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Stanford University
Data Scientist
June 29, 2026 – Present
machine-learning-for-design
February 24, 2026 – Present
A curated list of resources on machine learning for fluid flow, structures and design optimization.
View Projectairfoil_shape_opt_deep_rl
November 11, 2025 – December 1, 2025
Aerodynamic shape optimization using deep reinforcement learning.
View ProjectNIGnets
February 25, 2025 – December 1, 2025
Neural Injective Geometry networks (NIGnets) for non-self-intersecting geometry.
View Projectgeosimilarity
February 7, 2025 – February 26, 2025
Differentiable curve and surface similarity measures.
View ProjectAirfoil-Shape-Optimization-DL
May 27, 2024 – April 28, 2025
Airfoil Shape Optimization through Neural Network Input Optimization.
View Projectdeepfusion
April 29, 2024 – May 23, 2024
A highly modular and customizable Deep Learning framework.
View ProjectAirfoil-Shape-Optimization-RL
October 19, 2023 – December 8, 2023
Airfoil shape optimization using Reinforcement Learning
View ProjectEWS-MultipleWindow-Method
May 28, 2023 – August 19, 2023
Research on developing a new method for determining the warning time of Early Warning Signals. Also an attempt at removing window size uncertainty from EWS analysis
View ProjectModified-MannKendall-Test
May 23, 2023 – May 28, 2023
Modified-MannKendall-Test — GitHub repository
View ProjectMATLAB_Defaults
October 17, 2021 – December 15, 2022
A MATLAB repository for setting defaults especially for improving plots
View ProjectCultural Fit Analysis
The candidate's projects are heavily focused on research and personal exploration within specific domains (aerodynamics, geometry, signal processing). While this demonstrates initiative and deep technical interest, the diversity of projects outside these niche areas is limited. The target role is 'Data Scientist', and while the candidate's skills align with core data science methodologies (ML, DL, optimization), the application breadth is narrow. The candidate's experience at Stanford University as a Data Scientist, if current, suggests alignment with a research-heavy environment, which may or may not align with a broader industry role.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions suggest a strong independent work ethic and a focus on technical challenges, but team collaboration or communication skills cannot be evaluated.