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Machine Learning Researcher
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Arizona State University
Data Scientist
June 19, 2026 – Present
flamethrower-core
July 27, 2024 – July 27, 2024
Learn how to build your own deep learning library, from scratch.
View ProjectopenQMComputations
March 30, 2023 – August 29, 2023
openQMComputations — GitHub repository
View ProjectProtein-LM-HF-Finetune
January 4, 2023 – January 9, 2023
Protein-LM-HF-Finetune — GitHub repository
View ProjectMixed-Curvature-Pathways
September 23, 2022 – July 11, 2025
Mixed Curvature Embedding of PathBank Pathways
View ProjectSobolev
February 19, 2018 – April 2, 2018
Implementation of DeepMind's "Sobolev Training for Neural Networks"
View Projectneural-style
June 12, 2017 – June 12, 2017
A Tensorflow Implementation of "A Neural Algorithm of Artistic Style"
View ProjectMachineLearningTutorials
December 28, 2016 – July 8, 2017
Tutorials presenting a variety of concepts in machine learning.
View ProjectRetina
June 5, 2016 – August 22, 2016
Scientific Visualization Using Matplotlib and Plotly
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong inclination towards personal learning and exploration in machine learning and data science, which suggests a self-driven and curious individual. The diversity of projects (e.g., neural style transfer, protein language models, scientific visualization) indicates a broad interest within the data science domain. However, the lack of team-based or professional projects makes it difficult to assess collaboration and cultural integration in a corporate environment. The experience level of 0 and future-dated role also limit the assessment of cultural fit based on past professional interactions.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's experience level is listed as 0, and the current role is future-dated, making it difficult to evaluate real-world collaboration, problem-solving, or communication in a professional setting. Project descriptions are concise but do not offer insight into soft skills.