Eric Culbert Chi

Statistics, Machine Learning, Numerical Optimization

I am an associate professor in the Department of Statistics at Rice University. From 2015 to 2021, I was an assistant professor in the Department of Statistics at North Carolina State University. I earned my PhD in statistics at Rice University in 2011. After completing my PhD, I worked as a postdoctoral researcher in both the Human Genetics department at UCLA and the Digital Signal Processing group at Rice University. My research interests are in statistical learning and numerical optimization and their application to analyzing large and complicated modern data in biological science and engineering applications.

Works (42)

Updated: January 9th, 2025 16:00

2023 article

Bayesian Trend Filtering via Proximal Markov Chain Monte Carlo

Heng, Q., Zhou, H., & Chi, E. C. (2023, February 24). JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, Vol. 2.

By: Q. Heng n, H. Zhou & E. Chi 

author keywords: Convex optimization; Epigraphs; Moreau-Yosida envelope; Hamiltonian Monte Carlo; Trend filtering
TL;DR: This article extends the paradigm of proximal MCMC through introducing a novel new class of nondifferentiable priors called epigraph priors, and replaces the nonsmooth term in the posterior density with its Moreau-Yosida envelope, which enables the application of the gradient-based MCMC sampler Hamiltonian Monte Carlo. (via Semantic Scholar)
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Sources: ORCID, Web Of Science, NC State University Libraries
Added: January 20, 2023

2023 article

Robust Low-Rank Tensor Decomposition with the L-2 Criterion

Heng, Q., Chi, E. C., & Liu, Y. (2023, May 20). TECHNOMETRICS.

By: Q. Heng n, E. Chi  & Y. Liu *

author keywords: Inverse problem; L-2 criterion; Nonconvexity; Robustness; Tucker decomposition
TL;DR: A robust Tucker decomposition estimator based on the L2 criterion, called the Tucker- is presented, which has empirically stronger recovery performance in more challenging high-rank scenarios compared with existing alternatives. (via Semantic Scholar)
Sources: ORCID, Web Of Science
Added: April 11, 2023

2022 article

A Sharper Computational Tool for L2E Regression

Liu, X., Chi, E. C., & Lange, K. (2022, October 6). TECHNOMETRICS, Vol. 10.

By: X. Liu n, E. Chi  & K. Lange *

author keywords: Distance penalization; Integral squared error criterion; MM principle; Newton's method; Penalized estimation
TL;DR: The majorization-minimization (MM) principle is adopted to design a new algorithm for updating the vector of regression coefficients that achieves faster convergence than the previous alternating proximal gradient descent algorithm by Chi and Chi and improves performance in coefficient estimation and structure recovery. (via Semantic Scholar)
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Sources: ORCID, Web Of Science, NC State University Libraries
Added: October 8, 2022

2022 journal article

A User-Friendly Computational Framework for Robust Structured Regression with the L2 Criterion

Journal of Computational and Graphical Statistics.

By: J. Chi & E. Chi 

author keywords: Block-relaxation; Convex optimization; Minimum distance estimation; Regularization
TL;DR: This framework enables robust regression with the L2 criterion for additional structural constraints, works without requiring complex tuning procedures on the precision parameter, can be used to identify heterogeneous subpopulations, and can incorporate readily available nonrobust structured regression solvers. (via Semantic Scholar)
Source: ORCID
Added: March 30, 2022

2022 journal article

Revisiting convexity-preserving signal recovery with the linearly involved GMC penalty

PATTERN RECOGNITION LETTERS, 156, 60–66.

By: X. Liu n & E. Chi 

author keywords: Convexity-preserving nonconvex strategy; Generalized minimax concave penalty; Linearly involved convexity-preserving  model; Feasibility problem; Saddle-point problem
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16. Peace, Justice and Strong Institutions (OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: February 6, 2022

2021 article

An Interpretable Machine Learning Model to Classify Coronary Bifurcation Lesions

2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC), pp. 4432–4435.

By: X. Liu n, M. Vardhan *, Q. Wen*, A. Das *, A. Randles * & E. Chi 

MeSH headings : Coronary Artery Disease; Heart; Hemodynamics; Humans; Machine Learning; Stress, Mechanical
TL;DR: The experimental results show that CART can estimate a simple, interpretable, yet accurately predictive nonlinear model of TAWSS as a function of such features, and has the potential to refine predictions of disturbed hemodynamic flow based on an individual’s cardiac and lesion anatomy. (via Semantic Scholar)
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3. Good Health and Well-being (Web of Science; OpenAlex)
Sources: ORCID, Web Of Science, NC State University Libraries
Added: December 30, 2021

2021 journal article

COBRAC: a fast implementation of convex biclustering with compression

BIOINFORMATICS, 37(20), 3667–3669.

