WID Postdocs expand the institute’s research portfolio and expertise, bringing new perspectives to bear on problems.
Continuous optimization for machine learning, reinforcement learning and other data science problems
Claire's research aims to explain how children learn to communicate and to use communicative structure to learn about words and concepts.
Design, synthesize, and evaluate nanoparticles to deliver drugs specifically to targeted organs
Developing optimization algorithms that are both computationally and statistically efficient
Injection molding, Polymer processing, Process monitoring and optimization
CRISPR-Cas gene editing in the brain for the treatment of neurodegenerative disorders
The design of bioactive materials, such as exosomes, bacteria, and cells for disease treatment, especially microbiome-related disease and immunotherapy.
Interrogating the metabolism-epigenome axis within a stem cell model of human aging
Development and application of Machine Learning (ML) methods for the analysis of scRNA-Seq datasets
Designing efficient estimators which are robust to aggressive noise models
Engineering organotypic 2D CNS tissues from human pluripotent stem cells
Inferring phylogenetic networks and reticulate evolution from genomic data
Inference and decision-making with missing or unobserved information.
Designing practical, data-efficient algorithms with rigorous guarantees for statistical applications
Privacy-preserving machine learning and optimization
To understand the activation mechanism of p300/CBP protein acetyltransferase by acyl-CoA metabolism.
Effects of community interactions on microbial physiology
I work to determine antibiotic resistance in bacteria by analyzing images and genomes using AI.
Bioinformatics based on networks and large-scale quantitative genomics.