Faculty Spotlight: Stephanie Hicks, PhD

Stephanie Hicks, PhD, is an Associate Professor in the Department of Biostatistics, at the Bloomberg School of Public Health, and in the Department of Biomedical Engineering, at the Whiting School of Engineering. Stephanie joined Johns Hopkins in 2018 after completing her postdoc fellowship at the Dana-Farber Cancer Institute. She is an applied statistician working at the intersection of genomics and biomedical data science whose research addresses computational challenges in single-cell genomics, epigenomics, and spatial transcriptomics leading to an improved understanding of human health and disease.

Stephanie is a co-member of the Editorial Advisory Board for Genome Biology, a co-leader of the Genomic Data Science Working Group, a member of the Technical Advisory Board for Bioconductor, an Associate Editor for Reproducibility at the Journal of the American Statistical Association, and co-founder of R-Ladies Baltimore.

What drew you to biostatistics and public health? 

So many things. I think if you're quantitatively oriented, it's one of the best places to be able to apply your quantitative skills. If you are math, computer science, or engineering oriented, biostatistics in particular is a great space to apply your quantitative skills in a public health setting. And for me, that's working on open statistical and data science problems around human health and human disease. 

Describe your work in one sentence.

I work on developing computational methods to help people understand very messy, noisy, biological data at a molecular level in individual cells. 

What are your research interests?

My research interests are to develop computational tools for genomics data, and I do that at a single-cell resolution. 

The idea is if we have two groups of samples (let’s say for example a set of neurotypical samples and the set of samples who have Alzheimer's Disease), my research is to try and understand what the differences are by developing computational tools to help us figure out what's noise and what's biological signal, what's real and what's not real. If we can develop computational tools that help us remove the noise, then we can see the signal more clearly and we can understand what the differences are and develop drug targets.

Where do you see your area of study going in the next five years? 

Growing very fast. The reason is because the world around us is becoming more data-driven and data-oriented, and it's exactly the same for genomics. We are generating very large data sets across millions of individuals and we're at a point where these computational tools are essential to be able to figure out what’s noise versus and what's real biology that's driving different human diseases. 

The field is only growing bigger, mostly because of the opportunities for data availability that we didn't have five years ago. It's a very exciting field to be in because even very simple statistical ideas can have a huge impact in a field that's brand new.

What impact do you hope your research will have? 

A lot of people ask me “what's going to happen with AI in 20 years?” I don't know. But I do know that we are going to solve a lot of diseases in the next 20 years. The reason I know that is because the experimental technologies are changing at such a rapid pace, and the datasets are becoming large enough that we have a meaningful chance at identifying what are the molecular differences between health and disease states. In particular, for Mendelian disorders, where these are genetic disorders caused by mutations in a single gene. Not only do we can identify what the gene is, but we also now have gene-editing technologies, such as CRISPR, which can be used to treat these disorders. 

I believe we are going to solve a lot of disorders in the next 20 years. My hope is that computational tools will play a small part in that larger picture.

What do you like best about being at Hopkins? 

One of the things that I love most about Hopkins is that it's incredibly collaborative. I don't have to cross institutes; it's all one big university. I just email someone in another department, and ask if we can work together, and almost always they say yes.

What class(es) do you teach? 

I teach Statistical Programing Paradigms and Workflows. It's a new course that we just developed two years ago, and the idea is to give students who do not have a computational background a best practices overview of the really essential computational tools. 

For example, we teach introduction to command line with Unix, introduction to version control with GitHub, R package development, learning how to extract data from APIs or HTMLs, and using AI-paired programming with large language models (LLMs) to write code faster and more efficiently. There are many cool and cutting edge techniques that hopefully will serve the students well when they do their research. 

What would you say to a person considering a career in biostatistics?

If you are quantitatively-oriented, biostatistics is an amazing space to be, particularly if you have any interest at all at having a huge impact on human health and human disease. 

It's one of the most amazing spaces to be in, because if you have a quantitative background, if you're math-oriented, if you're computer science-oriented, one of the things that you might find challenging is how you can have an impact in the real world, and the intersection of biostatistics and public health is a great space to be able to have that impact.

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