By: H. Yi *, L. Huang *, G. Mishne * & E. Chi 

TL;DR: This work proposes an implementation of fast convex biclustering called COBRAC to reduce the computing time by iteratively compressing problem size along the solution path and applies it to several gene expression datasets to demonstrate its effectiveness and efficiency. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: January 3, 2022

2021 journal article

Discovering Geometry in Data Arrays

Computing in Science & Engineering, 23(6), 42–51.

By: E. Chi 

author keywords: Arrays; IEEE Fellows; Tensors; Organizations; Optimization; Neurons; Lung cancer
TL;DR: A framework for identifying complicated underlying patterns in big and noisy data arrays is reviewed and the key role that the Department of Energy Computational Sciences Graduate Fellowship played in setting the stage for this work is recounted. (via Semantic Scholar)
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7. Affordable and Clean Energy (OpenAlex)
Source: ORCID
Added: December 23, 2021

2021 article

Multi-scale affinities with missing data: Estimation and applications

Zhang, M., Mishne, G., & Chi, E. C. (2021, November 5). STATISTICAL ANALYSIS AND DATA MINING.

By: M. Zhang n, G. Mishne * & E. Chi 

author keywords: kernels; missing data; penalized estimation
TL;DR: This paper proposes a new method to construct row and column affinities even when data are missing by building off a co‐clustering technique that exploits the coupled similarity structure among both the rows and columns of a data matrix. (via Semantic Scholar)
Sources: ORCID, Web Of Science
Added: November 6, 2021

2021 journal article

Non-invasive characterization of complex coronary lesions

SCIENTIFIC REPORTS, 11(1).

By: M. Vardhan *, J. Gounley *, S. Chen*, E. Chi , A. Kahn *, J. Leopold *, A. Randles *

MeSH headings : Computer Simulation; Coronary Angiography / methods; Coronary Disease / classification; Coronary Disease / diagnostic imaging; Diagnosis, Differential; Hemodynamics; Humans; Radiographic Image Interpretation, Computer-Assisted / methods; Shear Strength
TL;DR: It is demonstrated that in contrast to traditional pressure-based metrics, there is a significant difference in the intracoronary hemodynamic forces, such as ESS, in complex lesions compared to simple lesions at both resting and hyperemic physiological states. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: May 17, 2021

2021 article

SCALABLE ALGORITHMS FOR CONVEX CLUSTERING

2021 IEEE DATA SCIENCE AND LEARNING WORKSHOP (DSLW).

By: W. Zhou n, H. Yi *, G. Mishne * & E. Chi 

author keywords: Convex optimization; Parallel computing; Sparsity; Unsupervised Learning
TL;DR: A Scalable cOnvex cLustering AlgoRithm via Parallel Coordinate Descent Method (SOLAR-PCDM) that improves the algorithm’s scalability by combining a parallelizable algorithm with a compression strategy. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: December 6, 2021

2021 journal article

Sparse Single Index Models for Multivariate Responses

Journal of Computational and Graphical Statistics, 30(1), 115–124.

By: Y. Feng  n, L. Xiao  n & E. Chi 

author keywords: Alternating direction method of multipliers; High dimension; Multivariate response; Single index model; Sparsity
TL;DR: A sparse multivariate single index model is proposed, where responses and predictors are linked by unspecified smooth functions and multiple matrix level penalties are employed to select predictors and induce low-rank structures across responses. (via Semantic Scholar)
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16. Peace, Justice and Strong Institutions (OpenAlex)
Source: ORCID
Added: July 1, 2020

2020 journal article

BASELINE DRIFT ESTIMATION FOR AIR QUALITY DATA USING QUANTILE TREND FILTERING

ANNALS OF APPLIED STATISTICS, 14(2), 585–604.

