People
A computational lab building the methods to read cell identity, and ultimately to rewrite it. Most of what we do is done jointly with experimental groups.
Principal investigator
Assistant Professor of Computational Biology.
dar2042@med.cornell.edu
Google Scholar ·
ORCID
Current members
Computational Engineer
Past members
Now Bioinformatician, Columbia University Irving Medical Center
Now Senior Bioinformatics Analyst, Englander Institute for Precision Medicine, Weill Cornell Medicine
Collaborators
These are the groups we work with most closely. Much of the work on this site is as much theirs as ours.
Weill Cornell Medicine
Our longest-running collaboration, on endothelial heterogeneity, angiocrine
signalling and the vascular niche. Joint work runs from the conversion of adult
endothelium into haematopoietic stem cells to adaptable endothelial cells for
organ regeneration and islet engraftment.
Weill Cornell Medicine
Rob's lab builds stem cell-derived and bioengineered human liver systems.
Joint work runs from the pluripotent stem cell organoid platforms used to
identify SARS-CoV-2 therapeutics in 2020 through to peribiliary mesenchymal
identity in liver homeostasis and injury.
Cincinnati Children's Hospital Medical Center
Liver development and biliary architecture. The Huppert lab works on how
hepatocyte plasticity can rebuild insufficient intrahepatic bile duct systems;
we work together on the single-cell landscape underlying that process.
Icahn School of Medicine at Mount Sinai
Host–microbial interactions and Crohn's disease. Joint work on the mucosal
immune and endothelial landscape of the inflamed intestine, and how vascular
signals shape the immune response there.
Join the lab
The lab is currently hiring postdoctoral researchers and research technicians. Both positions are based at Weill Cornell Medicine on the Upper East Side of Manhattan, alongside the experimental groups we work with day to day.
This is a computational lab embedded in a wet-lab environment. We do not take finished datasets and hand back a figure. Projects here usually start at the design stage, with an argument about what the experiment can and cannot show, and end with a prediction that somebody puts to the test at the bench. If you want to spend the next few years only writing code, or only pipetting, this is the wrong place. If you want to sit between the two and be taken seriously in both rooms, it is a good one.
Practically, that means you will have access to human and mouse single-cell, multiome and spatial data across several organs, to collaborators who can generate more of it, and to a compute environment that will not be the thing that slows you down. It also means you are expected to understand the biology you are modelling, to read the primary literature rather than the abstract, and to say when a result looks too good.
We work in the open where we can. Analysis code goes in version control, tools that are useful outside the lab get released, and data goes to public repositories when the paper does. See the Software page for what that has produced so far.
We are looking for people who want to work at the interface of computation and vascular or immune biology. Strong candidates might come from a computational background, in bioinformatics, machine learning, statistics or physics, and want to engage seriously with the biology. Or they might come from an experimental background and want to build real computational depth rather than a working familiarity with a few tools. Experience with single-cell or epigenomic data is welcome but not required if the quantitative foundation is there.
What we care about more than the CV is whether you can take a vague biological question, turn it into something a dataset can actually answer, and then argue convincingly about whether the answer is real. Evidence of that can look like a first-author paper, a well-built piece of software, a thesis chapter that changed your own mind, or a preprint that is still under review.
Projects available now sit within the themes on the Research page, and there is room to shape one around what you are good at. We will also expect you to spend part of your time building towards independence: writing fellowship applications, presenting the work, and taking ownership of a question that leaves with you.
We are looking for organised, careful people to support data generation and analysis workflows. The work covers sample and library preparation with our collaborating labs, running established processing pipelines, keeping data and metadata in order, and helping to maintain the analysis environment. A good technician here notices that a sample sheet does not match the sequencing run before anyone else does.
Comfort with the command line and some scripting, in R or Python, is useful. So is the willingness to learn it. Neither a bioinformatics degree nor previous single-cell experience is required. If you want to see what the analysis side looks like before applying, our video courses are free and start from no programming experience.
This is a good position for someone planning to apply to graduate or medical school who wants substantive research experience rather than two years of sample logging. Technicians here are named on papers they contributed to, are encouraged to take on an analysis of their own, and get help with applications when the time comes. Past members of the lab have gone on to bioinformatics positions at Columbia and at Weill Cornell.
Graduate students at Weill Cornell are welcome to rotate in the lab. Email before the rotation period opens so we can pick a project that is genuinely finishable in the time available, rather than a slice of something larger that you never get to see the end of. We also take a small number of undergraduate and masters students each year, where there is a defined project and someone with the time to supervise it properly.
Email dar2042@med.cornell.edu with the position in the subject line and include:
We read everything that arrives. If the fit looks plausible we will set up a call, then invite you to give a talk to the lab and meet the collaborating groups, since you will be working with them as much as with us. Replies are slower during grant seasons. If you have a deadline from somewhere else, say so in the first email and we will work to it.
If you do not fit either advertised position but think you should be here, write anyway and make the case. Unusual backgrounds are an argument for you, not against.