboratory | US | Therapeutics Data Commons: Machine  | Leveraging Active Subspaces to Capture Epistemic Model Uncer | 2024 |
| National Lab | Pacific Northwest National Laboratory | US | Artificial Intelligence Foundation  | Current and future directions in network biology | 2024 |
| National Lab | Brookhaven National Laboratory | US | Artificial Intelligence Foundation  | Current and future directions in network biology | 2024 |
| National Lab | Sandia National Laboratories | US | LSCP: Locally Selective Combination | Ensemble Grammar Induction For Detecting Anomalies in Time S | 2020 |
| Research Institute | Fraunhofer Institute for Translational Medicine and Pharmacology | DE | Artificial Intelligence Foundation  | Computational drug repurposing: approaches, evaluation of in | 2025 |
| Research Institute | Fraunhofer Institute for Algorithms and Scientific Computing | DE | Artificial Intelligence Foundation  | Computational drug repurposing: approaches, evaluation of in | 2025 |
| Research Institute | Fraunhofer Institute for Open Communication Systems | DE | PyOD: A Python Toolbox for Scalable | Morphological Profiling Dataset of EU-OPENSCREEN Bioactive C | 2024 |
| Research Institute | Fraunhofer Institute for Mechatronic Systems Design | DE | TODS: An Automated Time Series Outl | Meta-learning for Automated Selection of Anomaly Detectors f | 2023 |
| Research Institute | Fraunhofer Institute for Mechatronic Systems Design | DE | LSCP: Locally Selective Combination | Meta-learning for Automated Selection of Anomaly Detectors f | 2023 |
| Space Agency | Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) | DE | ADBench: Anomaly Detection Benchmar | Collaborative Representation-Based Attention Network for Hyp | 2025 |
| Space Agency | Jet Propulsion Laboratory | US | ADBench: Anomaly Detection Benchmar | Anomaly Detection for Spacecraft Radios Based on Open-Loop R | 2024 |
| US Government | National Institutes of Health | US | TrustLLM: Trustworthiness in Large  | Economics and Equity of Large Language Models: Health Care P | 2024 |
| US Government | National Institutes of Health | US | Artificial Intelligence Foundation  | Current and future directions in network biology | 2024 |
| US Government | Centers for Disease Control and Prevention | US | ECOD: Unsupervised Outlier Detectio | Sequence-based detection of emerging antigenically novel inf | 2024 |
| US Government | National Institutes of Health | US | ECOD: Unsupervised Outlier Detectio | Unsupervised quality assurance for brain MR image rigid regi | 2023 |

## Tier 1: Big Tech, Finance, Pharma, Healthcare, Industrial

**150 entries**

| Category | Institution | Country | Your Work Cited | Citing Paper | Year |
|----------|-----------|---------|----------------|-------------|------|
| Big Tech | Huawei Technologies (China) | CN | Treble Counterfactual VLMs: A Causa | A Survey of Multimodal Hallucination Evaluation and Detectio | 2026 |
| Big Tech | Adobe Systems (United States) | US | DPU: Dynamic Prototype Updating for | Few-Shot Graph Out-of-Distribution Detection with LLMs | 2025 |
| Big Tech | Amazon (United States) | US | TrustLLM: Trustworthiness in Large  | REAL Sampling: Boosting Factuality and Diversity of Open-end | 2025 |
| Big Tech | Amazon (Germany) | DE | TrustLLM: Trustworthiness in Large  | REAL Sampling: Boosting Factuality and Diversity of Open-end | 2025 |
| Big Tech | Tencent (China) | CN | ADBench: Anomaly Detection Benchmar | M3DM-NR: RGB-3D Noisy-Resistant Industrial Anomaly Detection | 2025 |
| Big Tech | Amazon (United States) | US | ADBench: Anomaly Detection Benchmar | REACT: Residual-Adaptive Contextual Tuning for Fast Model Ad | 2025 |
| Big Tech | Intel (United Kingdom) | GB | ADBench: Anomaly Detection Benchmar | Beyond Academic Benchmarks: Critical Analysis and Best Pract | 2025 |
