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Ph.D. Candidate

Bioinformatics & Systems Biology

I’m a Ph.D. candidate in the Bioinformatics and Systems Biology (BISB) program at the University of California, San Diego.

My research focuses on understanding how gene expression is regulated (see below for my specific research aims).

🎓 Education

B.S. in Bioengineering: Bioinformatics, 2017

Research Aims

The completion of the Human Genome Project in 2003 provided a nearly complete map of human DNA. This milestone enabled scientists to explore differences in our DNA sequence (genetic variation) by comparing genomes from many different individuals.

Today, we have genome sequences for over half a million people, identifying hundreds of thousands of genetic variants linked to common diseases like heart disease, Alzheimer’s, and diabetes. Yet, understanding the biological mechanisms behind these links remains a challenge. A significant portion of these genetic variants occur in the regions of the genome that do not produce proteins (called the non-coding genome) but instead play a pivotal role in regulating protein production in cells. Despite its importance, the non-coding genome is far less understood than the regions that code for proteins.

In my thesis, I use machine learning to analyze large-scale genomics datasets, aiming to predict how genetic variation in the non-coding genome impacts biological function.

Building a software ecosystem for machine learning in genomics

When I started my PhD in 2019, machine learning (ML) was already making a substantial impact in genomics. However, successfully applying ML was and still is consistently more challenging than is reported in the latest papers. Why?

Steep learning curves are everywhere. Genomics involves specialized assays, unique file formats, and buggy software. Rapid exploration of data and ideas are essential for effective analysis, but even simple tasks can take several steps (and tools) to complete.
Pitfalls are prevalent [1]. ML pitfalls, though becoming increasingly well-documented, can be difficult to detect. If not recognized, they lead to datasets that get undervalued or overestimated modeling capability.
Model interpretation is difficult. In many domains, we can get away with a model that just makes good predictions. This is not enough in biomedical research. We need to understand how a model is making its predictions. There are now several popular methods for interpreting ML models [2], but their complexity often keep models opaque.
Reproducing results is challenging. Reproducibility is fundamental to scientific research, but can be difficult in genomics. Difficult to understand method’s sections, mountains of custom code, and poor data sharing practices often make replication of results impossible.
Best practices are evolving. Keeping up with the best practices in ML for genomics can feel like trying to jump on a moving train. It is crucial to consolidate, reconcile, and document existing methodologies for broader adoption and discourse.
No unified framework currently exists. ML frameworks like scikit-learn, PyTorch, and Keras are wonderful and widely used, but they are too general for many tasks in ML for genomics. Genomics-focused tools are plentiful, but can be difficult to use and extend, either on their own or in concert with each other.

Tools

Docs | Publication | Preprint | GitHub

SeqData

Docs | GitHub

Talks

https://www.youtube.com/watch?v=47wbTR9yUpg

More to come soon!

My journey

“The road goes ever on and on…”

Curriculum Vitae

current_cv_updated.pdf