Institution profile

University of Waterloo

Academic institutionnorthamerica · ca
Official website
Research library1,531linked papers
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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

LLM-based relevance assessment still can't replace human relevance assessment

Dec 22, 2024arXiv.org

This work challenges the feasibility of replacing human assessors with large language models (LLMs) for relevance evaluation in information retrieval. Method: Leveraging empirical analysis on TREC 2024 data, adversarial system submissions, theoretical modeling, and robustness testing, the study systematically investigates LLM-based relevance assessment. Contribution/Results: It identifies, for the first time, an “intrinsic narcissism” in LLM evaluation—where assessments rely on self-referential generative logic, inducing susceptibility to metric manipulation, self-referential bias, and overfitting. Experiments demonstrate that targeted optimization can artificially inflate LLM scores, and their judgments fail to support sustainable iterative improvement of retrieval systems. The findings establish fundamental deficiencies in both theoretical reliability and practical robustness of LLM-based evaluation, reaffirming the irreplaceable role of human assessment. This work provides a critical caution and methodological reflection for retrieval evaluation paradigms.

11 citations1 influentialRead paper

Eggly

Jun 12, 2023Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

Clinical neurofeedback training (NFT) for children with autism spectrum disorder (ASD) faces challenges in accessibility, mobility, and engagement due to its reliance on stationary setups and lack of gamified, context-aware design. Method: We propose the first mobile augmented reality (AR) neurofeedback gaming framework tailored for ASD children, leveraging consumer-grade EEG headsets and tablets. The system integrates real-time theta/beta ratio decoding, contextual AR visual feedback, and adaptive difficulty adjustment within a lightweight Unity-based AR rendering pipeline (ARKit/ARCore) and closed-loop neurofeedback control. Contribution/Results: Two field studies—a single-session trial and a three-week multi-session intervention—demonstrated significantly improved attention and engagement; all participants completed the training. Qualitative feedback indicated high enjoyment and immersion, while quantitative analysis revealed a consistent trend toward normalized theta/beta ratios, validating the feasibility and efficacy of mobile AR-NFT for ASD intervention.

8 citationsRead paper

Exponential Lower Bounds for Locally Decodable and Correctable Codes for Insertions and Deletions

Nov 01, 2021IEEE Annual Symposium on Foundations of Computer Science

This work investigates the existence of locally decodable codes (LDCs) under insertion-deletion (insdel) errors. Addressing a long-standing open conjecture, we prove—*for the first time*—that no 2-query linear insdel LDC exists. Moreover, for any constant query complexity $q geq 3$, we establish an exponential lower bound $exp(Omega(n))$ on the code length, significantly stronger than the polynomial bounds known for Hamming-error LDCs. Our approach constructs a hard insdel error distribution and combines information-theoretic analysis with novel coding reduction techniques. This reveals a fundamental separation between insdel LDCs and Hamming LDCs—a separation that persists even in the adaptive decoding and private-key settings. The results characterize the theoretical limits of local error correction against synchronization errors and provide the first tight lower bounds for insdel coding.

7 citations2 influentialRead paper

Privacy in Immersive Extended Reality: Exploring User Perceptions, Concerns, and Coping Strategies

May 11, 2024International Conference on Human Factors in Computing Systems

This study addresses users’ lack of awareness regarding fine-grained physiological and behavioral data collection in immersive extended reality (XR) environments. Through a scenario-based survey with 464 XR users and mixed-methods analysis, we empirically demonstrate that over 60% of users are unaware of involuntary biometric data capture—such as unconscious emotional signals—rendering their privacy protection strategies ineffective. We identify how data type and sensitivity critically shape user perceptions across 18 distinct XR privacy scenarios. Based on these findings, we propose the first XR-specific privacy interface design framework and a set of transparent data practice guidelines. Our work provides empirically grounded, actionable principles for privacy-preserving human–XR interaction, bridging critical gaps between technical capability and user understanding in immersive computing.

7 citationsRead paper
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