Caroline Uhler
Andrew (1956) and Erna Viterbi Professor; Professor of EECS and in IDSS
Develops machine learning methods that integrate data modalities and infer causal relationships, applied to regulatory circuits in cells and tissues.
Caroline Uhler's lab at MIT works on probabilistic & causal inference. 35 papers since 2023, 20 at top venues, mostly Nature Communications and NeurIPS. 1 PhD student and 1 master's student. 65 alumni. Applications close .
At a glance
- papers, 2023–2026
- 9/ year
- at T1 venues
- 57%
- PhD students
- 1
- MS students
- 1
- alumni listed
- 65
Papers by yearfrom the lab homepage, 2023–2026
- 0 papers2026 · T1 0 · collecting
- 12 papers2025 · T1 6
- 11 papers2024 · T1 5
- 12 papers2023 · T1 9
T1 venuesOtherVenues: Nature Communications, NeurIPS, PNAS, AISTATS, ICLR
Research focuspaper titles that use each topic's words
- Probabilistic & causal inference14 papers
- cells1 paper
- integrate0 papers
- regulatory0 papers
- applied0 papers
Paper list
From the lab homepage.
202512 papers
- npj Digital Medicine 8A representation fusion framework for decoupling diagnostic information in multimodal learning (opens in a new tab)
- ICLR (T1)An information criterion for controlled disentanglement of multimodal data (opens in a new tab)
- Scientific ReportsDeep learning-based analysis reveals patient-level cancer therapy trajectories using single-cell PBMC chromatin images (opens in a new tab)
- PNAS (T1)Efficiently quantifying dependence in massive scientific datasets using InterDependence Scores (opens in a new tab)
- Nature Communications (T1)Genome-scale spatial mapping of the Hodgkin lymphoma microenvironment identifies tumor cell survival factors (opens in a new tab)
- Cell SystemsImage2Reg: Linking chromatin images to gene regulation using genetic perturbation screens (opens in a new tab)
- Nature Communications (T1)Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns (opens in a new tab)
- PNAS (T1)Latent causal diffusions for single-cell perturbation modeling (opens in a new tab)
- Nature Methods (T1)Prediction of protein subcellular localization in single cells (opens in a new tab)
- Proceedings of Machine Learning ResearchProbabilistic factorial experimental design for combinatorial interventions (opens in a new tab)
- Molecular Biology of the CellRemodeling of cytoskeleton, chromatin, and gene expression during mechanical rejuvenation of aged human dermal fibroblasts (opens in a new tab)
- Proceedings of Machine Learning ResearchSynthetic potential outcomes and causal mixture identifiability (opens in a new tab)
202411 papers
- Nature Cell BiologyBuilding a two-way street between cell biology and machine learning (opens in a new tab)
- AISTATS (T1)Causal discovery under off-target interventions (opens in a new tab)
- ICML (T1)Causal discovery with fewer independence tests (opens in a new tab)
- CLeaRCausal imputation for counterfactual SCMs: Bridging graphs and latent factor models (opens in a new tab)
- Advances of Neural Information Processing SystemsIdentifiability guarantees for causal disentanglement from purely observational data (opens in a new tab)
- Advances of Neural Information Processing SystemsLearning mixtures of unknown causal interventions (opens in a new tab)
- AISTATS (T1)Membership testing in Markov equivalence classes via independence queries (opens in a new tab)
- ICLR (T1)Removing biases from molecular representations via information maximization (opens in a new tab)
- ICLR WorkshopSeason combinatorial intervention predictions with Salt & Peper (opens in a new tab)
- Nature MicrobiologySingle-cell image-based genetic screens systematically identify regulators of Ebola virus subcellular infection dynamics (opens in a new tab)
- Nature Communications (T1)Unsupervised representation learning of chromatin images identifies changes in cell state and tissue organization during DCIS progression (opens in a new tab)
202312 papers
- Nature Machine Intelligence (T1)Active learning for optimal intervention design in causal models (opens in a new tab)
- Nature Communications (T1)Cross-modal autoencoder framework learns holistic representations of cardiovascular state (opens in a new tab)
- BloodGenome-scale high resolution spatial mapping of the pro-tumorigenic cellular niche in classic Hodgkins lymphoma (opens in a new tab)
- NeurIPS (T1)Identifiability guarantees for causal disentanglement from soft interventions (opens in a new tab)
- ICML (T1)Linear causal disentanglement via interventions (opens in a new tab)
- NeurIPS (T1)Meek separators and their applications in targeted causal discovery (opens in a new tab)
- Journal of the Royal Statistical Society Series BThe DeCAMFounder: Non-linear causal discovery in the presence of hidden variables (opens in a new tab)
- Aging CellTranscriptional changes are tightly coupled to chromatin reorganization during cellular aging (opens in a new tab)
- Nature Communications (T1)Transfer Learning with Kernel Methods (opens in a new tab)
- NeurIPS (T1)Unpaired multi-domain causal representation learning (opens in a new tab)
- NeurIPS (T1)Unsupervised protein-ligand binding energy prediction via neural Euler’s rotation equation (opens in a new tab)
- PNAS (T1)Wide and deep neural networks achieve optimality for classification (opens in a new tab)
Where alumni went65 listed
Apply to MITFall 2027
MIT EECS Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- See program page
- English
- TOEFL/IELTS
- Fee
- USD 90
Confirmed on the program page.
Similar labsnearest by research summary
- DETUM
Felix Dietrich
- Probabilistic & causal inference
- CNSJTU
Xiao Zhou
- Medical imaging & healthcare AI
- GBUCL
Gary Zhang
- Medical imaging & healthcare AI
- CNPeking
Yao Sui
- Medical imaging & healthcare AI
- USUCLA
Aydogan Ozcan
- Medical imaging & healthcare AI
- USNYU
Daniel Sodickson
- Medical imaging & healthcare AI
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
MITcloses