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Institute of Physics
Data Scientist
June 19, 2026 – Present
VASPilot
July 26, 2025 – October 18, 2025
An automatic tool utilizing multi agent framework CrewAI and MCP for VASP calculation.
View ProjectPODGen
March 18, 2025 – Present
PODGen is an open-source machine learning framework for the conditional generation of crystalline materials with targeted properties.
View Projectcontinuum-model-twist-tmds
October 28, 2024 – May 7, 2025
continuum model for twist bilayer MoTe2 and twist bilayer MoTe2
View ProjectCon-CDVAE
January 25, 2024 – Present
This is a conditionally generative model for crystal structures based on a modified version of CDVAE.
View ProjectDPmoire
April 23, 2023 – Present
A tool for constructing accurate machine learning force fields in moir\'e systems
View Projectwannier-tutorials
February 12, 2020 – Present
A repository hosting materials used during Wannier-related tutorials and schools
View ProjectACANN
July 23, 2018 – July 29, 2019
Solving an Analytic Continuation problem with a deep Artificial Neural Network (ANN)
View ProjectChernNumber
February 8, 2015 – August 27, 2018
Using different methods to calculate Chern number for Haldane model with disorder
View Projectwannier_tools
January 29, 2015 – June 9, 2025
WannierTools: An open-source software package for novel topological materials. Full documentation:
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on computational physics and materials science, indicating a strong academic or research-oriented background. While these projects demonstrate significant technical depth, their direct applicability to a typical industry 'Data Scientist' role might require a transition. The candidate's current role as 'Data Scientist' at the Institute of Physics aligns with their project focus, suggesting a fit within a research-intensive data science environment. However, the lack of diverse industry-standard data science projects (e.g., business analytics, A/B testing, customer segmentation, large-scale data engineering) suggests a potential gap in broader cultural fit for a general industry data science role.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions are technical but do not provide insight into collaboration, problem-solving approaches, or communication style in a team setting.