Lena Marsh
What Ohara thinks you know, and where it will point you next.
Summary
Written from your CV and links.
Fourth-year PhD candidate in chemical engineering who uses Bayesian optimisation and Gaussian-process surrogates to screen liquid electrolytes for lithium-metal batteries. Comfortable with probabilistic machine learning and high-throughput experiments; newer to multi-fidelity methods and calibrated uncertainty.
Expertise
Each level sets how advanced the items in your feed may be.
- Bayesian optimisationProficient
First-author paper on GP-guided electrolyte screening (2025).
- Battery electrolytesProficient
Three years of coin-cell cycling and conductivity measurements.
- Probabilistic machine learningWorking
Coursework in Gaussian processes; uses BoTorch in research code.
- Density functional theoryIntroductory
One collaboration using DFT-computed solvation energies.
Where to go next
The starting points your subject map grows from.
- Multi-fidelity Bayesian optimisation
Your screening uses one expensive experiment per candidate; cheaper simulations could steer it.
- Calibrated uncertainty for surrogates
Your GP surrogates drive decisions, so their error bars need to be trustworthy.
- Autonomous electrolyte labs
A natural next step from your high-throughput setup.
Methods and interests
Used to match items to how you work.
Gaussian processes · BoTorch · High-throughput experimentation · Electrochemical impedance spectroscopy · Python · Design of experiments
Interested in Self-driving laboratories, Multi-fidelity optimisation, Science communication.
Reading level
How dense an explanation you get in each domain.
- Machine learningWorking
- ElectrochemistryProficient
- StatisticsWorking
Education
From your CV.
- PhD (in progress), Chemical Engineering2022–2027
A US research university
- BS, Chemical Engineering2018–2022
A US state university
Plan
What this account can use.
Owner: everything unlocked. Billing is off on this server.
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