Qi Fang(方淇)

I am a graduate student at the Department of Computer Science, University of Toronto. I am interested in general-purpose robotics that exhibit several key capabilities:

• Visual and behavioral robustness
• Autonomous operation with strong generalization
• Self-optimization over time
• Interpretable competencies
• Responsiveness to human emotional needs

My broader interests extend to related fields including: Reinforcement Learning (RL), language and vision models (LLMs, VLMs, VLAs), Human-Computer Interaction (HCI), and trustworthy AI systems.

I completed my Bachelor of Computer Science at Beijing University of Posts and Telecommunications (BUPT). I collaborated with Prof. Chen Feng and Dr. Felix Juefei Xu at AI4CE LAB, New York University.

I am open to research collaborations. Feel free to reach me via qifang at cs.toronto.edu

Gmail  |  Google Scholar  |  Github

We proposed a novel probability-guaranteed sampling method inspired by RANSAC to achieve a trust-worthy group of benign agents, outperforming adversarial training Method by 73%.

We proposed a new LiDAR perception task, Occupancy Completion and Forecasting (OCF), combining scene completion and forecasting in autonomous driving, and evaluates it using the newly curated OCFBench dataset.

We validated a key indicator of relative position between the ego vehicle and surrounding vehicles having important effects on take-over performance and driving safety.

Research Topics:
[1] Multi-agent Collaboration / [2] Adversarial Machine Learning / [3] Robust Robot Vision

[1][2] -> Among Us, accepted to ICCV 23'
[2] -> Reverse Adversarial Learning
[3] -> 3D Perception in Adverse Weather Conditions

Design, develop, and test autonomous driving strategies for DiDi’s level-4 robotaxis to optimize the design of algorithms, data science models, and autonomous features in terms of their reliability, safety, and efficiency

Research, quality assurance, user testing, evaluation and optimization of XPeng’s Valet Parking Assist (VPA) feature

Research Topics: Human factors and ergonomics in conditional automated driving. -> AHFE 2021 Paper

Major Courses: Operating System / Database System / Discrete Mathematics / Machine Learning / Data Structures / Compilers / Computer Networks