| Big Tech | Amazon (United States) | US | BOND: Benchmarking Unsupervised Out | TGTOD: A Global Temporal Graph Transformer for Outlier Detec | 2025 |
| Big Tech | IBM Research - Thomas J. Watson Research Center | US | Contrastive Attributed Network Anom | Deep Graph Anomaly Detection: A Survey and New Perspectives | 2025 |
| Big Tech | Tencent (China) | CN | Contrastive Attributed Network Anom | How to use Graph Data in the Wild to Help Graph Anomaly Dete | 2025 |
| Big Tech | Huawei Technologies (United States) | US | TODS: An Automated Time Series Outl | TAB: Unified Benchmarking of Time Series Anomaly Detection M | 2025 |
| Big Tech | Huawei Technologies (China) | CN | TODS: An Automated Time Series Outl | TAB: Unified Benchmarking of Time Series Anomaly Detection M | 2025 |
| Big Tech | IBM Research - Zurich | CH | Therapeutics Data Commons: Machine  | Foundation models for materials discovery – current state an | 2025 |
| Big Tech | IBM (United States) | US | Therapeutics Data Commons: Machine  | Foundation models for materials discovery – current state an | 2025 |
| Big Tech | IBM Research - Thomas J. Watson Research Center | US | Therapeutics Data Commons: Machine  | Foundation models for materials discovery – current state an | 2025 |
| Big Tech | Intel (United States) | US | Therapeutics Data Commons: Machine  | A framework for evaluating the chemical knowledge and reason | 2025 |
| Big Tech | Nvidia (United Kingdom) | GB | Therapeutics Data Commons: Machine  | Boosting the predictive power of protein representations wit | 2025 |
| Big Tech | Baidu (China) | CN | Employee Turnover Prediction with M | A Comprehensive Survey of Artificial Intelligence Techniques | 2025 |
| Big Tech | Microsoft Research (United Kingdom) | GB | NNG-Mix: Improving Semi-supervised  | Distribution Shifts at Scale: Out-of-distribution Detection  | 2025 |
| Big Tech | IBM Research - Zurich | CH | Artificial Intelligence Foundation  | Foundation models for materials discovery – current state an | 2025 |
| Big Tech | IBM (United States) | US | Artificial Intelligence Foundation  | Foundation models for materials discovery – current state an | 2025 |
| Big Tech | IBM Research - Thomas J. Watson Research Center | US | Artificial Intelligence Foundation  | Foundation models for materials discovery – current state an | 2025 |
| Big Tech | IBM (United States) | US | Artificial Intelligence Foundation  | GP-MoLFormer: a foundation model for molecular generation | 2025 |
| Big Tech | Microsoft Research Asia (China) | CN | Artificial Intelligence Foundation  | Controlling risks of AI in chemical science with agents | 2025 |
| Big Tech | Amazon (United States) | US | ECOD: Unsupervised Outlier Detectio | REACT: Residual-Adaptive Contextual Tuning for Fast Model Ad | 2025 |
| Big Tech | Huawei Technologies (China) | CN | ECOD: Unsupervised Outlier Detectio | Compatible Unsupervised Anomaly Detection with Multi-Perspec | 2025 |
| Big Tech | Amazon (United States) | US | TrustLLM: Trustworthiness in Large  | Economics and Equity of Large Language Models: Health Care P | 2024 |
| Big Tech | Adobe Systems (United States) | US | TrustLLM: Trustworthiness in Large  | Benchmark suites instead of leaderboards for evaluating AI f | 2024 |
| Big Tech | Tencent (China) | CN | ADBench: Anomaly Detection Benchmar | IM-IAD: Industrial Image Anomaly Detection Benchmark in Manu | 2024 |
| Big Tech | Microsoft Research (United Kingdom) | GB | ADBench: Anomaly Detection Benchmar | Building AI Agents for Autonomous Clouds: Challenges and Des | 2024 |
| Big Tech | Tencent (China) | CN | ADBench: Anomaly Detection Benchmar | SoftPatch+: Fully unsupervised anomaly classification and se | 2024 |
| Big Tech | Huawei Technologies (China) | CN | Contrastive Attributed Network Anom | You Can't Ignore Either: Unifying Structure and Feature Deno | 2024 |
