This award is given annually to up to four graduate students working in the area of Statistical Computing or Graphics. The papers submitted to this competition are meant to be largely the work of the student, and hence we require that they be the first author. The paper should involve some aspect of Statistical Computing, which might be original methodological research, a novel application, or a software-related project. Each year, the winners are invited to present their papers at a special contributed session at the Joint Statistical Meetings, and the Section gives them a grant towards their attendance expenses. The competition is open to applicants who are students in the fall of the year prior to the competition.
Please find the submission details at http://asa.stat.uconn.edu.
Frequently Asked Questions
Exactly who is eligible for the competition?
Anyone who is a student (graduate or undergraduate) at a university. We do ask for a faculty member (usually the student's mentor or advisor) to certify student status in the fall term before the competition. In the past, all of the entrants have been graduate students, but undergraduates could make substantial contributions in a new field like statistical computing. We do ask for a CV as well so that the committee judging the entries can be calibrated about an entrant's educational level.
One correspondent asked if high school students could enter. We don't prohibit this, but realistically high school students might be at a disadvantage against graduate students close to finishing their PhD.
How am I supposed to fit my manuscript into just 6 pages?
We impose a six-page limit to (a) force students to think carefully about their main ideas and express them concisely, and (b) out of consideration for our judges, who are volunteering their time to help out with this competition.
If you are struggling to meet this limit, we offer two suggestions. First, consider a two-column format, as in this template. This is often a good way to save space while preserving readability. Second, you may include supplemental results, such as proofs or figures depicting additional simulations or examples, in an Appendix that starts on page 7. The first six pages, however, should be a self-contained paper that the judges can read and understand without having to consult the Appendix.
It is *not* recommended, however, to squeeze a much longer paper into six pages by shrinking the font, margins, and space between lines. This greatly limits the readability of your paper and tends to irritate the judges.
What does the letter from the faculty member need to contain?
- A certification of your student status in the fall term before the competition.
- If the paper is jointly authored, an indication as to how much is your work.
The review panel of the Student Paper Award consisted of Linglong Kong (Section on Statistical Computing), Israel Almodovar (Section on Statistical Computing), Inyoung Kim (Section on Statistical Graphics), Kiegan Rice (Section on Statistical Graphics), Raymond Wong (both sections; chair of the review panel). The four 2022 Student Paper Awards go to:
Mengyu Li, Renmin University of China, "Core-elements for Least Squares Estimation";
Emily A. Robinson, University of Nebraska, Lincoln, "Eye Fitting Straight Lines in the Modern Era";
Abhishek Shetty, University of California, Berkeley, "Distribution Compression in Near-linear Time";
Xinkai Zhou, University of California, Los Angeles, "Bag of Little Bootstraps for Massive and Distributed Longitudinal Data".
The review panel of the Student Paper Award consisted of Israel Almodovar (Section on Statistical Computing), Lucy D'Agostino McGowan (Section on Statistical Computing), Inyoung Kim (Section on Statistical Graphics), Earo Wang (Section on Statistical Graphics), Raymond Wong (both sections; chair of the review panel). The four 2021 Student Paper Awards go to:
- Gaurav Agarwal, King Abdullah University of Science and Technology, "Flexible Quantile Contour Estimation for Multivariate Functional Data: Beyond Convexity";
- Jiaxin Hu, University of Wisconsin-Madison, "Supervised Tensor Decomposition with Interactive Side Information";
- Nathan Osborne, Rice University, "Latent Network Estimation and Variable Selection for Compositional Data via Variational EM";
- Yifan Zhu, Iowa State University, "Three-dimensional Radial Visualization of High-dimensional Datasets with Mixed Features".
The student award recipients will present their work in a topic-contributed session of the 2021 Joint Statistical Meetings. They will also receive their certificate and cash prize at the mixer of the Section on Statistical Computing and the Section on Statistical Graphics.
