Fei Feng

Data Scientist

https://talent.gravityer.com/fei-feng

Ph.D. Candidate at UCLA applied math.

Member since June 26, 2026

Key Strengths

Demonstrated experience with C++ and Python, which are foundational for data science and machine learning.
Extensive personal projects in optimization, reinforcement learning, and numerical methods, indicating a strong theoretical and practical interest in core data science domains.
Experience with scientific computing tools like MATLAB, suggesting familiarity with mathematical modeling and algorithm implementation.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are primarily academic/research-oriented, focusing on optimization and machine learning algorithms. This indicates a strong fit for roles requiring deep analytical skills and research capabilities. The diversity of projects across C++, Python, and MATLAB suggests adaptability in technical environments. However, the lack of team-based projects or industry experience makes it difficult to fully assess collaboration and broader cultural fit.

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. No psychometric test results or interview feedback provided.

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Projects

ENIAC

March 16, 2021 – April 7, 2021

code for paper: Provably Correct Optimization and Exploration with Non-linear Policies

View Project

FlorenceFeng.github.io

February 23, 2021 – January 19, 2022

FlorenceFeng.github.io — GitHub repository

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AsyncQVI

January 31, 2019 – March 15, 2019

A light C++11 package for three reinforcement learning algorithms.

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IPSVRG

January 26, 2019 – May 4, 2019

A light MATLAB package for acceleration of SVRG and Katyusha X by inexact preconditioning.

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IPSVRG

December 18, 2018 – May 4, 2019

IPSVRG — GitHub repository

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Async-LP

November 13, 2018 – November 13, 2018

Async-LP — GitHub repository

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AsyncQVI

October 30, 2018 – March 16, 2019

A light c++11 package for three reinforcement learning algorithms.

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A2BCD

February 16, 2018 – September 2, 2019

Code for the numerical test in the paper

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TMAC

June 3, 2016 – April 25, 2017

TMAC: A Toolbox of Modern Async-Parallel, Coordinate, Splitting, and Stochastic Methods

View Project

Key Strengths

Demonstrated experience with C++ and Python, which are foundational for data science and machine learning.
Extensive personal projects in optimization, reinforcement learning, and numerical methods, indicating a strong theoretical and practical interest in core data science domains.
Experience with scientific computing tools like MATLAB, suggesting familiarity with mathematical modeling and algorithm implementation.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Insufficient data to assess soft skills and operational fit. No psychometric test results or interview feedback provided.

Fei Feng

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit