Eric Xing
John F. Nash, Jr. Professor of Machine Learning, Director of MBZUAI-CMU Institute of Virtual and Programmable Cells, Associate Department Head
Studies machine learning and AI systems, including large foundation models, distributed learning and computational biology.
Eric Xing's lab at CMU works on generative & foundation models, distributed, cloud & edge systems and large language models. 77 papers since 2023, 55 at top venues, mostly NeurIPS and ICLR. 16 PhD students. 60 alumni, most often to Waymo. Applications close .
At a glance
- papers, 2023–2026
- 20/ year
- at T1 venues
- 71%
- PhD students
- 16
- MS students
- 0
- alumni listed
- 60
Papers by yearfrom the lab homepage, 2023–2026
- 2 papers2026 · T1 2 · collecting
- 27 papers2025 · T1 15
- 25 papers2024 · T1 16
- 23 papers2023 · T1 22
T1 venuesOtherVenues: NeurIPS, ICLR, CVPR, ICML, MLSys
Research focuspaper titles that use each topic's words
- Large language models36 papers
- Generative & foundation models23 papers
- Distributed, cloud & edge systems3 papers
- distributed learning0 papers
- computational biology0 papers
Paper list
From the lab homepage.
20262 papers
202527 papers
- Nature BiotechnologyA Call for Built-in Biosecurity Safeguards for Generative AI Tools (opens in a new tab)
- Nature (T1)A Foundation Model for Continuous Glucose Monitoring Data
- ICLR (T1)Causal Representation Learning from Multimodal Biomedical Observations (opens in a new tab)
- Proceedings of the Conference on Language Modeling, .CITER: Collaborative Inference for Efficient Large Language Model Decoding with Token-Level Routing (opens in a new tab)
- ACM Multimedia Demo and Video Track.CReLeRI: Explainable, Concept-centric, Representation, Learning, Reasoning, and Interaction Video Analysis System (opens in a new tab)
- ICML (T1)Data Mixing Optimization for Supervised Fine-Tuning of Large Language Models (opens in a new tab)
- NeurIPS (T1)Dimensional Collapse in VQVAEs: Evidence and Remedies (opens in a new tab)
- NeurIPS (T1)Faster Video Diffusion with Trainable Sparse Attention (opens in a new tab)
- ICLR (T1)Follow My Instruction and Spill the Beans: Scalable Data Extraction from Retrieval-Augmented Generation Systems (opens in a new tab)
- Nature (T1)GET: A Foundation Model of Transcription Across Human Cell Types (opens in a new tab)
- Advances in Neural Information Processing Systems 39 (NeurIPS)Guru: Incentivizing General Reasoning Skills with a Curated Open Reinforcement Learning Dataset (opens in a new tab)
- Advances in Neural Information Processing Systems 39 (NeurIPS)In Search of Lost Language Models Training Dynamics (opens in a new tab)
202425 papers
- The Conference of the North American Chapter of the Association for Computational Linguistics.A Study on the Calibration of In-context Learning. (opens in a new tab)
- Proceedings of the International Conference on Intelligence Systems for Molecular Biology, .AttentionPert: Accurately Modeling Multiplexed Genetic Perturbations with Multi-scale Effects. (opens in a new tab)
- ICML (T1)Contextualized Policy Recovery: Modeling and Interpreting Medical Decisions with Adaptive Imitation Learning. (opens in a new tab)
- Proceedings of the Conference on Language Modeling, .Crystal: Illuminating LLM Abilities on Language and Code. (opens in a new tab)
- Proceedings of the Conference on Language Modeling, .DISTFLASHATTN: Distributed Memory-efficient Attention for Long-context LLMs Training. (opens in a new tab)
- MLSys (T1)Does Compressing Activations Help Model Parallel Training?. (opens in a new tab)
- Proceeding of the Conference on Empirical Methods on Natural Language Processing.Dynamic Rewarding with Prompt Optimization Enables Tuning-free Self-Alignment of Language Models.
- CVPR (T1)Efficient Test-Time Adaptation of Vision-Language Models. (opens in a new tab)
- The Conference of the North American Chapter of the Association for Computational Linguistics.Evaluating Step-by-Step Reasoning through Symbolic Verification. (opens in a new tab)
- Proceeding of the Conference on Empirical Methods on Natural Language Processing.Fast Matrix Multiplications for Lookup Table-Quantized LLMs. (opens in a new tab)
- CVPR (T1)FreGS: 3D Gaussian Splatting with Progressive Frequency Regularization. (opens in a new tab)
- ICLR (T1)Fusing Models with Complementary Expertise (opens in a new tab)
202323 papers
- CVPR (T1)3D Semantic Segmentation in the Wild: Learning Generalized Models for Adverse-Condition Point Clouds (opens in a new tab)
- ACL (T1)BertNet: Harvesting Knowledge Graphs with Arbitrary Relations from Pretrained Language Models (opens in a new tab)
- ICLR (T1)Betty: An Automatic Differentiation Library for Multilevel Optimization (opens in a new tab)
- NeurIPS (T1)Cappy: Outperforming and Boosting Large MultiTask LMs with a Small Scorer (opens in a new tab)
- NeurIPS (T1)Counterfactual Generation with Identifiability Guarantee (opens in a new tab)
- MLSys (T1)Cuttlefish: Low-Rank Model Training without All the Tuning (opens in a new tab)
- TMLR (T1)Exploring Transformer Backbones for Heterogeneous Treatment Effect Estimation (opens in a new tab)
- ICLR (T1)Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach (opens in a new tab)
- NeurIPS (T1)FedNAR: Federated Optimization with Normalized Annealing Regularization (opens in a new tab)
- NeurIPS (T1)Identification of Nonlinear Latent Hierarchical Models (opens in a new tab)
- ACL (T1)Improved Logical Reasoning of Language Models via Differentiable Symbolic Programming (opens in a new tab)
- TKDE (T1)Iterative Graph Self-Distillation (opens in a new tab)
Where alumni went60 listed, 100% to industry
Industry 1Academia 0
Most often to
- Waymo
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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