By: H. Brantley n, J. Guinness* & E. Chi 

author keywords: Air quality; nonparametric quantile regression; trend estimation
TL;DR: The model provides better quantile trend estimates than existing methods and improves signal classification of low-cost air quality sensor output and proposes a parallelizable alternating direction method of moments (ADMM) algorithm to handle the computational challenge posed by very long time series. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
Sources: Web Of Science, NC State University Libraries
Added: July 20, 2020

2020 journal article

FastLORS: Joint modelling for expression quantitative trait loci mapping in R

STAT, 9(1).

By: J. Rhyne  n, X. Jeng  n, E. Chi  & J. Tzeng n 

author keywords: block coordinate descent; eQTL mapping; low-rank approximation; proximal gradient descent; sparse regression
TL;DR: FastLORS is a software package that implements a new algorithm to solve sparse multivariate regression for expression quantitative trait loci (eQTLs) mapping that reduces the computational cost compared with LORS. (via Semantic Scholar)
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Sources: ORCID, Web Of Science, NC State University Libraries
Added: May 7, 2020

2020 journal article

Multiway Graph Signal Processing on Tensors: Integrative Analysis of Irregular Geometries

IEEE SIGNAL PROCESSING MAGAZINE, 37(6), 160–173.

By: J. Stanley *, E. Chi  & G. Mishne *

author keywords: Tensors; Signal processing; Two dimensional displays; Geometry; Discrete Fourier transforms; Graphical models; Laplace equations
TL;DR: Modern signal processing frameworks that generalize GSP to multiway data are reviewed, starting from graph signals coupled to familiar regular axes, such as time in sensor networks, and then extending to general graphs across all tensor modes. (via Semantic Scholar)
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Sources: Web Of Science, NC State University Libraries
Added: November 30, 2020

2020 journal article

Provable Convex Co-clustering of Tensors

Journal of Machine Learning Research, 21(214), 1–58. http://jmlr.org/papers/v21/18-155.html

By: E. Chi , B. Gaines, W. Sun, H. Zhou & J. Yang

jmlr.org
Source: ORCID
Added: March 26, 2021

2019 conference paper

Co-manifold learning with missing data

In K. Chaudhuri & R. Salakhutdinov (Eds.), International Conference on Machine Learning (Vol. 97, pp. 4605–4614). http://proceedings.mlr.press/v97/mishne19a.html

By: G. Mishne, E. Chi  & R. Coifman

Ed(s): K. Chaudhuri & R. Salakhutdinov

Event: at Long Beach, California, USA

proceedings.mlr.press
Source: ORCID
Added: December 6, 2019

2019 journal article

Going Off the Grid: Iterative Model Selection for Biclustered Matrix Completion

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 28(1), 36–47.

By: E. Chi , L. Hu n, A. Saibaba n  & A. Rao *

author keywords: Convex optimization; Degrees of freedom; Hutchinson estimator; Information criterion; Penalization; Sparse linear systems
TL;DR: This work presents a novel iterative procedure for directly minimizing an information criterion to select an appropriate amount of row and column smoothing, namely, to perform model selection. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: May 13, 2019

2019 review

Matrix completion from a computational statistics perspective

[Review of ]. WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL STATISTICS, 11(5).

By: E. Chi  & T. Li *

author keywords: Collaborative filtering; low-rank approximation; missing data; optimization; recommender systems
TL;DR: This review examines the success behind low‐rank matrix completion, one of the most studied and employed versions of matrix completion and sees opportunities to weaken the commonly enforced assumption of missing completely at random in matrix completion. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 26, 2019

2019 journal article

Recovering Trees with Convex Clustering

SIAM Journal on Mathematics of Data Science, 1(3), 383–407.

By: E. Chi  & S. Steinerberger

author keywords: convex optimization; fused lasso; hierarchical clustering; penalized regression; sparsity
TL;DR: It is proved that if the affinities of $w_{ij}$ reflect a tree structure in the $\left\{x_1, \dots, x_n\right\}$, then the convex clustering solution path reconstructs the tree exactly. (via Semantic Scholar)
Source: ORCID
Added: December 6, 2019

2019 article

Shape Constrained Tensor Decompositions

2019 IEEE INTERNATIONAL CONFERENCE ON DATA SCIENCE AND ADVANCED ANALYTICS (DSAA 2019), pp. 287–297.

By: B. Lusch *, E. Chi  & J. Kutz *

author keywords: tensor decomposition; multiway arrays; multilinear algebra; higher-order singular value decomposition (HOSVD); over-complete libraries; sparse regression
TL;DR: A new low-rank tensor factorization where one mode is coded as a sparse linear combination of elements from an over-complete library to provide a viable technique for analyzing multitudes of data in a more comprehensible fashion. (via Semantic Scholar)
Sources: Web Of Science, NC State University Libraries
Added: July 13, 2020

2019 journal article

Tensor canonical correlation analysis

Stat, 8(1).

