Zhangjie Cao

Data Scientist

https://talent.gravityer.com/zhangjie-cao

PhD. student of Computer Science Department, Stanford University

Member since June 26, 2026

Key Strengths

Extensive experience in machine learning research and development, particularly in areas like domain adaptation, hashing, and inverse reinforcement learning.
Strong proficiency in Python, C++, and Cuda, essential for data science and deep learning roles.
Demonstrated ability to implement and release research code, indicating practical application of theoretical knowledge.
Familiarity with deep learning frameworks like TensorFlow and PyTorch (implied by project descriptions).

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects are heavily focused on academic research code releases, which suggests a strong inclination towards research and development. While this aligns with the technical demands of a Data Scientist role, the lack of diverse project types (e.g., industry applications, team projects, product development) makes it difficult to fully assess cultural fit for a collaborative, product-oriented environment. The candidate's experience level is listed as 0, which contradicts the depth of their projects, suggesting a potential mismatch in self-assessment or a focus purely on academic contributions rather than industry experience.

Soft Skills & Operational Fit

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

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Evaluating your skill match against the job requirements…

Assessing your cultural and operational fit

Projects

TPAMI-SAN

April 28, 2022 – April 28, 2022

TPAMI-SAN — GitHub repository

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Confidence-Aware-Imitation-Learning

September 30, 2021 – October 30, 2021

Confidence-Aware-Imitation-Learning — GitHub repository

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Learning-Feasibility-Different-Dynamics

September 22, 2021 – September 22, 2021

Code for 'Learning Feasibility to Imitate Demonstrators with Different Dynamics'

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TREX-pytorch

July 26, 2021 – September 22, 2021

A PyTorch implementation for the paper 'Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations'.

View Project

Learn-Imperfect-Varying-Dynamics

March 13, 2021 – May 28, 2024

Code for the paper 'Learning from Imperfect Demonstrations from Agents with Varying Dynamics'

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CDAN

August 19, 2018 – August 25, 2021

Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

View Project

DPH

July 14, 2018 – January 17, 2019

Code release for "Deep Priority Hashing" (ACMMM 2018)

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PADA

July 11, 2018 – October 29, 2018

Code release for "Partial Adversarial Domain Adaptation" (ECCV 2018)

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transfer-tensorflow

August 28, 2017 – September 18, 2017

A TensorFlow Library for Transfer Learning

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HashNet

July 18, 2017 – August 2, 2019

Code release for "HashNet: Deep Learning to Hash by Continuation" (ICCV 2017)

View Project

Key Strengths

Extensive experience in machine learning research and development, particularly in areas like domain adaptation, hashing, and inverse reinforcement learning.
Strong proficiency in Python, C++, and Cuda, essential for data science and deep learning roles.
Demonstrated ability to implement and release research code, indicating practical application of theoretical knowledge.
Familiarity with deep learning frameworks like TensorFlow and PyTorch (implied by project descriptions).

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

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

Zhangjie Cao

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Projects

Key Strengths

Cultural & Operational Fit