PhD in Materials Science
Scientific Engineering Associate · Lawrence Berkeley National Laboratory
Computational Methods for Electrified Interfaces | Machine Learning for Physics
Data Science & Scientific Software Development
🐍 Python is my rock, and I am ready to roll! 🪨
I am a Scientific Engineering Associate at Lawrence Berkeley National Laboratory's Molecular Foundry, where I apply computational methods to understand the atomistic nature of electrified interfaces, support incoming visiting researchers, and develop the Crucible Data Platform. I completed my PhD in Materials Science through a joint program between ETH Zürich and Berkeley Lab in September 2025.
My work combines expertise in molecular dynamics simulations, ab-initio methods, machine learning, and scientific software development to tackle challenging problems in materials science. I am passionate about creating open-source tools that enable reproducible research and accelerate scientific discovery, and I actively contribute to data science infrastructure for large-scale materials characterization projects.
Python, Unix Shell, Fortran, LaTeX, Git, MatLab, HTML/CSS, Kotlin
(GPU4)PySCF, GPAW, VASP, LAMMPS, GROMACS, CP2K, Plumed, MBXAS, Quantum Espresso, Wannier90, UppASD, LigParGen
NumPy, SciPy, Matplotlib, ASE, MDAnalysis, UMAP, HDBSCAN, scikit-learn, TensorFlow, GPflow, PyTorch, DeepMD-kit
Developing computational methods to model charge-transfer at electrode-electrolyte interfaces for energy storage applications.
Applying unsupervised learning to molecular dynamics data and electronic structure to discover hidden patterns.
Bridging length scales from atoms to continuum for comprehensive understanding of electrochemical systems.
Computational predictions of X-ray absorption spectra to guide experimental characterization of materials.
Building open-source Python tools for large-scale materials characterization data management, enabling dataset creation, sample provenance tracking, and metadata capture across experimental workflows.
ETH Zürich & Lawrence Berkeley National Laboratory | Jan 2021 - Sep 2025
Thesis: "Comprehensive Modeling of the Electrochemical Interface: Integrating Ab-Initio Methods, Molecular Dynamics, and Machine Learning"
Advisors: Prof. Dr. Nicola Spaldin & Dr. David Prendergast
ETH Zürich | Jan 2017 - Nov 2019 | Grade average: 5.6/6
Thesis: "Multiscale modeling of electrified interfaces -- from atoms to the continuum -- for energy applications"
ETH Zürich | Sep 2013 - Aug 2016 | Grade average: 5.2/6
Lawrence Berkeley National Laboratory · Molecular Foundry | Jan 2024 - Present
Lawrence Berkeley National Laboratory · Molecular Foundry | Jan 2021 - Jan 2024
ETH Zürich · Materials Theory | Sep 2019 - Jan 2021
Lawrence Berkeley National Laboratory | 2023 - Present
ETH Zürich · Materials Theory | 2019 - 2020
MD System Builder
Automates the construction of complex systems for Molecular Dynamics simulations, including solid-solid interfaces, solid-liquid interfaces, inhomogeneous electrolyte systems, and polymer membranes.
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Many-Body X-ray Absorption Spectroscopy
Python package for setting up, manipulating, running, and visualizing X-ray Absorption Spectroscopy (XAS) calculations. Integrates Gaussian process regression for autonomous learning of XAS spectra.
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Trajectory Post-Processing & Data Mining
Python package for the post-processing and data mining of molecular dynamics trajectories, combining unsupervised learning and hierarchical clustering for quantitative mapping of local atomic structures.
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Python Client for Crucible
Python library and CLI tool for interacting with Crucible, the Molecular Foundry's centralized data management system. Enables programmatic management of experimental datasets, sample tracking with hierarchical relationships, and comprehensive metadata storage.
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Mobile Android App for Crucible
Android application providing mobile access to the Molecular Foundry's Crucible data management system. Features QR code scanning for quick resource lookup, full-text search, project organization, dataset browsing, and relationship navigation.
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High-Throughput Thin Film Analysis
Primary Python interface for analyzing a dataset of 10,000 thin film samples, integrating the Crucible API for automated sample characterization workflows.
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Let's discuss research collaborations, open-source projects, or opportunities in computational materials science!