The review panel of the Student Paper Award consisted of Xiaoyue Zoe Cheng, Jay Emerson, Lucy D'Agostino McGowan, Raymond Wong, and Jun Yan (chair). The four 2020 Student Paper Awards go to:
The student award recipients will present their work in a topic-contributed session; they will also receive their certificate and cash prize at the mixer of the Section on Statistical Computing and the Section on Statistical Graphics at the 2020 JSM in Philadelphia.
- "A Matrix-Free Method for Exploratory Factor Analysis of High-Dimensional Gaussian Data," by Fan Dai, Iowa State University
- "A Grammar of Graphics Framework for Generalized Parallel Coordinate Plots," by Yawei Ge, Iowa State University
- "Moving-Resting Process with Measurement Error in Animal Movement Modeling, " by Chaoran Hu, University of Connecticut
- "End-to-end Statistical Learning, with or without Labels," by Corinne Jones, University of Washington
2019The review panel of the Student Paper Award consisted of Xiaoyue Zoe Cheng, Yixuan Qiu, Jon Steingrimsson, and Raymond Wong (chair). The four 2019 Student Paper Awards go to:
The student winners will present their work in a topic-contributed session; they will also receive their certificate and cash prize at the mixer of the Section on Statistical Computing and the Section on Statistical Graphics at the 2019 JSM in Denver.
- "Computing High-Dimensional Normal and Student-t Probabilities with Tile-Low-Rank Quasi-Monte Carlo and Block Reordering" by Jian Cao, King Abdullah University of Science and Technology;
- "Online Decentralized Leverage Score Sampling for Streaming Multidimensional Time Series", by Rui Xie, Department of Statistics, University of Georgia;
- "Plotting Likelihood-Ratio Based Confidence Regions for Two-Parameter Univariate Probability Models with the R conf Package", by Christopher Weld, Department of Applied Science, College of William & Mary;
- "Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial data", by Daniel Zilber, Department of Statistics, Texas AM University.
Congratulations to all the award recipients for their impressive work!
This year we had a great competition with 25 submissions. The committee selected four winners and one honorable mention. Thank you to our judges for all their hard work in reading and evaluating these papers: Heike Hofmann, Daniel Sussman, Raymond Wong, and Hao Helen Zhang (chair). The four winners are:
- "BRISC: Bootstrap for rapid inference on spatial covariances", by Arkajyoti Saha (Department of Biostatistics, Johns Hopkins University)
- "MM algorithms for variance component models", by Liuyi Hu (Department of Statistics, North Carolina State University).
- "An asympirical smoothing parameters selection approach for SS-ANOVA models in large samples", by Xiaoxiao Sun (Department of Statistics, University of Georgia)
- "Calendar-based graphics for visualizing people's daily schedules", by Earo Wang (Department of Econometrics and Business Statistics, Monash University)
The honorable mention is
- "Dependency diagnostic: visually understanding pairwise variable relations", by Kevin Lin (Department of Statistics, Carnegie Mellon University)
We had a great competition this year with 27 submissions. Thank you to our judges for all their hard work in reading and evaluating these papers: Robert Gramacy, Kenneth Shirley, Kate Cowles, and Deepayan Sarkar. This year’s winners are:
- “acc: An R package to process, visualize, and analyze accelerometer data”, by Jae Joon Song (University of Texas)
- “The biglasso Package: A Memory- and Computation-Efficient Solver For Lasso Model Fitting With Big Data in R”, by Yaohui Zeng (University of Iowa)
- “Scalable Bayesian Learning for Sparse Logistic Models”, by Xichen Huang (University of Illinois)
- “The Self-Multiset Sampler”, by Weihong Huang (University of Illinois)
We had a great competition this year with 27 submissions. Thank you to our judges for all their hard work in reading and evaluating these papers: Tim Hesterberg, Di Cook, and Grant Brown. This year's winners are:
- "A fully Bayesian strategy for high-dimensional hierarchical modeling using massively parallel computing", by Will Landau (Iowa State University)
- "The picasso Package for Nonconvex Regularized M-estimation in High Dimensions in R", by Xingguo Li (University of Minnesota)
- "Nonparametric Signal Procession of Space-Time Trajectory Data: Algorithm for Eye Movement Pattern Recognition", by Shinjini Nandi (Temple University)
- "Using the geomnet Package: Visualizing African Slave Trade, 1514 - 1866", by Samantha Tyner (Iowa State University)
We had a great competition this year with 21 submissions. We would like to acknowledge our judges for their hard work in evaluating these submissions: Hadley Wickham, Jonathan Lane, and Mario Morales. We had two winners from the Graphics Section and two from the Computing Section, as listed below. This year's winners are:
