Understanding and predicting evolution can allow us to design genetic circuits, pharmaceutical interventions, and even public policies that are more effective and robust. Achieving this requires new experimental, mathematical, and computational tools.

To this end, MsEE Lab develops computational simulations and experimental synthetic biology to quantify likelihood in evolution, and applies these methods on different areas of real-world impact.

Computational models of evolutionary likelihood

Genomic sequencing generates an abundance of data about evolution. To fully capitalize on this resource, we also need new tools to quantitatively establish causality in evolution and explore alternative hypotheses, particularly in complex, multi-scale systems such as pathogens spreading within in a population. MsEE lab builds these kinds of tools, combining bottom-up, mechanistic simulations informed with top-down statistical inference and machine learning.

An active project in this vein focuses on Opqua, a computational platform for simulating pathogen genetics and evolution in epidemiological models. It has been applied to understand fundamental knowledge in genomic epidemiology using small simulations of infections [article]. We are working to expand this project by computationally predicting and experimentally validating intervention strategies that limit both the spread and the evolution of diseases.

Designing evolution through synthetic biology

In order to measure the effects of engineering interventions on evolution, we need tightly-controlled experimental systems with which to dissect these effects. Synthetic biology allows us to repurpose systems with unrelated biological functions into tools for manipulating evolution in a controlled environment.

Applications of evolutionary engineering

We collaborate with different area experts to apply these tools and design principles for evolutionarily-robust bioengineering on problems ranging across antimalarial discovery, influenza immunity, and therapeutic delivery for cancer and beyond. We look forward to seeing this list grow!

Research output

We maintain a complete list of MsEE Lab published work (research papers and beyond) with public-access links to all published material. The lab currently has no independently published work, though you may check out Pablo's work here.

An integral part of scientific practice is making sure it is carried on by others, enabling the growth of science as an institution. As MsEE Lab aims to develop a field of evolutionary engineering, its most important task must be the training and development of scientists who will carry out the work of the field. We take teaching and mentorship very seriously, and consider it an integral (and the primary) part of our work.

To give a real sense of our commitment to these endeavors, Pablo has developed workshops, programming, and written resources to support research project mentorship skills as a Teaching Development Fellow at MIT's Teaching and Learning Lab. He also completed courses on Subject Design, Lesson Planning, Microteaching, and teaching methods that provide space for all kinds of students on the way to obtaining a MIT TLL Teaching Certificate. He served for five years as a confidential peer counsellor and conflict coach through the MIT BE REFS.

More of Pablo's thoughts about these (and other) topics can be read at his old labSquared project.

The lab opens in the summer of 2026. Help us fill this space up!

Pablo Cárdenas R. leads MsEE Lab. A native of Colombia, Pablo spent his undergrad at Universidad de los Andes dipping toes in computational, experimental, and field work in biology. He left Bogotá for research stints in France and the US, where he learned that experiments fail in labs on every continent and of every budget. Undaunted, he got a PhD from MIT engineering malaria parasites, with side quests in epidemiology and evolution. He currently researches influenza immunity and evolution as a postdoc at the Ragon Institute and is an incoming assistant professor of Chemical and Biomolecular Engineering at Cornell. Pablo enjoys everything to do with music, games with balls or cardboard, and the great experiment that is human history.

Former members

The lab currently has no alumni.

Prospective graduate students: We will be taking graduate students starting in 2026 through multiple graduate fields (graduate programs in Cornellian), including—but not limited to—Chemical Engineering. Reach out about your interests.

Prospective undergraduate researchers: Check out Cornell's existing undergraduate research programs for both Cornell and non-Cornell students, such as these ones or these ones. Reach out about your interests.

Prospective technicians: Reach out about your interests and career plans.

Prospective postdocs: Reach out about your interests and career plans. Consider postdoc fellowships including the Schmidt AI in Science Postdoctoral Fellowship, Cornell internal postdoc fellowships, and external postdoc fellowships (h/t to Shuwen Yue for the links).

Prospective collaborators: we love collaborating on projects or just talking science and mentorship! By lab policy, we preprint all our research and openly publish all our code and methods, preferrably open source. We prefer to publish in not-for-profit journals whenever possible. Please reach out!

Science communicators: your job is crucial. Please tell us how we can help! Reach out.

Confused and eager students at any stage: We've all been there! Pablo loves dishing out career and science philosophy advice as freely as his schedule allows. Reach out about what's keeping you up at night, for better or for worse.

Anyone else: Reach out anyway and tell us why!

Reach out to Pablo at pablocarderam@gmail.com if you're interested in our work at all.

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