By: E. Min *, E. Chi  & H. Zhou *

author keywords: block coordinate ascent; CP decomposition; multidimensional array data
TL;DR: This paper presents tensor CCA (TCCA) to discover relationships between two tensors while simultaneously preserving multidimensional structure of the tensors and utilizing substantially fewer parameters, and proposes efficient estimation algorithms with global convergence guarantees. (via Semantic Scholar)
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Source: ORCID
Added: January 3, 2020

2018 journal article

A majorization-minimization algorithm for split feasibility problems

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 71(3), 795–828.

By: J. Xu *, E. Chi , M. Yang n & K. Lange *

author keywords: Majorize-minimize; Nonlinear split feasibility; Intensity modulated radiation therapy; Proximity function minimization; Constrained regression
TL;DR: It is shown that the Euclidean norm appearing in the proximity function of the non-linear split feasibility problem can be replaced by arbitrary Bregman divergences, and the algorithm is based on the principle of majorization–minimization, is amenable to quasi-Newton acceleration, and comes complete with convergence guarantees under mild assumptions. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
10. Reduced Inequalities (Web of Science)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: December 10, 2018

2017 journal article

Convex Biclustering

BIOMETRICS, 73(1), 10–19.

By: E. Chi , G. Allen* & R. Baraniuk *

author keywords: Clustering; Convex optimization; Fused lasso; Gene expression; Reproducible research; Structured sparsity
MeSH headings : Algorithms; Cluster Analysis; Computational Biology / methods; Data Interpretation, Statistical; Databases, Genetic; Gene Expression Profiling / methods; Gene Regulatory Networks; Oligonucleotide Array Sequence Analysis
TL;DR: This work presents a convex formulation of the biclustering problem that possesses a unique global minimizer and an iterative algorithm, COBRA, that is guaranteed to identify it and demonstrates the advantages of the approach, which includes stably and reproducibly identifying biclusterings, on simulated and real microarray data. (via Semantic Scholar)
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2017 conference paper

Generalized Linear Model Regression under Distance-to-set Penalties

In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, & R. Garnett (Eds.), Neural Information Processing Systems (pp. 1385–1395). https://papers.nips.cc/paper/6737-generalized-linear-model-regression-under-distance-to-set-penalties

By: J. Xu, E. Chi  & K. Lange

Ed(s): I. Guyon, U. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett

papers.nips.cc
Source: ORCID
Added: December 6, 2019

2016 journal article

ESTIMATING A COMMON PERIOD FOR A SET OF IRREGULARLY SAMPLED FUNCTIONS WITH APPLICATIONS TO PERIODIC VARIABLE STAR DATA

ANNALS OF APPLIED STATISTICS, 10(1), 165–197.

By: J. Long  n, E. Chi  & R. Baraniuk  n

author keywords: Astrostatistics; penalized likelihood; period estimation; functional data; MM algorithm; block coordinate descent
TL;DR: Two new methods for period estimation when brightnesses are poorly--sampled in all filters are introduced and a fast algorithm to optimize the penalized likelihood by combining block coordinate descent with the majorization-minimization (MM) principle is developed. (via Semantic Scholar)
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Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2016 journal article

k-POD: A Method for k-Means Clustering of Missing Data

AMERICAN STATISTICIAN, 70(1), 91–99.

By: J. Chi, E. Chi  & R. Baraniuk

author keywords: Clustering; k-means; Imputation; Majorization-minimization; Missing data
TL;DR: The k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data. (via Semantic Scholar)
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UN Sustainable Development Goal Categories
Sources: Web Of Science, ORCID, NC State University Libraries
Added: August 6, 2018

2015 journal article

Convex Clustering: An Attractive Alternative to Hierarchical Clustering

PLoS Computational Biology, 11(5), e1004228.

By: G. Chen*, E. Chi , J. Ranola * & K. Lange *

MeSH headings : Algorithms; Cluster Analysis; Computational Biology / methods; Databases, Genetic; Gene Expression Profiling / methods; Humans; Pattern Recognition, Automated / methods; Software
TL;DR: A novel proximal distance algorithm for minimizing the objective function of convex clustering is derived and tested and the program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. (via Semantic Scholar)
Source: ORCID
Added: December 6, 2019

2015 journal article

Splitting Methods for Convex Clustering

Journal of Computational and Graphical Statistics, 24(4), 994–1013.

