Scholar
Jan-Christian Hütter
Google Scholar ID: UE7lJeUAAAAJ
Genentech
Citations & Impact
Citations
1,531
H-index
13
i10-index
14
Publications
20
Co-authors
0
Publications
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
- NODAGS-Flow: Nonlinear Cyclic Causal Structure Learning (arXiv, 2023)
- Maximum Likelihood Estimation for Brownian Motion Tree Models Based on One Sample (arXiv, 2021)
- Stepwise-Edited, Human Melanoma Models Reveal Mutations’ Effect on Tumor and Microenvironment (Science, 2022)
- Cycling Cancer Persister Cells Arise from Lineages with Distinct Programs (Nature, 2021)
- Minimax Rates of Estimation for Smooth Optimal Transport Maps (The Annals of Statistics, 2021)
- Skin-Resident Innate Lymphoid Cells Converge on a Pathogenic Effector State (Nature, 2021)
- Optimal Rates for Estimation of Two-Dimensional Totally Positive Distributions (Electronic Journal of Statistics, 2020)
- Estimation of Monge Matrices (Bernoulli, 2020)
Research Experience
- Currently a Principal ML Scientist II in the Biology Research | AI Development (BRAID) department at Genentech.
Education
- PhD from MIT in 2019, supervised by Philippe Rigollet; Postdoctoral Researcher at the Broad Institute in the Regev group, co-advised by Caroline Uhler in 2021.
Background
- Develops methods for the analysis of omics data, particularly in the context of large-scale high-content perturbation screens. These screens enable mapping out functional properties of genes and gene-regulatory networks at unprecedented scale. To draw conclusions from the associated data, he harnesses mathematical, statistical, and machine learning methods such as statistical optimal transport and differentiable causal discovery.
Co-authors