Christos Faloutsos
Fredkin University Professor of Computer Science
Mines large graphs and time series, with work on graph patterns, anomaly detection and fraud detection.
Christos Faloutsos's lab at CMU works on graph mining & networks, time series, anomaly & spatio-temporal data and recognition, detection & segmentation. 55 papers since 2023, 33 at top venues, mostly ICLR and VLDB. Applications close .
At a glance
- papers, 2023–2026
- 14/ year
- at T1 venues
- 60%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage and DBLP, 2023–2026
- 5 papers2026 · T1 3 · collecting
- 12 papers2025 · T1 8
- 21 papers2024 · T1 15
- 17 papers2023 · T1 7
T1 venuesOtherVenues: ICLR, VLDB, ICDE, KDD, NeurIPS
Research focuspaper titles that use each topic's words
- Time series, anomaly & spatio-temporal data21 papers
- Graph mining & networks18 papers
- Recognition, detection & segmentation7 papers
- detection7 papers
- large graphs0 papers
Paper list
From the lab homepage and DBLP.
20265 papers
- PAKDDEBVC: Electronic Bee Veterinarian - Beyond Monitoring and Onto Control (opens in a new tab)
- VLDB (T1)From Big Time Series Forecasting to Foundation Models for Structured Data (opens in a new tab)
- ICCCNNetCause: Counterfactual Learning for Root Cause Analysis in Large-Scale Networks (opens in a new tab)
- ICLR (T1)Understanding the Implicit Biases of Design Choices for Time Series Foundation Models (opens in a new tab)
- ICLR (T1)Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility (opens in a new tab)
202512 papers
- ICML (T1)CurvGAD: Leveraging Curvature for Enhanced Graph Anomaly Detection (opens in a new tab)
- ICML (T1)Enhancing Foundation Models for Time Series Forecasting via Wavelet-based Tokenization (opens in a new tab)
- ICDE (T1)Featpilot: Automatic Feature Augmentation on Tabular Data (opens in a new tab)
- ACL (T1)HybGRAG: Hybrid Retrieval-Augmented Generation on Textual and Relational Knowledge Bases (opens in a new tab)
- WWW (T1)Kronecker Generative Models for Power-Law Patterns in Real-World Hypergraphs (opens in a new tab)
- NeurIPS (T1)Mitra: Mixed Synthetic Priors for Enhancing Tabular Foundation Models (opens in a new tab)
- VLDB (T1)OmniMatch: Joinability Discovery in Data Products (opens in a new tab)
- Findings of NAACLPolyJoin: Semantic Multi-key Joinable Table Search in Data Lakes (opens in a new tab)
- ACM Trans. Knowl. Discov. DataPrincipled Mining, Forecasting, and Monitoring of Honeybee Time Series with EBV+ (opens in a new tab)
- TrustComPrototype-Integrated Representation Learning for Novelty Detection (opens in a new tab)
- ICLR (T1)Spectro-Riemannian Graph Neural Networks (opens in a new tab)
- Principled Mining, Forecasting, and Monitoring of Honeybee Time Series with EBV+Time Series with EBV+
202421 papers
- NeurIPS (T1)4DBInfer: A 4D Benchmarking Toolbox for Graph-Centric Predictive Modeling on RDBs (opens in a new tab)
- VLDB (T1)A Flexible Forecasting Stack (opens in a new tab)
- VLDB Journal (T1)Correction to: TurboLift: fast accuracy lifting for historical data recovery (opens in a new tab)
- EMNLP (T1)CoverICL: Selective Annotation for In-Context Learning via Active Graph Coverage (opens in a new tab)
- ICDE (T1)DATALORE: Can a Large Language Model Find All Lost Scrolls in a Data Repository? (opens in a new tab)
- PAKDDDiffFind: Discovering Differential Equations from Time Series (opens in a new tab)
- SDMEBV: Electronic Bee-Veterinarian for Principled Mining and Forecasting of Honeybee Time Series (opens in a new tab)
- J. Data-centric Mach. Learn. Res.Evaluating Durability: Benchmark Insights into Image and Text Watermarking (opens in a new tab)
- KDD (T1)Graph Machine Learning Meets Multi-Table Relational Data (opens in a new tab)
- KDD (T1)GraphStorm: All-in-one Graph Machine Learning Framework for Industry Applications (opens in a new tab)
- Trans. Mach. Learn. Res.Large Language Models (LLMs) on Tabular Data: Prediction, Generation, and Understanding - A Survey (opens in a new tab)
- ICDE (T1)Mccatch: Scalable Microcluster Detection in Dimensional and Nondimensional Datasets (opens in a new tab)
202317 papers
- VLDB (T1)Accelerating Similarity Search for Elastic Measures: A Study and New Generalization of Lower Bounding Distances (opens in a new tab)
- Findings of ACLAutomatic Table Union Search with Tabular Representation Learning (opens in a new tab)
- J. Biomed. InformaticsBenefit-aware early prediction of health outcomes on multivariate EEG time series (opens in a new tab)
- CIKM (T1)CallMine: Fraud Detection and Visualization of Million-Scale Call Graphs (opens in a new tab)
- Findings of ACLConcept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs (opens in a new tab)
- WIREs Data. Mining. Knowl. Discov.DeepFixCX: Explainable privacy-preserving image compression for medical image analysis (opens in a new tab)
- ACM Trans. Knowl. Discov. DataDeltaShield: Information Theory for Human- Trafficking Detection (opens in a new tab)
- WIREs Data. Mining. Knowl. Discov.ExplainFix: Explainable spatially fixed deep networks (opens in a new tab)
- CBMSExploratory Data Analysis in Electronic Health Records Graphs: Intuitive Features and Visualization Tools (opens in a new tab)
- NeurIPS (T1)GLEMOS: Benchmark for Instantaneous Graph Learning Model Selection (opens in a new tab)
- KDD (T1)Less is More: SlimG for Accurate, Robust, and Interpretable Graph Mining (opens in a new tab)
- ICLR (T1)MetaGL: Evaluation-Free Selection of Graph Learning Models via Meta-Learning (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CMUFall 2027
CMU SCS Computer Science PhD
- Round
- Single round
- Opens
- GRE
- See program page
- English
- TOEFL / IELTS / Duolingo (F-1/J-1 non-native speakers)
- Fee
- USD 100 (USD 80 by early deadline)
Confirmed on the program page.
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Data
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- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
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- The lab homepage and DBLP
- Members
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- Alumni
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Tiers are venue classifications, not lab rankings.
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