- Computing: Ben Courtney Stevenson (University of St Andrews, United Kingdom) - An R package for the estimation of animal density from a fixed array of remote detectors
- Computing: Kaylea Haynes (Lancaster University, Lancaster) - Efficient penalty search for multiple changepoint detection in Big data
- Graphics: Lindsay Rutter (Iowa State University) - phyViz: Phylogenetic visualization of genealogical information in R
- Graphics: Eric Hare and Andrea J. Kaplan (Iowa State University) - Introducing statistics with intRo
We had a great competition this year with 22 submissions, 19 in the Computing Section and 3 for the Graphics Section. We would like to acknowledge our judges for their hard work in evaluating these submissions: John Castelloe, Erik Iverson, and Heike Hofmann. We had one winner from the Graphics Section and three winners from the Computing Section, as listed below:
This year's winners are:
- Computing: Gina Grünhage (Technische Universität Berlin) - Visualizing the Effects of a Changing Distance Using Continuous Embeddings
- Computing: Geoffrey Thompson (Iowa State University) - An Adaptive Method for Lossy Compression of Big Images
- Computing: Guan Yu (University of North Carolina at Chapel Hill) - Sparse Regression Incorporating Graphical Structure Among Predictors
- Graphics: Susan Vanderplas (Iowa State University) - The Curse of Three Dimensions: Why Your Brain Is Lying to You
The number of submissions this year is better than last year, a total of 28 submissions (18 from last year and only 10 before that). The judges were John Castelloe, Erik Iverson and Michael Lawrence. Many thanks for their efforts to review and rank all the papers so quickly.
This year's winners are:
- Abbass Sharif, (Utah State University) "Multivariate Visual Data Mining Tools for Functional Actigraphy Data"
- Adam Loy, (Iowa State University), "Are you Normal, The problem of confounded residual structures in hierarchical models"
- Nathaniel Helwig, (University of Illinois at Urbana-Champaign), "Fast and stable multiple smoothing parameter selection in smoothing spline analysis of variance models with large samples"
- Xinxin Shu, (University of Illinois at Urbana-Champaign), "Time-varying networks estimation and dynamic model selection"
The number of submissions this year is better than last year, a total of 18 submissions (10 from last year). The four judges were John Castelloe, Erik Iverson, Mark Greenwood and Michael Lawrence. Many thanks for their efforts to review and rank all the papers so quickly.
This year's winners are:
- Graphics: Niladri Roy Chowdhury, (Iowa State University), "Where's Waldo: Looking closely at a Lineup"
- Computing: Yunzhi Lin, (University of Wisconsin), Lasso Tree for Cancer Stage Grouping with Survival Data
- Computing: Karl Pazdernik, (Iowa State University), Efficient Maximum Likelihood Estimation for Fixed Rank Kriging
- Computing: Jingfei Zhang, (University of Illinois at Urbana-Champaign), Sampling for Conditional Inference on Network Data
- Graphics: Mahbubul Majumder (advisors Heike Hofmann and Dianne Cook, Department of Statistics, Iowa State University) "Visual Statistical Inference for Regression Parameters"
- Computing: Wonyul Lee (advisor Yufeng Liu, University of North Carolina at Chapel Hill) "Simultaneous Multiple Response Regression and Inverse Covariance Matrix Estimation via Penalized Gaussian Maximum Likelihood"
- Computing: Yunpeng Zhao (advisors Elizaveta Levina and Ji Zhu, Department of Statistics, University of Michigan) "Community extraction for social networks"
This year a large number of excellent entries were received, from which the selection committee has chosen four winners (in alphabetical order):
- Han Liu (advisors John Lafferty and Larry Wasserman, Statistics and Machine Learning Program, Carnegie Mellon University)
"Multivariate Dyadic Regression Trees for Sparse Learning Problems"
- Hua Ouyang (advisor Alexander Gray, College of Computing, Georgia Institute of Technology)
"Fast Stochastic Frank-Wolfe Algorithms for Nonlinear SVMs"
- Ali Shojaie (advisor George Michailidis, Department of Statistics, University of Michigan)
"Discovering Graphical Granger Causality Using the Truncating Lasso Penalty"
- Ying Sun (advisors Jeff Hart and Marc G. Genton, Department of Statistics, Texas A&M University)
"Functional Boxplots for Complex Data Visualization"
The students will be recognized at the Statistical Computing/Statistical Graphics business meeting at JSM 2010. Congratulations to the winners and many thanks to the judges for their hard work in making this year's competition a success!
