Emily Fox
Professor of Statistics and of Computer Science
Develops Bayesian and time-series machine learning methods with applications to health.
Emily Fox's lab at Stanford works on probabilistic & causal inference. 23 papers since 2023, 10 at top venues, mostly ICML and Cell. Applications close .
At a glance
- papers, 2023–2026
- 6/ year
- at T1 venues
- 43%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom the lab homepage, 2023–2026
- 5 papers2026 · T1 3 · collecting
- 5 papers2025 · T1 3
- 7 papers2024 · T1 3
- 6 papers2023 · T1 1
T1 venuesOtherVenues: ICML, Cell, ECCV, EMNLP, ICLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference2 papers
- bayesian1 paper
- develops bayesian0 papers
- time-series0 papers
- learning methods0 papers
Paper list
From the lab homepage.
20265 papers
- ECCV (T1)CellFluxRL: Biologically-Constrained Virtual Cell Modeling via Reinforcement Learning (opens in a new tab)
- EMNLP (T1)Continuous-Utility Direct Preference Optimization (opens in a new tab)
- Pacific Symposium on BiocomputingPertSpectra: Interpretable Matrix Factorization for Predicting Functional Impact of Genetic Perturbation Experiments (opens in a new tab)
- ICLR (T1)PETRI: Learning Unified Cell Embeddings from Unpaired Modalities via Early-Fusion Joint Reconstruction (opens in a new tab)
- COLMSwitching Linear Attention
20255 papers
- American Journal of Human GeneticsA Scalable Framework for Identifying Allelic Series from Summary Statistics (opens in a new tab)
- UAI (T1)HDP-Flow: Generalizable Bayesian Nonparametric Model for Time Series State Discovery (opens in a new tab)
- ICML (T1)KinDEL: DNA-Encoded Library Dataset for Kinase Inhibitors (opens in a new tab)
- ICLR Learning Meaningful Representations of Life (LMRL) WorkshopRoll-AE: A Spatiotemporal Invariant Autoencoder for Uncovering Neuronal Electrophysiological Patterns (opens in a new tab)
- JMLR (T1)Test-Time Regression: A Unifying Framework for Designing Sequence Models with Associative Memory (opens in a new tab)
20247 papers
- ICML (T1)Automated Statistical Model Discovery with Language Models (opens in a new tab)
- NeurIPS Statistical Foundations of LLMs and Foundation Models WorkshopCriticAL: Model Criticism Automation with Language Models (opens in a new tab)
- Cell (T1)How to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities (opens in a new tab)
- ICML (T1)Hybrid^2 Neural ODE Causal Modeling and an Application to Glycemic Response (opens in a new tab)
- Machine Learning for Health (ML4H) SymposiumLearning Explainable Treatment Policies with Clinician-Informed Representations: A Practical Approach (opens in a new tab)
- NEJM EvidenceSmart Start: Designing Powerful Clinical Trials Using Pilot Study Data (opens in a new tab)
- to appear in Journal of Neural EngineeringUsing a Linear Dynamic System to Measure Functional Connectivity from M/EEG (opens in a new tab)
20236 papers
- EpilepsiaA Pharmacokinetic Model of Anti-Seizure Medication Load to Guide Care in the Epilepsy Monitoring Unit (opens in a new tab)
- Frontiers in EndocrinologyAdding glycemic and physical activity metrics to a multimodal algorithm-enabled decision-support tool for type 1 diabetes care: Keys to implementation and opportunities (opens in a new tab)
- Machine Learning for Health (ML4H) SymposiumInterpretable Mechanistic Representations for Meal-level Glycemic Control in the Wild (opens in a new tab)
- ICML (T1)Sequence Modeling with Multiresolution Convolutional Memory (opens in a new tab)
- Bayesian AnalysisStochastic Gradient MCMC for Nonlinear State Space Models (opens in a new tab)
- Journal of Clinical and Translational ScienceThe Evolving Role of Data & Safety Monitoring Boards for Real-World Clinical Trials (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to StanfordFall 2027
Stanford Computer Science PhD
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- Opens
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- English
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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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Tiers are venue classifications, not lab rankings.
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