| Big Tech | Microsoft (United States) | US | Automatic Unsupervised Outlier Mode | End-to-End AutoML for Unsupervised Log Anomaly Detection | 2024 |
| Big Tech | Intel (United States) | US | Revisiting Time Series Outlier Dete | A Robust Framework for Evaluation of Unsupervised Time-Serie | 2024 |
| Big Tech | IBM Research - Ireland | IE | Therapeutics Data Commons: Machine  | Knowledge Enhanced Representation Learning for Drug Discover | 2024 |
| Big Tech | IBM Research - Zurich | CH | Therapeutics Data Commons: Machine  | Knowledge Enhanced Representation Learning for Drug Discover | 2024 |
| Big Tech | Huawei Technologies (China) | CN | XGBOD: Improving Supervised Outlier | Towards Online and Safe Configuration Tuning with Semi-super | 2024 |
| Big Tech | IBM Research - Ireland | IE | Artificial Intelligence Foundation  | Knowledge Enhanced Representation Learning for Drug Discover | 2024 |
| Big Tech | IBM Research - Zurich | CH | Artificial Intelligence Foundation  | Knowledge Enhanced Representation Learning for Drug Discover | 2024 |
| Big Tech | IBM Research - Thomas J. Watson Research Center | US | Artificial Intelligence Foundation  | A physics-inspired approach to the understanding of molecula | 2024 |
| Big Tech | Microsoft Research (United Kingdom) | GB | ECOD: Unsupervised Outlier Detectio | Outlier Detection in Temporal and Spatial Sequences Via Corr | 2024 |
| Big Tech | Tencent (China) | CN | ADBench: Anomaly Detection Benchmar | Improving Generalizability of Graph Anomaly Detection Models | 2023 |
| Big Tech | Microsoft Research (United Kingdom) | GB | ADBench: Anomaly Detection Benchmar | ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy  | 2023 |
| Big Tech | Microsoft Research Asia (China) | CN | ADBench: Anomaly Detection Benchmar | UADB: Unsupervised Anomaly Detection Booster | 2023 |
| Big Tech | Alibaba Group (China) | CN | ADBench: Anomaly Detection Benchmar | ADPal: Automatic Detection of Troubled Users in Online Servi | 2023 |
| Big Tech | Tencent (China) | CN | BOND: Benchmarking Unsupervised Out | Improving Generalizability of Graph Anomaly Detection Models | 2023 |
| Big Tech | Microsoft Research (United Kingdom) | GB | BOND: Benchmarking Unsupervised Out | ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy  | 2023 |
| Big Tech | Tencent (China) | CN | Contrastive Attributed Network Anom | Improving Generalizability of Graph Anomaly Detection Models | 2023 |
| Big Tech | Microsoft Research (United Kingdom) | GB | Automatic Unsupervised Outlier Mode | ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy  | 2023 |
| Big Tech | Alibaba Group (United States) | US | Revisiting Time Series Outlier Dete | DCdetector: Dual Attention Contrastive Representation Learni | 2023 |
| Big Tech | Alibaba Group (China) | CN | Revisiting Time Series Outlier Dete | DCdetector: Dual Attention Contrastive Representation Learni | 2023 |
| Big Tech | Microsoft Research (United Kingdom) | GB | SUOD: Accelerating Large-scale Unsu | TraceArk: Towards Actionable Performance Anomaly Alerting fo | 2023 |
| Big Tech | Microsoft (Norway) | NO | SUOD: Accelerating Large-scale Unsu | TraceArk: Towards Actionable Performance Anomaly Alerting fo | 2023 |
| Big Tech | Microsoft Research Asia (China) | CN | SUOD: Accelerating Large-scale Unsu | UADB: Unsupervised Anomaly Detection Booster | 2023 |
| Big Tech | IBM Research - Zurich | CH | Therapeutics Data Commons: Machine  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | IBM (United Kingdom) | GB | Therapeutics Data Commons: Machine  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | IBM Research - Tokyo | JP | Therapeutics Data Commons: Machine  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | IBM Research - Almaden | US | Therapeutics Data Commons: Machine  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | Google (United States) | US | Therapeutics Data Commons: Machine  | Olympus, enhanced: benchmarking mixed-parameter and multi-ob | 2023 |
