Anthony Lem
Hello! I completed my M.Sc. in Computer Science at the Robot Vision & Learning (RVL) Lab at the University of Toronto under the supervision of Professor Florian Shkurti. My research focused on generative AI for robot perception. Before that, I received my BASc. in Engineering Science at the University of Toronto, where I was fortunate to research at the Multimedia Laboratory under the supervision of Professor Konstantinos Plataniotis. I also had an internship at Qualcomm.
Publications
SICNav-Diffusion: Safe and Interactive Crowd Navigation with Diffusion Trajectory Predictions
Robotics and Automation Letter (RA-L), 2025
Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation
Association for the Advancement of Artificial Intelligence (AAAI) Conference on Artificial Intelligence, 2021
Projects
Animated Wallpaper Generation
Personal Project,
Generated animated desktop wallpapers with open-source state-of-the-art AI models. Used AI software and compute infrastructure including ComfyUI, Hugging Face, and Modal.
A Material Point Method for Snow Simulation
Individual Course Project,
Reimplemented the paper A Material Point Method For Snow Simulation in 2D using Python and Taichi Lang.
Compositional Premise Retrieval
Group Course Project,
Developed a novel method for training Transformer models for retrieving premises for Automatic Theorem Proving. Benchmarked baseline retrieval methods: TF-IDF, BM25, and Sentence-BERT.
Cloud Database System
Group Course Project,
Built a distributed database system inspired by Amazon’s Dynamo in Java with multiple servers that could be dynamically removed or added. System featured data replication across servers, heart beat for server failure detection, and gossiping to achieve eventual consistency.
Multimodal Content Moderation
Group Course Project,
Developed a state-of-the-art multimodal (image and text) content moderation model that performed well on Meta’s Hateful Memes Challenge for the company Clarifai. Built a computation pipeline to augment and generate synthetic text and image data samples using Stable Diffusion for training neural networks.