Institution profile

University of Toronto

Academic institutionnorthamerica · ca
Official website
Research library2,046linked papers
Opportunities0open roles
Selected work

Representative Papers

NeRF: Neural Radiance Field in 3D Vision, A Comprehensive Review

Oct 01, 2022

This survey addresses the lack of unified taxonomies and reproducible benchmarks in existing NeRF literature. We propose a dual-dimensional classification framework—spanning architectural design and application scenarios—to systematically unify implicit neural representations and differentiable volumetric rendering theory. Our structured review encompasses over 120 works, and we introduce the first open-source, standardized benchmark evaluating cross-model performance and inference speed. Key technical challenges—including radiance field optimization, multi-view geometric constraints, and real-time rendering—are distilled and analyzed. We further identify promising research directions, such as scalable scene representation and physically consistent modeling. The survey bridges theoretical rigor with practical utility, serving as both an authoritative entry point and a foundational reference for the NeRF community.

53 citations2 influentialRead paper

Application-Driven Innovation in Machine Learning

Mar 26, 2024International Conference on Machine Learning

Application-driven machine learning (ML) research has been systematically undervalued in academia, leading to a growing disconnect between algorithmic innovation and real-world needs; this marginalization is reinforced by structural biases in peer review, faculty hiring, and pedagogy. Method: This paper introduces, for the first time, a formally defined “application-driven ML research paradigm,” elucidating its complementary relationship with the dominant methodology-driven paradigm. Drawing on interdisciplinary frameworks from education theory, research governance, and ML practice—and substantiated by empirical case studies and institutional critique—it diagnoses three systemic barriers hindering such research. Contribution/Results: The core contribution is a set of actionable, process-level interventions to reform academic evaluation systems, grounded in both theoretical analysis and pragmatic implementation pathways. These proposals have already catalyzed curricular reforms in AI education and adjustments to national funding review criteria across multiple universities, fostering cross-domain collaboration and methodological feedback loops between application domains and core ML research.

22 citations2 influentialRead paper

In-and-Out: Algorithmic Diffusion for Sampling Convex Bodies

May 02, 2024Neural Information Processing Systems

This work addresses efficient uniform sampling from high-dimensional convex bodies, providing strong convergence guarantees under Rényi divergence—including total variation (TV), Wasserstein-2 ($mathcal{W}_2$), Kullback–Leibler (KL), and $chi^2$ divergences. We propose a novel stochastic walk algorithm that models sampling through the lens of stochastic diffusion—a first in this context—and characterizes convergence rates via the functional isoperimetric constant of the target distribution, thereby departing from conventional polynomial mixing-time analysis. Theoretically, our algorithm achieves the optimal time complexity $O^*(n^2 R^2)$, where $n$ denotes dimensionality and $R$ the body’s diameter. Moreover, we derive unified, tight convergence bounds across the entire Rényi divergence family. To our knowledge, this is the first uniform sampling scheme attaining simultaneous optimality under multiple probability metrics.

12 citations2 influentialRead paper

How Viable are Energy Savings in Smart Homes? A Call to Embrace Rebound Effects in Sustainable HCI

Aug 01, 2023ACM J. Comput. Sustain. Soc.

Energy savings from smart home technologies are often undermined by rebound effects—behavioral or systemic compensations triggered by increased efficiency—rendering sustainability gains transient. Method: Through a cross-disciplinary literature mapping analysis across Web of Science, Scopus, IEEE Xplore, Springer, and ACM SIGCHI proceedings, this study systematically identifies research gaps concerning rebound effects in computing, human-computer interaction (HCI), and smart home domains. Contribution/Results: We propose the first classification framework for rebound effects tailored to sustainable HCI, along with corresponding intervention pathways. Findings reveal that current energy-efficiency evaluations routinely neglect rebound mechanisms, while HCI is uniquely positioned to advance rebound identification, computational modeling, and behaviorally informed interventions. This work establishes a theoretical foundation and methodological toolkit for accurately assessing the real-world environmental impact of smart home systems.

12 citations1 influentialRead paper

ChatBench: From Static Benchmarks to Human-AI Evaluation

Mar 22, 2025Annual Meeting of the Association for Computational Linguistics

Existing LLM benchmarks (e.g., MMLU) evaluate only “AI-alone” performance, failing to reflect real-world human-AI collaboration. Method: We introduce ChatBench—the first benchmark for human-AI collaborative question answering—built from authentic interactions where humans and LLMs jointly solve MMLU questions. It comprises an open-source dataset of 396 questions, 144K answers, and 7,336 dialogue turns, categorized into AI-alone, user-alone, and user-AI settings. We further propose a differentiable user simulator to enable scalable, interactive evaluation. Contribution/Results: Our work establishes the first systematic evaluation paradigm for human-AI collaboration. We find no significant correlation between AI-alone accuracy and user-AI performance, revealing that static benchmarks substantially misestimate actual collaborative efficacy. The user simulator improves evaluation correlation by over 20 percentage points. ChatBench provides both a foundational benchmark and a methodological framework to advance LLM research in human-AI co-intelligence.

8 citations1 influentialRead paper
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