Carlee Joe-Wong
Sense of Wonder Group Professor
Studies network economics and distributed and federated machine learning.
Carlee Joe-Wong's lab at CMU works on trustworthy & safe ML, reasoning & neuro-symbolic AI and distributed, cloud & edge systems. 55 papers since 2023, 17 at top venues, mostly NeurIPS and AISTATS. 9 PhD students and 1 master's student. 11 alumni, most often to CACI Inc and Meta. Applications close .
At a glance
- papers, 2023–2026
- 14/ year
- at T1 venues
- 31%
- PhD students
- 9
- MS students
- 1
- alumni listed
- 11
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 10 papers2025 · T1 6
- 19 papers2024 · T1 7
- 26 papers2023 · T1 4
T1 venuesOtherVenues: NeurIPS, AISTATS, INFOCOM, AAAI, MLSys
Research focuspaper titles that use each topic's words
- Distributed, cloud & edge systems15 papers
- Trustworthy & safe ML1 paper
- Reasoning & neuro-symbolic AI0 papers
- network economics0 papers
- federated machine0 papers
Paper list
From the lab homepage.
202510 papers
- IEEE Transactions on Signal ProcessingEmpowering Federated Learning with Implicit Gossiping: Mitigating Connection Unreliability Amidst Unknown and Arbitrary Dynamics (opens in a new tab)
- AISTATS (T1)FedBaF: Federated Learning Aggregation Biased by a Foundation Model (opens in a new tab)
- AISTATS (T1)Federated Communication-Efficient Multi-Objective Optimization (opens in a new tab)
- FLUIDFedGAT: A Privacy-Preserving Federated Approximation Algorithm for Graph Attention Networks (opens in a new tab)
- ICASSP (T1)FedTLU: Federated Learning with Targeted Layer Updates (opens in a new tab)
- MLSys (T1)Interference-aware Edge Runtime Prediction with Conformal Matrix Completion (opens in a new tab)
- AAAI WorkshopLLM-Powered Decentralized Generative Agents with Adaptive Hierarchical Knowledge Graph for Cooperative Planning (opens in a new tab)
- AAAI (T1)Neural Combinatorial Clustered Bandits for Recommendation Systems (opens in a new tab)
- ICLR (T1)Pairwise Elimination with Instance-Dependent Guarantees for Bandits with Cost Subsidy (opens in a new tab)
- AI4WCNTowards Adversarially Robust Human-in-the-Loop Learning for HVAC Systems (opens in a new tab)
202419 papers
- FMSysAn LLM-Based Digital Twin for Optimizing Human-in-the Loop Systems (opens in a new tab)
- FMSysCoRAST: Towards Foundation Model-Powered Correlated Data Analysis in Resource-Constrained CPS and IoT (opens in a new tab)
- INFOCOM (T1)Distributed Experimental Design Networks (opens in a new tab)
- INFOCOM (T1)Edge-MSL: Split Learning on the Mobile Edge via Multi-Armed Bandits (opens in a new tab)
- NeurIPS (T1)Efficient Contextual LLM Cascades through Budget-Constrained Policy Learning (opens in a new tab)
- NeurIPS (T1)Efficient Federated Learning against Heterogeneous and Non-stationary Client Unavailability (opens in a new tab)
- UAI (T1)FedAST: Federated Asynchronous Simultaneous Training (opens in a new tab)
- ECML-PKDDFederated Learning with Flexible Architectures (opens in a new tab)
- ACM KDD Applied Data Science TrackFedSecurity: A Benchmark for Attacks and Defenses in Federated Learning and Federated LLMs (opens in a new tab)
- IEEE/ACM Transactions on NetworkingHow Valuable Is Your Data? Optimizing Device Recruitment in Federated Learning (opens in a new tab)
- poster at ACM COMPASS .HumekaFL: Automated Detection of Neonatal Asphyxia Using Federated Learning
- poster at ACM/IEEE IPSN .Listen and Then Sense: Vibration-based Sports Crowd Monitoring by Pre-training with Public Audio Datasets
202326 papers
- CISSACRE: Actor Critic Reinforcement Learning for Failure-Aware Edge Computing Migrations (opens in a new tab)
- IEEE/ACM IWQoSAdaCoOpt: Leverage the Interplay of Batch Size and Aggregation Frequency for Federated Learning (opens in a new tab)
- BUGSAdversarial Robustness Unhardening via Backdoor Attacks in Federated Learning (opens in a new tab)
- poster at ACM APNET .An Online Control Approach of Collaborative Federated Learning with Constrained Resources
- ACM MobiHocCache-Enabled Federated Learning Systems (opens in a new tab)
- AISTATS (T1)Characterizing Internal Evasion Attacks in Federated Learning (opens in a new tab)
- IEEE/IFIP WiOptDOLL: Distributed OnLine Learning using Preemptible Cloud Instances (opens in a new tab)
- IEEE Transactions on Network Science and EngineeringDynamic Coupling Strategy for Interdependent Network Systems Against Cascading Failures (opens in a new tab)
- IEEE Transactions on Mobile ComputingDYNAMITE: Dynamic Interplay of Mini-Batch Size and Aggregation Frequency for Federated Learning with Static and Streaming Dataset (opens in a new tab)
- IEEE SECONEdgeC3: Online Management for Edge-Cloud Collaborative Continuous Learning (opens in a new tab)
- ICCEvaluating the Optimality of Dynamic Coupling Strategies in Interdependent Network Systems
- held in conjunction with IEEE INFOCOM .Federated Learning at the Edge: An Interplay of Mini-batch Size and Aggregation Frequency, FOGML 2023
Where alumni went11 listed, 38% to industry
Industry 3Academia 5
Most often to
- CACI Inc
- Meta
- TensorOpera AI as a Gen AI Research Scientist
Also to
- Caltech
- Cornell University
- Sun Yat-Sen University as an Associate Professor in the School of Computer Science and Engineering
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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