| Big Tech | Microsoft Research (United Kingdom) | GB | AutoAudit: Mining Accounting and Ti | ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy  | 2023 |
| Big Tech | Microsoft Research (United Kingdom) | GB | LSCP: Locally Selective Combination | ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy  | 2023 |
| Big Tech | Microsoft Research Asia (China) | CN | Music Artist Classification with Co | MovieFactory: Automatic Movie Creation from Text using Large | 2023 |
| Big Tech | Nvidia (United States) | US | Artificial Intelligence Foundation  | Scientific discovery in the age of artificial intelligence | 2023 |
| Big Tech | Google (United Kingdom) | GB | Artificial Intelligence Foundation  | Scientific discovery in the age of artificial intelligence | 2023 |
| Big Tech | Microsoft Research Asia (China) | CN | Artificial Intelligence Foundation  | Scientific discovery in the age of artificial intelligence | 2023 |
| Big Tech | Microsoft (Netherlands) | NL | Artificial Intelligence Foundation  | Scientific discovery in the age of artificial intelligence | 2023 |
| Big Tech | IBM Research - Zurich | CH | Artificial Intelligence Foundation  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | IBM (United Kingdom) | GB | Artificial Intelligence Foundation  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | IBM Research - Tokyo | JP | Artificial Intelligence Foundation  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | IBM Research - Almaden | US | Artificial Intelligence Foundation  | Accelerating material design with the generative toolkit for | 2023 |
| Big Tech | IBM Research - Zurich | CH | Artificial Intelligence Foundation  | The rise of automated curiosity-driven discoveries in chemis | 2023 |
| Big Tech | Google (United States) | US | Artificial Intelligence Foundation  | Olympus, enhanced: benchmarking mixed-parameter and multi-ob | 2023 |
| Big Tech | IBM Research - Thomas J. Watson Research Center | US | Artificial Intelligence Foundation  | Evaluating the roughness of structure–property relationships | 2023 |
| Big Tech | Salesforce (United States) | US | ECOD: Unsupervised Outlier Detectio | Unsupervised Skin Lesion Segmentation via Structural Entropy | 2023 |
| Big Tech | Microsoft Research Asia (China) | CN | ECOD: Unsupervised Outlier Detectio | UADB: Unsupervised Anomaly Detection Booster | 2023 |
| Big Tech | Amazon (United Kingdom) | GB | ECOD: Unsupervised Outlier Detectio | Low-Count Time Series Anomaly Detection | 2023 |
| Big Tech | Amazon (United States) | US | ECOD: Unsupervised Outlier Detectio | Low-Count Time Series Anomaly Detection | 2023 |
| Big Tech | Amazon (United Kingdom) | GB | ECOD: Unsupervised Outlier Detectio | Low-count Time Series Anomaly Detection | 2023 |
| Big Tech | Huawei Technologies (France) | FR | Automatic Unsupervised Outlier Mode | Human readable network troubleshooting based on anomaly dete | 2022 |
| Big Tech | Samsung (South Korea) | KR | Revisiting Time Series Outlier Dete | Towards a Rigorous Evaluation of Time-Series Anomaly Detecti | 2022 |
| Big Tech | Alibaba Group (China) | CN | Revisiting Time Series Outlier Dete | TFAD | 2022 |
| Big Tech | Alibaba Group (United States) | US | Revisiting Time Series Outlier Dete | TFAD | 2022 |
| Big Tech | IBM Research - Thomas J. Watson Research Center | US | Revisiting Time Series Outlier Dete | Deep Learning for Time Series Anomaly Detection: A Survey | 2022 |
| Big Tech | Huawei Technologies (France) | FR | SUOD: Toward Scalable Unsupervised  | The New Abnormal: Network Anomalies in the AI Era | 2021 |
