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CS PhD candidate @WashU @mvrl
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
unidrivev3
September 27, 2025 – September 27, 2025
based on unidrivev2, change it to blip-3o based continuous architecture
View Projectunidrivev1
August 23, 2025 – August 23, 2025
v1, text CE loss and image CE loss do seperately, optionally add mask for previous image token
View ProjectGroundingBooth.github.io
September 1, 2024 – September 1, 2024
GroundingBooth project page
View ProjectGroundingBooth_web
September 1, 2024 – September 1, 2024
GroundingBooth_web — GitHub repository
View ProjectMCPDepth
September 18, 2023 – Present
[CVPRW 2026] MCPDepth: Omnidirectional Depth Estimation via Stereo Matching from Multi-Cylindrical Panoramas
View ProjectVLPL
August 7, 2023 – Present
The official implementation of VLPL: Vision Language Pseudo Label for Multi-label Learning with Single Positive Labels
View Projectstarter-hugo-academic
August 20, 2021 – November 19, 2023
starter-hugo-academic — GitHub repository
View ProjectCultural Fit Analysis
The candidate's project portfolio demonstrates a strong inclination towards academic and research-driven work, particularly in computer vision and natural language processing. This aligns well with roles requiring innovation, deep technical investigation, and contribution to cutting-edge AI solutions. The diversity of projects, from multi-label learning to omnidirectional depth estimation, suggests adaptability and a broad interest within the data science domain. However, the lack of team-based or industry-specific projects makes it difficult to fully assess cultural fit in a corporate environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a strong technical focus and ability to contribute to research-heavy initiatives, which could imply strong problem-solving and independent work skills. However, collaboration, communication, and leadership aspects are not evident from the provided data.