Angus Fung

I am a Ph.D candidate at the University of Toronto, Robotics Institute, advised by Goldie Nejat, where I work on robot perception and control using self-supervised learning and generative AI.

Previously, I worked on learning algorithms at the Vector Institute where I was advised by Jimmy Ba. Currently, in my free-time, I am building Syncere AI with Aaron Tan to bring consumer robots into society.

My long-term goal is to bring useful robots into society, such as in domestic and hospitality settings. To achieve this, my research aims to discover the next-generation, performance-optimized form factor for these robots.

1. Music: I am active as a church organist having held positions at the Metropolitan United Church (under Dr. Patricia Wright) and St. Michael's Cathedral Basilica. I received my ARCT Diploma in Piano and Organ Performance in 2013 at the Royal Conservatory of Music.
2. Medicine: I build AI models to tackle open questions in neuroscience and neuro-ophthalmology with Dr. Anthony Lang and Dr. Edward Margolin.
   
3. Gaming: I am a hobbyist game developer with experience at deploying games at scale, specifically in game design, load balancing, DDoS mitigation, security protocols, and anti-cheat mechanisms.
4. Startups: I co-founded Scholarply and ONE800 with Aaron Tan, leveraging LLMs for scholarship applications and personal companionship.
5. Entertainment: I worked with 2x Grammy Award recipient Sean Leon to build AI technology for their God's Algorithm Project.

Updated: 10/01

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We present Finder, a novel approach to the multi-object search problem that leverages vision language models (VLMs) to efficiently locate multiple objects in diverse unknown environments. Our method combines semantic mapping with spatio-probabilistic reasoning and adaptive planning, improving object recognition and scene understanding through VLMs.

We present a novel people tracking architecture for mobile service robots using conditional latent diffusion models, which we name Latent Diffusion Track (LDTrack), to solve the robotic problem of tracking multiple dynamic people under intraclass variations.

We present a novel multimodal person detection architecture to address the mobile robot problem of person detection under intraclass variations (e.g. partial occlusion, varying illumination, pose deformation) by introducing our Temporal Invariant Multimodal Contrastive Learning (TimCLR) method.

We present a novel multi-robot person search system to generate search plans for multi-robot teams to find multiple dynamic users before a deadline.

We present the novel Weakly Supervised Mask Data Distillation architecture for autonomously generating pseudo segmentation labels.

We present a method to calibrate the time-varying extrinsic transformation between any number of cameras and achieves measurement excitation over the entire configuration space of the mechanism resulting in a more accurate calibration.

We investigate the first use of deep networks for victim identification in Urban Search and Rescue, for cases of partial occlusions and varying illumination, on a RGB-D dataset obtained by a mobile robot navigating cluttered USAR-like environments.