| Big Tech | Alibaba Group (China) | CN | LSCP: Locally Selective Combination | A spatial-compositional feature fusion convolutional autoenc | 2021 |
| Big Tech | Adobe Systems (United States) | US | LSCP: Locally Selective Combination | IPOF: An Extremely and Excitingly Simple Outlier Detection B | 2021 |
| Big Tech | Tencent (China) | CN | Music Artist Classification with Co | Large-scale singer recognition using deep metric learning: a | 2021 |
| Big Tech | Adobe Systems (United States) | US | XGBOD: Improving Supervised Outlier | Towards addressing unauthorized sharing of subscriptions | 2021 |
| Big Tech | Adobe Systems (United States) | US | XGBOD: Improving Supervised Outlier | Virtual-SRE For Monitoring Large Scale Time-series Data | 2021 |
| Big Tech | Alibaba Group (China) | CN | LSCP: Locally Selective Combination | Modeling Heterogeneous Statistical Patterns in High-dimensio | 2020 |
| Big Tech | Samsung (South Korea) | KR | An Empirical Study of Touch-based A | The Personal Identification Chord | 2018 |
| Consulting | Deloitte (United States) | US | SUOD: Accelerating Large-scale Unsu | Fraud detection in healthcare claims using machine learning: | 2024 |
| Finance | BlackRock (United States) | US | ADBench: Anomaly Detection Benchmar | Can an unsupervised clustering algorithm reproduce a categor | 2024 |
| Finance | BlackRock (United States) | US | The Need for Unsupervised Outlier M | Can an unsupervised clustering algorithm reproduce a categor | 2024 |
| Finance | Visa (United States) | US | ADBench: Anomaly Detection Benchmar | Tackling Diverse Minorities in Imbalanced Classification | 2023 |
| Finance | Capital One (United States) | US | TODS: An Automated Time Series Outl | From Detection to Action: a Human-in-the-loop Toolkit for An | 2023 |
| Finance | Visa (United Kingdom) | GB | TODS: An Automated Time Series Outl | Time Series Synthesis Using the Matrix Profile for Anonymiza | 2023 |
| Finance | Morgan Stanley (United States) | US | SynC: A Copula based Framework for  | A supervised generative optimization approach for tabular da | 2023 |
| Finance | Visa (United States) | US | XGBOD: Improving Supervised Outlier | Tackling Diverse Minorities in Imbalanced Classification | 2023 |
| Finance | BlackRock (United States) | US | The Need for Unsupervised Outlier M | Quantifying Outlierness of Funds from their Categories using | 2023 |
| Healthcare | Mayo Clinic in Florida | US | TrustLLM: Trustworthiness in Large  | Ethical framework for responsible foundational models in med | 2025 |
| Industrial | Robert Bosch (United States) | US | ADBench: Anomaly Detection Benchmar | Model Selection of Anomaly Detectors in the Absence of Label | 2025 |
| Industrial | Robert Bosch (Germany) | DE | XGBOD: Improving Supervised Outlier | The OPS-SAT benchmark for detecting anomalies in satellite t | 2025 |
| Industrial | Robert Bosch (United States) | US | The Need for Unsupervised Outlier M | Model Selection of Anomaly Detectors in the Absence of Label | 2025 |
| Industrial | Robert Bosch (Germany) | DE | ECOD: Unsupervised Outlier Detectio | The OPS-SAT benchmark for detecting anomalies in satellite t | 2025 |
| Industrial | Siemens (China) | CN | ADBench: Anomaly Detection Benchmar | PARs: Predicate-based Association Rules for Efficient and Ac | 2024 |
| Industrial | Honeywell (France) | FR | Employee Turnover Prediction with M | Identifying Survival-Changing Sequential Patterns for Employ | 2023 |
| Industrial | Robert Bosch (Germany) | DE | SUOD: Accelerating Large-scale Unsu | On Why the System Makes the Corner Case: AI-based Holistic A | 2022 |
| Pharma | Novartis (China) | CN | Therapeutics Data Commons: Machine  | Machine Learning for Toxicity Prediction Using Chemical Stru | 2025 |
| Pharma | Eli Lilly (United States) | US | Therapeutics Data Commons: Machine  | Machine Learning for Toxicity Prediction Using Chemical Stru | 2025 |