This year a large number of excellent entries were received, from which the selection committee selected five winners (in alphabetical order):
- Bjorn Bornkamp (advisors Jose Pinheiro and Katja Ickstadt)
"MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies"
- Wei-Chen Chen (advisor Karin Dorman)
"Twisted Sisters: Disentangling Selection in Overlapping Reading Frames"
- Jian Guo (advisors Elizaveta Levina, George Michailidis and Ji Zhu)
"Pairwise Variable Selection for High-dimensional Model-based Clustering"
- Ruth Hummel (advisor David Hunter)
"A Steplength Algorithm for Fitting ERGMs"
- Mihee Lee (advisors J.S. Marron and Haipeng Shen)
"Penalized Sieve Deconvolution Estimation of Mixture Distributions with Boundary Effects"
We are pleased to announce the four winners of this year's Student Paper Competition. There were a total of 18 submissions and the four judges of this year's competition, Juana Sanchez, Linda Pickle, Jane Harvill and Peter Craigmile did an outstanding job of reviewing and ranking all papers in a very short period. Many thanks to them for their efforts and patience.
This year's winners are:
- Ming-Hung Kao (advisor John Stufken)
Multi-objective Optimal Experimental Designs for Event-Related fMRI Studies
- Ernest Kwan (advisor Michael Friendly)
Tableplot: A New Display for Factor Analysis
- Adam Rothman (advisor Liza Levina and Ji Zhu)
Sparse Permutation Invariant Covariance Estimation
- Michael Wu (advisor Xihong Lin)
Two-Group Classification Using Sparse Linear Discriminant Analysis
The students will be recognized at the Statistical Computing/Statistical Graphics business meeting at JSM 2009. Congratulations to the winners and many thanks to the judges for their hard work in making this year's competition a success!
- Andrew Finley (advisors Sudipto Banerjee and Alan R. Ek),
spBayes: An R Package for Univariate and Multivariate Hierarchical Point-referenced Spatial Models
- Alexander Pearson (advisor Derick R. Peterson),
A Flexible Model Selection Algorithm for the Cox Model with High-Dimensional Data
- Sijian Wang (advisor Ji Zhu),
Improved Centroids Estimation for the Nearest Shrunken Centroid Classifier
- Hadley Wickham (advisors Di Cook and Heike Hofmann),
Exploratory Model Analysis
- Youjuan Li, University of Michigan-Ann Arbor (advisor: Ji Zhu)
Efficient Computation and Variable Selection for the L1-norm Quantile Regression
- Fan Lu, University of Wisconsin-Madison (advisor: Grace Wahba)
Kernel Regularization and Dimension Reduction
- Rebecca Nugent, University of Washington-Seattle (advisor: Werner Stuetzle)
Clustering with Confidence
- Philip Reiss, Columbia University (advisor: Todd Ogden)
An Algorithm for Regression of Scalars on Images
- Guangzhe Fan, University of Alabama
Regression Tree Analysis using TARGET
- Feng Gao, Emory University
Estimation of Baseline Hazard with Time-Dependent Covariates
- Alexander Gray, Carnegie Mellon
Very Fast Multivariate Kernel Density Estimation via Computational Geometry
- Yufeng Liu, The Ohio State University
Multicateogry Support Vector Machine and Psi-Learning
- Subharup Guha, Ohio State
Benchmark Estimation for Markov Chain Monte Carlo Samples
- Roger Peng, UCLA
Estimating the Renewal Distribution of a Spatial-Temporal Process
- Ronny Vallejos, University of Connecticut
A Recursive Algorithm to Restore Images Based on Robust Estimation of NSHP Autoregressive Models
- Ji Zhu, Stanford University
Kernel Logistic Regression and the Import Vector Machine
- Roberto Gonzalez, York University, U.K.
