Caroline Uhler
#34,259
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Swiss statistician
Caroline Uhler's AcademicInfluence.com Rankings
Caroline Uhlermathematics Degrees
Mathematics
#3284
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#4880
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Statistics
#680
World Rank
#765
Historical Rank
Measure Theory
#3698
World Rank
#4362
Historical Rank
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Mathematics
Caroline Uhler's Degrees
- PhD Statistics ETH Zurich
- Masters Mathematics ETH Zurich
Why Is Caroline Uhler Influential?
(Suggest an Edit or Addition)According to Wikipedia, Caroline Uhler is a Swiss statistician working in the field of machine learning and applications in genomics. Her research focuses on developing methods for causal inference to infer regulatory relationships from different data modalities . She is a Full Professor in the Department of Electrical Engineering and Computer Science and the Institute for Data, Systems and Society at the Massachusetts Institute of Technology. In addition, she is a Core Institute Member at the Broad Institute, where she directs the Eric and Wendy Schmidt Center.
Caroline Uhler's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- A Complete Neandertal Mitochondrial Genome Sequence Determined by High-Throughput Sequencing (2008) (539)
- Regulation of genome organization and gene expression by nuclear mechanotransduction (2017) (247)
- Geometry of the faithfulness assumption in causal inference (2012) (171)
- Privacy-Preserving Data Sharing for Genome-Wide Association Studies (2012) (134)
- Scalable privacy-preserving data sharing methodology for genome-wide association studies (2014) (131)
- Privacy Preserving GWAS Data Sharing (2011) (90)
- Orientation and repositioning of chromosomes correlate with cell geometry–dependent gene expression (2017) (86)
- Nuclear Mechanopathology and Cancer Diagnosis. (2018) (80)
- Permutation-based Causal Inference Algorithms with Interventions (2017) (78)
- Geometry of maximum likelihood estimation in Gaussian graphical models (2010) (78)
- Multivariate Gaussians, semidefinite matrix completion, and convex algebraic geometry (2009) (70)
- Total positivity in Markov structures (2015) (65)
- Multi-domain translation between single-cell imaging and sequencing data using autoencoders (2019) (60)
- Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions (2018) (57)
- Maximum likelihood estimation in Gaussian models under total positivity (2017) (56)
- Maximum likelihood estimation for linear Gaussian covariance models (2014) (55)
- Differentially-Private Logistic Regression for Detecting Multiple-SNP Association in GWAS Databases (2014) (45)
- Learning directed acyclic graph models based on sparsest permutations (2018) (44)
- Scalable Unbalanced Optimal Transport using Generative Adversarial Networks (2018) (43)
- ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure Discovery (2019) (42)
- Predicting cell lineages using autoencoders and optimal transport (2020) (41)
- Multiscale simulations of complex systems by learning their effective dynamics (2020) (41)
- Network analysis identifies chromosome intermingling regions as regulatory hotspots for transcription (2017) (41)
- Packing Ellipsoids with Overlap (2012) (40)
- Exact formulas for the normalizing constants of Wishart distributions for graphical models (2014) (40)
- Causal network models of SARS-CoV-2 expression and aging to identify candidates for drug repurposing (2020) (39)
- Exponential varieties (2014) (38)
- Permutation-Based Causal Structure Learning with Unknown Intervention Targets (2019) (36)
- Consistency Guarantees for Greedy Permutation-Based Causal Inference Algorithms (2017) (35)
- Consistency Guarantees for Permutation-Based Causal Inference Algorithms (2017) (33)
- Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis (2017) (32)