| Pharma | AstraZeneca (Sweden) | SE | Therapeutics Data Commons: Machine  | Machine Learning for Toxicity Prediction Using Chemical Stru | 2025 |
| Pharma | Pfizer (United States) | US | Therapeutics Data Commons: Machine  | Machine Learning for Toxicity Prediction Using Chemical Stru | 2025 |
| Pharma | Sanofi (France) | FR | Therapeutics Data Commons: Machine  | Machine Learning for Toxicity Prediction Using Chemical Stru | 2025 |
| Pharma | Merck & Co., Inc., Rahway, NJ, USA (United States) | US | Therapeutics Data Commons: Machine  | Data Scaling and Generalization Insights for Medicinal Chemi | 2025 |
| Pharma | Roche (Switzerland) | CH | Artificial Intelligence Foundation  | Combinatorial prediction of therapeutic perturbations using  | 2025 |
| Pharma | Merck & Co., Inc., Rahway, NJ, USA (United States) | US | Artificial Intelligence Foundation  | Combinatorial prediction of therapeutic perturbations using  | 2025 |
| Pharma | AstraZeneca (Sweden) | SE | Therapeutics Data Commons: Machine  | Human-in-the-loop active learning for goal-oriented molecule | 2024 |
| Pharma | AstraZeneca (Sweden) | SE | Therapeutics Data Commons: Machine  | Using test-time augmentation to investigate explainable AI:  | 2024 |
| Pharma | AstraZeneca (Japan) | JP | Therapeutics Data Commons: Machine  | Registries in Machine Learning-Based Drug Discovery: A Short | 2024 |
| Pharma | AstraZeneca (Finland) | FI | Therapeutics Data Commons: Machine  | Registries in Machine Learning-Based Drug Discovery: A Short | 2024 |
| Pharma | AstraZeneca (United Kingdom) | GB | Therapeutics Data Commons: Machine  | Registries in Machine Learning-Based Drug Discovery: A Short | 2024 |
| Pharma | Novartis (Switzerland) | CH | Artificial Intelligence Foundation  | A call for an industry-led initiative to critically assess m | 2024 |
| Pharma | Merck & Co., Inc., Rahway, NJ, USA (United States) | US | Artificial Intelligence Foundation  | A call for an industry-led initiative to critically assess m | 2024 |
| Pharma | Pfizer (Germany) | DE | Artificial Intelligence Foundation  | A call for an industry-led initiative to critically assess m | 2024 |
| Pharma | AstraZeneca (Sweden) | SE | Artificial Intelligence Foundation  | A call for an industry-led initiative to critically assess m | 2024 |
| Pharma | Sanofi (France) | FR | Artificial Intelligence Foundation  | Deep Batch Active Learning for Drug Discovery | 2024 |
| Pharma | Sanofi (United States) | US | Artificial Intelligence Foundation  | Deep Batch Active Learning for Drug Discovery | 2024 |
| Pharma | Sanofi (China) | CN | Artificial Intelligence Foundation  | Deep Batch Active Learning for Drug Discovery | 2024 |
| Pharma | Sanofi (Germany) | DE | Artificial Intelligence Foundation  | Deep Batch Active Learning for Drug Discovery | 2024 |
| Pharma | Roche (Switzerland) | CH | Artificial Intelligence Foundation  | Combinatorial prediction of therapeutic perturbations using  | 2024 |
| Pharma | Merck & Co., Inc., Rahway, NJ, USA (United States) | US | Artificial Intelligence Foundation  | Combinatorial prediction of therapeutic perturbations using  | 2024 |
| Pharma | AstraZeneca (Brazil) | BR | Artificial Intelligence Foundation  | Representation Learning of Human Disease Mechanisms for a Fo | 2024 |
| Pharma | AstraZeneca (United States) | US | Artificial Intelligence Foundation  | Representation Learning of Human Disease Mechanisms for a Fo | 2024 |
| Pharma | AstraZeneca (Sweden) | SE | Artificial Intelligence Foundation  | Representation Learning of Human Disease Mechanisms for a Fo | 2024 |
| Pharma | AstraZeneca (United Kingdom) | GB | Artificial Intelligence Foundation  | Representation Learning of Human Disease Mechanisms for a Fo | 2024 |
