IE

Ignacia Echeverria

Molecular Biophysics; Computational Structural Biology

University of California San Francisco

Flexible CommitmentsRespects PrivacyTravel OftenClear Vision
4.0/ 5.0
7 student reviews
👍4
👎0
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About Ignacia Echeverria at University of California San Francisco

Ignacia Echeverria is an Assistant Professor at the University of California San Francisco working in molecular biophysics and computational structural biology. Her research focuses on developing integrative, computational, and theoretical models to describe the structure, dynamics, and function of proteins, nucleic acids, and carbohydrates, with the goal of understanding their biological roles and evolutionary principles. Her work emphasizes integrative structure determination approaches that combine diverse experimental data with computational modeling to study large protein assemblies and complex molecular systems. She applies methods from molecular biophysics, statistical mechanics, stochastic modeling, molecular modeling, and scientific computing to investigate how molecular interactions give rise to biological function. She received her Ph.D. in Molecular Biophysics from Johns Hopkins University and holds a bachelor’s degree in Physics from Pontificia Universidad Católica de Chile. Prior to joining UCSF as faculty, she conducted postdoctoral research at UCSF in the Andrej Šali laboratory and at the University of Maryland in the Garyk Papoian laboratory.

Research Areas

molecular biophysicscomputational structural biologyprotein structureprotein dynamicsintegrative modelingstatistical mechanicsmolecular modeling

Rating Breakdown

Supervision Style4.3
Responsiveness3.3
Workload4.0
Funding Support3.7
Communication4.3

Reviews (3)

A
Anonymous2/12/2026
4.0

Quick to respond to urgent matters, more leisurely with routine emails. Has clear expectations about what constitutes 'urgent' which actually helps you prioritize.

A
Anonymous12/19/2025
4.0

I worked with the group for about a year on integrative modeling projects. Interactions were primarily through weekly lab meetings and code reviews. Guidance emphasized reproducible computational workflows and clear documentation; feedback on modeling approaches was constructive and technically specific. I did not have much visibility into grant administration or lab-level funding. Suitable for students who prefer a computational/theoretical focus and value rigorous code and model validation practices.

👍

A student recommended this supervisor and marked them as Flexible Commitments

Anonymous quick feedback

3 months ago

A
Anonymous6/20/2025
4.0

Creates space for innovation while maintaining rigor. Lab meetings are intellectually stimulating without being intimidating. Fair and transparent about expectations from the start.

👍

A student recommended this supervisor and marked them as Travel Often

Anonymous quick feedback

1 months ago

👍

A student recommended this supervisor and marked them as Clear Vision

Anonymous quick feedback

6 months ago

👍

A student recommended this supervisor and marked them as Respects Privacy

Anonymous quick feedback

6 months ago

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