A Panel Data Simultaneous Equation Model with a Dependent Categorical Variable and Selectivity
- Satoshi Miyata, Ohio State University
Adaptive Freeknot Splines
- Rituparna Sen, University of Chicago
Predicting a Web User's Next Access Based on Log Data
- Mu Zhu, Stanford University
Feature Extraction for Non-parametric Discriminant Analysis
- Heike Hofmann, University of Augsburg
Generalized Odds Ratios for Visual Modeling
- Stijn Vansteelandt, University of Ghent
The Imputation towards Directional Extremes (IDE) Algorithm for Analyzing Sensitiveity to Incomplete Outcomes
(with E. Goetghebeur)
- Iain Pardoe, University of Minnesota
A Bayesian Sampling Approach to Regression Model Checking
- Peter Karcher, University of California-Santa Barbara
Generalized Nonparametric Mixed Effects Models
(with Yuedong Wang)
- Alexandre Bureau, Dept of Biostatistics, University of California, Berkeley
An S-PLUS Implementation of Hidden Markov Models in Continuous Time
(with James P. Hughes and Stephen Shiboski)
- Ilya Gluhohvsky, Dept. of Statistics, Stanford University
Image Restoration Using Modifications of Simulated Annealing
- Peter D. Hoff, Dept of Statistics, University of Wisconsin-Madison
Nonparametric Maximum Likelihood Estimation Via Mixtures
- Muhammad Jalaluddin, Dept of Statistics, University of Wisconsin-Madison
An Algorithm for Robust Inference for the Cox Model with Frailties
(with Michael R. Kosorok)
- Alessandra Brazzale, Department of Mathematics, Swiss Federal Institute of Technology
Approximate Conditional Inference in Logistic and Loglinear Models
- Matt Calder, Department of Statistics, Colorado State University
Scompile: A Compiler for SPLUS
- Steven Scott, Department of Statistics, Harvard University
Bayesian Analysis of a Two State Markov Modulated Poisson Process
- Yan Yu, Statistics Center, Cornell University
Fitting Trees to Curve Data: An Application to Time of Day Patterns of International Calls
(with Diane Lambert)
- Wenjiang J. Fu, University of Toronto
Penalized Regressions: the Bridge versus the Lasso
- Alan Gous, Stanford University
Adaptive Estimation of Distributions using Exponential Sub-Families
- Gareth James, Stanford University
The Error Coding Method and PaCT's
- Ramani S. Pilla, Pennsylvania State University
New Cyclic Data Augmentation Approaches for Accelerating EM in Mixture Problems
- Dmitrii Danilov, St. Petersburg State University
Principal Components in Time Series Forecasting
- Ranjan Maitra, University of Washington
Estimating Precision in Functional Images
- Bob Mau, University of Wisconsin, Madison
Bayesian Phylogenetic Inference via Markov Chain Monte Carlo Methods
(with Michael Newton)
- Chris Volinsky, University of Washington
Applying Bayesian Model Averaging to Cox Models
(with David Madigan, Adrian Raftery and Richard Kronmal)
- Sudeshna Adak, Stanford University
Tree based Adaptive Estimation of Time-dependent Spectra for Nonstationary Processes
- John Gavin, University of Bath
Subpixel Reconstruction in Image Analysis
(with Christopher Jennison)
- William Lu, University of California, Berkeley
The Expectation-Smoothing Approach for Indirect Curve Estimation
- Yingnian Wu, Harvard University
Random Shuffling a New Approach to Match Making