| Pharma | AstraZeneca (Australia) | AU | Artificial Intelligence Foundation  | Representation Learning of Human Disease Mechanisms for a Fo | 2024 |
| Pharma | AstraZeneca (Sweden) | SE | Therapeutics Data Commons: Machine  | Machine learning for small molecule drug discovery in academ | 2023 |
| Pharma | Novartis Institutes for BioMedical Research | None | Therapeutics Data Commons: Machine  | Machine learning for small molecule drug discovery in academ | 2023 |
| Pharma | Novartis (Switzerland) | CH | Therapeutics Data Commons: Machine  | Machine learning for small molecule drug discovery in academ | 2023 |
| Pharma | Pfizer (Germany) | DE | Therapeutics Data Commons: Machine  | Equivariant Graph Neural Networks for Toxicity Prediction | 2023 |
| Pharma | Novartis (United States) | US | Artificial Intelligence Foundation  | Computer‐aided evaluation and exploration of chemical spaces | 2023 |
| Pharma | Novartis (Switzerland) | CH | Therapeutics Data Commons: Machine  | Chemoinformatics and Artificial Intelligence Colloquium: Pro | 2022 |
| Pharma | Novartis Institutes for BioMedical Research | None | Therapeutics Data Commons: Machine  | Chemoinformatics and Artificial Intelligence Colloquium: Pro | 2022 |
| Pharma | AstraZeneca (Sweden) | SE | Therapeutics Data Commons: Machine  | Hierarchical Clustering Split for Low-Bias Evaluation of Dru | 2021 |
| Pharma | AstraZeneca (United States) | US | Therapeutics Data Commons: Machine  | Hierarchical Clustering Split for Low-Bias Evaluation of Dru | 2021 |
| Retail | Walmart (United States) | US | PyOD: A Python Toolbox for Scalable | Anomaly Detection for an E-commerce Pricing System | 2019 |
| Telecom | Ericsson (Sweden) | SE | TODS: An Automated Time Series Outl | Resilient automatic model selection for mobility prediction | 2025 |
| Telecom | Ericsson (Sweden) | SE | ADBench: Anomaly Detection Benchmar | Data-Efficient Automatic Model Selection in Unsupervised Ano | 2022 |
| Telecom | Ericsson (Sweden) | SE | Automatic Unsupervised Outlier Mode | Data-Efficient Automatic Model Selection in Unsupervised Ano | 2022 |
| Telecom | Cisco Systems (United States) | US | TODS: An Automated Time Series Outl | Traffic Anomaly Detection Via Conditional Normalizing Flow | 2022 |

## Summary by Institution

| Institution | Category | Work-Citations |
|-----------|----------|---------------|
| Microsoft Research (United Kingdom) | Big Tech | 9 |
| Tencent (China) | Big Tech | 8 |
| AstraZeneca (Sweden) | Pharma | 7 |
| IBM Research - Zurich | Big Tech | 7 |
| Amazon (United States) | Big Tech | 6 |
| Microsoft Research Asia (China) | Big Tech | 6 |
| IBM Research - Thomas J. Watson Research Center | Big Tech | 6 |
| Adobe Systems (United States) | Big Tech | 5 |
| Huawei Technologies (China) | Big Tech | 5 |
| Alibaba Group (China) | Big Tech | 5 |
| Merck & Co., Inc., Rahway, NJ, USA (United States) | Pharma | 4 |
| National Institutes of Health | US Government | 3 |
| Ericsson (Sweden) | Telecom | 3 |
| BlackRock (United States) | Finance | 3 |
| Robert Bosch (Germany) | Industrial | 3 |
| IBM (United States) | Big Tech | 3 |
| Novartis (Switzerland) | Pharma | 3 |
| OpenAI (United States) | Foundation Model Co | 2 |
| Fraunhofer Institute for Mechatronic Systems Design | Research Institute | 2 |
| Los Alamos National Laboratory | National Lab | 2 |
| Brookhaven National Laboratory | National Lab | 2 |
| Deutsche Bundesbank | Central Bank | 2 |
| Robert Bosch (United States) | Industrial | 2 |
| Visa (United States) | Finance | 2 |
| Huawei Technologies (France) | Big Tech | 2 |
| Samsung (South Korea) | Big Tech | 2 |
| Alibaba Group (United States) | Big Tech | 2 |
| Intel (United States) | Big Tech | 2 |
| Sanofi (France) | Pharma | 2 |
| Novartis Institutes for BioMedical Research | Pharma | 2 |
| IBM (United Kingdom) | Big Tech | 2 |