- Overparameterized neural networks implement associative memory (2019) (32)
- Learning directed acyclic graphs based on sparsest permutations (2013) (31)
- Memorization in Overparameterized Autoencoders (2018) (31)
- Gaussian Graphical Models: An Algebraic and Geometric Perspective (2017) (30)
- Chromosome Intermingling: Mechanical Hotspots for Genome Regulation. (2017) (29)
- Ordering-Based Causal Structure Learning in the Presence of Latent Variables (2019) (25)
- Covariance Matrix Estimation under Total Positivity for Portfolio Selection* (2019) (24)
- Direct Estimation of Differences in Causal Graphs (2018) (20)
- High-dimensional joint estimation of multiple directed Gaussian graphical models (2018) (20)
- Mechano-genomic regulation of coronaviruses and its interplay with ageing (2020) (20)
- Joint inference of networks from stationary graph signals (2017) (19)
- Maximum likelihood estimation for totally positive log‐concave densities (2018) (18)
- Generalized Permutohedra from Probabilistic Graphical Models (2016) (18)
- Joint Inference of Multiple Graphs from Matrix Polynomials (2020) (18)
- Learning High-dimensional Gaussian Graphical Models under Total Positivity without Adjustment of Tuning Parameters (2019) (17)
- Hypersurfaces and Their Singularities in Partial Correlation Testing (2012) (17)
- Geometric control and modeling of genome reprogramming (2016) (15)
- Counting Markov Equivalence Classes by Number of Immoralities (2016) (14)
- Multi-Domain Translation by Learning Uncoupled Autoencoders (2019) (14)
- Commuting birth-and-death processes. (2008) (13)
- Exact Goodness‐of‐Fit Testing for the Ising Model (2014) (13)
- Counting Markov equivalence classes for DAG models on trees (2017) (13)
- Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models (2018) (12)
- Causal Imputation via Synthetic Interventions (2020) (11)
- Geometry of Log-Concave Density Estimation (2017) (11)
- Simple, fast, and flexible framework for matrix completion with infinite width neural networks (2021) (11)
- Patchnet: Interpretable Neural Networks for Image Classification (2017) (11)
- Total positivity in exponential families with application to binary variables (2019) (10)
- DCI: Learning Causal Differences between Gene Regulatory Networks (2020) (10)
- Autoencoder and Optimal Transport to Infer Single-Cell Trajectories of Biological Processes (2018) (9)
- Causal Structure Discovery from Distributions Arising from Mixtures of DAGs (2020) (9)
- Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning (2021) (9)
- The DeCAMFounder: Non-Linear Causal Discovery in the Presence of Hidden Variables (2021) (8)
- Learning the Effective Dynamics of Complex Multiscale Systems (2020) (8)
- Optimal Transport using GANs for Lineage Tracing (2020) (7)
- Sphere Packing with Limited Overlap (2014) (7)
- Detecting epistasis via Markov bases (2010) (7)
- Geometry of discrete copulas (2018) (7)
- Identifying 3D Genome Organization in Diploid Organisms via Euclidean Distance Geometry (2021) (7)
- Brownian motion tree models are toric (2019) (6)
- Matching a Desired Causal State via Shift Interventions (2021) (6)
- Do Deeper Convolutional Networks Perform Better? (2020) (6)
- Anchored Causal Inference in the Presence of Measurement Error (2019) (6)
- Downsampling leads to Image Memorization in Convolutional Autoencoders (2018) (6)
- Causal Structure Discovery between Clusters of Nodes Induced by Latent Factors (2022) (5)
- On Alignment in Deep Linear Neural Networks (2020) (5)
- Size of Interventional Markov Equivalence Classes in Random DAG Models (2019) (5)
- Mol2Image: Improved Conditional Flow Models for Molecule to Image Synthesis (2020) (5)
- Overparameterized Neural Networks Can Implement Associative Memory (2019) (4)
- Cross-modal autoencoder framework learns holistic representations of cardiovascular state (2022) (4)
- Total positivity in structured binary distributions (2019) (4)