By: E. Chi  & K. Lange

author keywords: Alternating direction method of multipliers; Alternating minimization algorithm; Convex optimization; Hierarchical clustering; k-means; Regularization paths
TL;DR: This work presents two splitting methods for solving the convex clustering problem, an instance of the alternating direction method of multipliers (ADMM) and the alternating minimization algorithm (AMA), and demonstrates the performance of the ADMM and AMA on both simulated and real data examples. (via Semantic Scholar)
Source: ORCID
Added: December 6, 2019

2014 journal article

A Brief Survey of Modern Optimization for Statisticians

INTERNATIONAL STATISTICAL REVIEW, 82(1), 46–70.

By: K. Lange *, E. Chi  & H. Zhou n

author keywords: acceleration; augmented Lagrangian; penalization; MM algorithm; Newton's method; Block relaxation
TL;DR: This broad survey stresses a few important principles in algorithm design, and suggests that it is more productive to mix and match them than view these principles in isolation. (via Semantic Scholar)
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Sources: ORCID, Web Of Science
Added: August 6, 2018

2014 journal article

A Look at the Generalized Heron Problem through the Lens of Majorization-Minimization

The American Mathematical Monthly, 121(2), pp. 95–108.

By: E. Chi  & K. Lange *

TL;DR: By exploiting the majorizationminimization (MM) principle of computational statistics and rudimentary techniques from differential calculus, this work is able to construct a very fast algorithm for solving the Euclidean version of the generalized Heron problem. (via Semantic Scholar)
Source: ORCID
Added: December 6, 2019

2014 journal article

Distance majorization and its applications

Mathematical Programming, 1–28.

By: E. Chi , H. Zhou n & K. Lange *

author keywords: Constrained optimization; Majorization-minimization (MM); Sequential unconstrained minimization; Projection
TL;DR: This proposal is an instance of a sequential unconstrained minimization technique and revolves around three ideas: the majorization-minimization principle, the classical penalty method for constrained optimization, and quasi-Newton acceleration of fixed-point algorithms. (via Semantic Scholar)
Sources: ORCID, Web Of Science
Added: August 6, 2018

2014 journal article

Rejoinder

INTERNATIONAL STATISTICAL REVIEW, 82(1), 81–89.

By: K. Lange *, E. Chi  & H. Zhou n

Contributors: K. Lange *, E. Chi  & H. Zhou n

Sources: ORCID, Web Of Science
Added: August 6, 2018

2014 journal article

Robust Parametric Classification and Variable Selection by a Minimum Distance Criterion

Journal of Computational and Graphical Statistics, 23(1), 111–128.

By: E. Chi  & D. Scott *

author keywords: Elastic net; Implosion breakdown; LASSO; Logistic regression; Majorization-minimization; Robust estimation
TL;DR: It is shown that by choosing a minimum distance criterion together with an elastic net penalty, one can simultaneously find a parsimonious model and avoid estimation implosion even in the presence of many outliers in the important small n large p situation. (via Semantic Scholar)
Source: ORCID
Added: December 6, 2019

2014 journal article

Stable estimation of a covariance matrix guided by nuclear norm penalties

Computational Statistics & Data Analysis, 80(0), 117–128.

By: E. Chi  & K. Lange *

author keywords: Covariance estimation; Regularization; Condition number; Discriminant analysis; EM clustering
TL;DR: A novel prior is introduced to ensure a well-conditioned maximum a posteriori (MAP) covariance estimate and shrinks the sample covariance estimator towards a stable target and leads to a MAP estimator that is consistent and asymptotically efficient. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
UN Sustainable Development Goal Categories
10. Reduced Inequalities (OpenAlex)
Source: ORCID
Added: December 6, 2019

2013 journal article

Genotype imputation via matrix completion

GENOME RESEARCH, 23(3), 509–518.