| IBM Research - Tokyo | Big Tech | 2 |
| IBM Research - Almaden | Big Tech | 2 |
| AstraZeneca (United States) | Pharma | 2 |
| Google (United States) | Big Tech | 2 |
| IBM Research - Ireland | Big Tech | 2 |
| Pfizer (Germany) | Pharma | 2 |
| AstraZeneca (United Kingdom) | Pharma | 2 |
| Roche (Switzerland) | Pharma | 2 |
| Amazon (United Kingdom) | Big Tech | 2 |
| Jet Propulsion Laboratory | Space Agency | 1 |
| Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR) | Space Agency | 1 |
| Argonne National Laboratory | National Lab | 1 |
| Sandia National Laboratories | National Lab | 1 |
| Google DeepMind (United Kingdom) | Foundation Model Co | 1 |
| Pacific Northwest National Laboratory | National Lab | 1 |
| Fraunhofer Institute for Translational Medicine and Pharmacology | Research Institute | 1 |
| Fraunhofer Institute for Algorithms and Scientific Computing | Research Institute | 1 |
| Centers for Disease Control and Prevention | US Government | 1 |
| Deutsches Elektronen-Synchrotron DESY | International Lab | 1 |
| Fraunhofer Institute for Open Communication Systems | Research Institute | 1 |
| Mayo Clinic in Florida | Healthcare | 1 |
| Amazon (Germany) | Big Tech | 1 |
| Siemens (China) | Industrial | 1 |
| Intel (United Kingdom) | Big Tech | 1 |
| Microsoft (United States) | Big Tech | 1 |
| Deloitte (United States) | Consulting | 1 |
| Microsoft (Norway) | Big Tech | 1 |
| Huawei Technologies (United States) | Big Tech | 1 |
| Cisco Systems (United States) | Telecom | 1 |
| Capital One (United States) | Finance | 1 |
| Visa (United Kingdom) | Finance | 1 |
| Novartis (China) | Pharma | 1 |
| Eli Lilly (United States) | Pharma | 1 |
| Pfizer (United States) | Pharma | 1 |
| Nvidia (United Kingdom) | Big Tech | 1 |
| AstraZeneca (Japan) | Pharma | 1 |
| AstraZeneca (Finland) | Pharma | 1 |
| Morgan Stanley (United States) | Finance | 1 |
| Baidu (China) | Big Tech | 1 |
| Honeywell (France) | Industrial | 1 |
| Nvidia (United States) | Big Tech | 1 |
| Google (United Kingdom) | Big Tech | 1 |
| Microsoft (Netherlands) | Big Tech | 1 |
| Sanofi (United States) | Pharma | 1 |
| Sanofi (China) | Pharma | 1 |
| Sanofi (Germany) | Pharma | 1 |
| Novartis (United States) | Pharma | 1 |
| AstraZeneca (Brazil) | Pharma | 1 |
| AstraZeneca (Australia) | Pharma | 1 |
| Salesforce (United States) | Big Tech | 1 |
| Walmart (United States) | Retail | 1 |

## Coverage

**Papers with citations on OpenAlex:** 44/99

**Indexed but 0 citations (46):** Can Multimodal LLMs Perform Time Series , Charts Are Not Images: On the Challenges, Defenses Against Prompt Attacks Learn Su, Doxing via the Lens: Revealing Location-, Mitigating Hallucinations in Large Langu, Topology Matters: Measuring Memory Leaka, A Personalized Conversational Benchmark:, AD-AGENT: A Multi-agent Framework for En, Edit Away and My Face Will Not Stay: Per, Few-Shot Graph Out-of-Distribution Detec, JailDAM: Jailbreak Detection with Adapti, LLM-Empowered Patient-Provider Communica, Learning from the Storm: A Multivariate , MetaOOD: Automatic Selection of OOD Dete, Navigating Between Explainability and Ex, SocialMaze: A Benchmark for Evaluating S, TRUSTEVAL: A Dynamic Evaluation Toolkit , AutoBench-V: Can Large Vision-Language M, Hyperparameter Optimization for Unsuperv, DSV: An Alignment Validation Loss for Se, ... and 26 more

**Not found on OpenAlex (9):** CoAct: Co-Active Preference Learning wit, DecAlign: Hierarchical Cross-Modal Align, TrustGen: A Platform of Dynamic Benchmar, DyFlow: Dynamic Workflow Framework for A, Secure On-Device Video OOD Detection Wit, ELECT: Toward Unsupervised Outlier Model, Don’t Let It Hallucinate: Premise Verifi, Auditable Agents... (arXiv preprint), Can Molecular Foundation Models Know Wha

*OpenAlex coverage improves over time. Re-run in 3-6 months to capture newly indexed papers.*