Institution profile

University of Southern California

Academic institutionnorthamerica · us
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Research library2,128linked papers
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Selected work

Representative Papers

Computational Grids

Oct 01, 1998International Conference on High Performance Computing for Computational Science

This paper addresses the challenge of coordinating geographically distributed, heterogeneous, and autonomous computing resources. Method: It systematically introduces the “computational grid” concept and architecture for building a scalable, secure, and transparent virtual supercomputer. Key innovations include resource virtualization, cross-domain trust mechanisms, a unified naming and scheduling model, a distributed middleware framework, resource discovery and scheduling algorithms, a prototype security authentication protocol based on the Grid Security Infrastructure (GSI), and a cross-platform communication standard. Contribution/Results: The work establishes the foundational paradigm of grid computing, providing both theoretical grounding and practical implementation blueprints. It directly enabled the development of core infrastructure—including the Globus Toolkit—and catalyzed the advancement of e-Science. Moreover, it served as a seminal intellectual precursor to modern cloud and edge computing paradigms.

363 citations9 influentialRead paper

SparseGS: Real-Time 360{deg} Sparse View Synthesis using Gaussian Splatting

Nov 30, 2023

This work addresses the severe degradation in 3D Gaussian Splatting (3DGS) reconstruction quality under sparse training views (only 3–12 images), manifesting as “floating artifacts” and “background collapse” in unseen viewpoints. To tackle this, we propose a depth-prior-guided optimization framework. Our key contributions are: (1) a geometry-aware depth prior derived from monocular depth estimation, enforcing spatial plausibility of Gaussian distributions; (2) an unseen-view regularization module that explicitly enhances generalization under sparse view coverage; and (3) an adaptive joint geometry-density pruning strategy to improve reconstruction compactness and stability. Integrating differentiable Gaussian rendering, depth-guided optimization, and viewpoint-aware regularization, our method achieves state-of-the-art performance on Mip-NeRF360, LLFF, and DTU benchmarks—reaching top-tier forward-facing scene quality using only three input images, with efficient training and real-time inference.

63 citations5 influentialRead paper

How to Detect Network Dependence in Latent Factor Models? A Bias-Corrected CD Test

Sep 01, 2021

This paper addresses the failure of residual cross-sectional dependence tests in latent factor panel models. We propose a bias-corrected CD* test statistic. Theoretically, we first establish the asymptotic validity of the standard CD test under weak factors and rigorously derive the asymptotic standard normality of CD* under the null hypothesis, while demonstrating its high local power against network-type alternatives. Methodologically, the CD* test integrates factor estimation, residual extraction, and analytical bias correction, accommodating both strong and weak factors as well as serially correlated errors. Monte Carlo simulations show that CD* achieves accurate size and superior power in small samples, consistently outperforming the JR test. Empirically, applying CD* to a housing price dynamics model across 377 U.S. metropolitan statistical areas reveals statistically significant spatial dependence in residuals.

49 citations1 influentialRead paper

Nuclear norm regularized estimation of panel regression models

Oct 25, 2018

This paper addresses identification and estimation challenges in interactive fixed-effects panel regressions arising from low-rank covariates and unknown factor dimensions. We propose two convex optimization estimators based on nuclear-norm (trace-norm) regularization and minimization. Unlike conventional non-convex least squares methods—which suffer from local optima and require pre-specified factor numbers—our approach guarantees global optimality, automatically accommodates low-rank structure, and handles unknown factor dimensions. By employing iterative weighted least squares, we construct a convex algorithm asymptotically equivalent to the LS estimators of Bai (2009) and Moon–Weidner (2017). We establish consistency and asymptotic efficiency of the estimators, while significantly reducing computational complexity. Empirically, the method robustly avoids local optima. This work constitutes the first systematic application of nuclear-norm convex optimization to interactive-effect panel estimation, achieving both statistical efficiency and computational feasibility.

48 citations9 influentialRead paper

FedBCGD: Communication-Efficient Accelerated Block Coordinate Gradient Descent for Federated Learning

Oct 28, 2024ACM Multimedia

This work addresses the high communication overhead of large-scale models, such as Vision Transformers, in federated learning by proposing Federated Block Coordinate Gradient Descent (FedBCGD) and its accelerated variant, FedBCGD+. The method introduces, for the first time in federated learning, a block-wise parameter communication mechanism that uploads only a subset of parameter blocks per round, combined with stochastic variance reduction and client drift control strategies. Theoretical analysis shows that the communication complexity is reduced by a factor of 1/N compared to existing methods, where N denotes the number of blocks. Experimental results demonstrate that the proposed algorithms achieve faster convergence and higher communication efficiency than current state-of-the-art approaches.

37 citations2 influentialRead paper
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