By: E. Chi , H. Zhou n, G. Chen*, D. Del Vecchyo * & K. Lange *

MeSH headings : Algorithms; Artificial Intelligence; Computer Simulation; Genome, Human; Genotype; HapMap Project; Humans; Microarray Analysis; Models, Genetic; Polymorphism, Single Nucleotide; Software
TL;DR: Compared with leading imputation programs, the matrix completion algorithm embodied in the program MENDEL-IMPUTE achieves comparable imputation accuracy while reducing run times significantly. (via Semantic Scholar)
UN Sustainable Development Goals Color Wheel
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Sources: ORCID, Web Of Science
Added: August 6, 2018

2013 conference paper

Imaging genetics via sparse canonical correlation analysis

Biomedical Imaging (ISBI), 2013 IEEE 10th International Symposium on, 740–743.

By: E. Chi , G. Allen*, H. Zhou  n, O. Kohannim*, K. Lange * & P. Thompson *

TL;DR: This work investigates the use of sparse canonical correlation analysis (CCA) to home in on sets of genetic variants that explain variance in a set of images, extending recent work on penalized matrix decomposition to account for the correlations in both datasets. (via Semantic Scholar)
Source: ORCID
Added: December 6, 2019

2012 journal article

On Tensors, Sparsity, and Nonnegative Factorizations

SIAM Journal of Matrix Analysis and Applications, 33(4), 1272–1299.

By: E. Chi  & T. Kolda *

author keywords: nonnegative tensor factorization; nonnegative CANDECOMP-PARAFAC; Poisson tensor factorization; Lee-Seung multiplicative updates; majorization-minimization algorithms
TL;DR: This paper proposes that the random variation is best described via a Poisson distribution, which better describes the zeros observed in the data as compared to the typical assumption of a Gaussian distribution, and presents a new algorithm for Poisson tensor factorization called CANDECOMP--PARAFAC alternating Poisson regression (CP-APR), based on a majorization-minimization approach. (via Semantic Scholar)
Source: ORCID
Added: December 6, 2019

2006 journal article

Proton auroral intensifications and injections at synchronous altitude

Geophysical Research Letters, 33(6).

By: E. Chi , S. Mende *, M. Fok * & G. Reeves *

Contributors: E. Chi , S. Mende *, M. Fok * & G. Reeves *

Source: ORCID
Added: December 6, 2019

2005 journal article

Different Algorithms for Normal and Protection Paths

Journal of Network and Systems Management, 13(1), 13–33.

By: R. Gupta *, E. Chi  & J. Walrand *

TL;DR: This paper presents a modular suite of algorithms that enable us to manage normal and protection paths differently and concludes that in order to choose an optimal algorithm for a protected QoS routing application, it is recommended to also consider a combination of two different algorithms for normal and backup paths. (via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: February 21, 2020

2004 journal article

Proactive resource provisioning

Computer Communications, 27(12), 1174–1182.

By: E. Chi , M. Fu * & J. Walrand *

author keywords: resource provisioning; quality of service; voice over IP; MPLS; service level agreement
TL;DR: This work considers a dynamic Service Level Agreement negotiation scheme between peer autonomous systems (ASes) that implement DiffServ per domain behaviors and presents a heuristic but computationally simple and distributed scheme that uses traffic statistics to forecast the near-future demand. (via Semantic Scholar)
Sources: Crossref, NC State University Libraries
Added: February 21, 2020

2004 journal article

Proactive resource provisioning

Computer Communications, 27(12), 1174–1182.

By: E. Chi , M. Fu & J. Walrand

Sources: Crossref, NC State University Libraries
Added: February 21, 2020

Employment

Updated: July 7th, 2021 19:32

2021 - present

Rice University Houston, Texas, US
Associate Professor Statistics

2015 - 2021

North Carolina State University Raleigh, North Carolina, US
Assistant Professor Statistics

2013 - 2015

Rice University Houston, TX, US
Postdoctoral Research Associate Electrical and Computer Engineering

2011 - 2013

University of California Los Angeles Los Angeles, CA, US
Postdoctoral Scholar Human Genetics

Education

Updated: July 21st, 2014 05:06

2007 - 2011

Rice University Houston, Texas, US
Ph.D. Statistics

Funding History

Funding history based on the linked ORCID record. Updated: May 9th, 2020 08:44

grant September 23, 2019 - August 31, 2022
Imputing single cell RNA sequencing data: Mathematical, statistical and computational challenges
National Institute of General Medical Sciences
grant July 1, 2018 - June 30, 2023
CAREER: Stable and Scalable Estimation of the Intrinsic Geometry of Multiway Data
Directorate for Mathematical & Physical Sciences

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