Qi Fang(方淇)

Hi! I am Qi Fang. First-year @ MScAC, University of Toronto.

My research enthusiasm lies in Computer Vision, AI Security, and Robotics. I am particularly interested in achieving robust robot perception, e.g. under the conditions of adverse weather and adversarial attacks.

Currently I am in research collaboration at AI4CE LAB, New York University, fortunately supervised by Prof. Chen Feng and Dr. Felix Juefei Xu. Previously I interned at iDLAB, Tsinghua University, advised by Prof. Wenjun Wang, where I focused on human behavior interacting with self-driving vehicles.

I am currently seeking a full-time research internship starting from May 2024, for a duration of eight months.

Feel free to contact me by email: qifang804 at gmail dot com

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

"To augment the performance of lidar perception under challenging meteorological conditions. Focusing on noise-removal and noise-utilization methods."

"To explore the potential of reverse adversarial attacks to realize robust vision as well as robust 'objects' to current deep vision models."

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

Tasks: 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

Tasks: 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