Big ideas on small organisms:
Using computing to address biological questions

Our Mission

The who, what, why, as if we actually know where we are heading

Who we are

We are an interdisciplinary lab of biologists, computer scientists and engineers that want to push the boundaries of knowledge.

What we do

We analyze data to understand complex systems and predict their behavior. We generate and test hypotheses, create algorithms, build software systems.

Where we are

We are part of the Computer Science department and the UC Davis Genome Center. Our lab and offices are on the 5th floor of Genome and Biomedical Science Facility (GBSF).

How we do it

We develop and apply machine learning, optimization and other computational methods with HPC support on data generated in our lab and elsewhere.

Why we do it

Long hours, low pay, constant pressure over results, publications and funding, what is there not to like? The excitement to work on discovering something novel, useful, potentially ground-breaking is difficult to match.

Our focus

We serve multiple disciplines but we are better at biological, medical and agricultural systems and models. Experimentally, we focus mostly on microbes. Often tagged as ML/AI geeks, HPC pests, Systems & Synthetic Biology aficionados.

Our Scientific process

How we engage projects and collaborations


This always starts from the science or business question that we want to answer. We identify the challenge, the opportunity and the resources we need to succeed.
We go to whatever lengths necessary to engage the right people, design the most informative experiments and remove any intrinsic bias to ensure that success if within grasp.


Once the scope and success criteria have been defined, we approach R&D through an engineering lense.
Usually interdisciplinary teams of 2-5 students, postdoctoral associates and other trainees meet weekly, divide tasks and exchange ideas.


R&D is not a monolithic feed-forward process, it involves constant feedback, evaluation and refinement, where early failures become the guideposts for future success. Once the scientific aims of a project are completed, the final step includes peer-review of our methods, publication of our findings and making available our products to our collaborators and the public.

Our Expertise

A bit of background on our skills and expertise

Machine Learning
High Performance Computing
Multi-Scale Modelling
Synthetic Biology
Artificial Intelligence
Multi Omics
Systems Biology