- Linear Convergence and Implicit Regularization of Generalized Mirror Descent with Time-Dependent Mirrors (2020) (4)
- Causal Structure Learning: A Combinatorial Perspective (2022) (4)
- Increasing Depth Leads to U-Shaped Test Risk in Over-parameterized Convolutional Networks (2020) (4)
- A Mechanism for Producing Aligned Latent Spaces with Autoencoders (2021) (4)
- Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample (2021) (3)
- Graph-based autoencoder integrates spatial transcriptomics with chromatin images and identifies joint biomarkers for Alzheimer’s disease (2022) (3)
- Linear Causal Disentanglement via Interventions (2022) (3)
- Balancedness and Alignment are Unlikely in Linear Neural Networks (2020) (2)
- Linear Convergence of Generalized Mirror Descent with Time-Dependent Mirrors (2020) (2)
- Faithfulness and learning hypergraphs from discrete distributions (2014) (2)
- Machine Learning for Nuclear Mechano-Morphometric Biomarkers in Cancer Diagnosis (2017) (1)
- Learning High-Dimensional Gaussian Graphical Models under Total Positivity without Tuning Parameters (2019) (1)
- Wide and Deep Neural Networks Achieve Optimality for Classification (2022) (1)
- Efficient Permutation Discovery in Causal DAGs (2020) (1)
- Loading monotonicity of weighted premiums, and total positivity properties of weight functions (2018) (1)
- Algebraic Statistics in Practice: Applications to Networks (2019) (1)
- Extremal Positive Semidefinite Matrices for Graphs without $K_5$ Minors (2015) (1)
- Geometry of Log-Concave Density Estimation (2018) (1)
- Extremal positive semidefinite matrices whose sparsity pattern is given by graphs without K5 minors (2016) (1)
- Multi-domain translation between single-cell imaging and sequencing data using autoencoders (2021) (1)
- Lateral confined growth of cells activates Lef1 dependent pathways to regulate cell-state transitions (2022) (1)
- A Strategy for Detecting Multiple Trait Loci in Disease Association Studies (2008) (0)
- Generalized Fréchet Bounds for Cell Entries in Multidimensional Contingency Tables (2017) (0)
- Extremal Positive Semidefinite Matrices for Weakly Bipartite Graphs (2015) (0)
- Systematically characterizing the roles of E3-ligase family members in inflammatory responses with massively parallel Perturb-seq (2023) (0)
- Mechanogenomic coupling of lung tissue stiffness, EMT and coronavirus pathogenicity (2020) (0)
- Local Quadratic Convergence of Stochastic Gradient Descent with Adaptive Step Size (2021) (0)
- Unsupervised Protein-Ligand Binding Energy Prediction via Neural Euler's Rotation Equation (2023) (0)
- Publisher Correction: Geometry of Log-Concave Density Estimation (2022) (0)
- A machine learning approach to evaluating renewable energy technology: An alternative LACE study on Solar Photo-Voltaic (PV) (2020) (0)
- Personalized Health 2020 29 and 30 June 2020 Virtual Only Personalized (2020) (0)
- Unpaired Multi-Domain Causal Representation Learning (2023) (0)
- ST ] 2 M ay 2 01 6 TOTAL POSITIVITY IN MARKOV STRUCTURES (2018) (0)
- Sequencing and analysis of a complete Neandertal mitochondrial genome. (2008) (0)
- A Combinatorial Perspective of Markov Equivalence Classes for DAG Models (2016) (0)
- Active Learning for Optimal Intervention Design in Causal Models (2022) (0)
- Publisher Correction: Geometry of Log-Concave Density Estimation (2022) (0)
- Hypersurfaces and Their Singularities in Partial Correlation Testing (2014) (0)
- Transfer Learning with Kernel Methods (2022) (0)
- Lateral confined growth of cells activates Lef1 dependent pathways to regulate cell-state transitions. (2023) (0)
- Exact Goodness-ofFit Testing for the Ising Model Citation (2016) (0)
- Network Analysis Identifies Regulatory Hotspots in Regions of Chromosome Interactions (2017) (0)
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What Schools Are Affiliated With Caroline Uhler?
Caroline Uhler is affiliated with the following schools:
