Statistics & Data Science Research | Cornell Bowers

Utilizing AI and data analysis to fight cancer.

Telling the difference between benign and cancerous thyroid nodules before surgery is notoriously challenging, but a new study combining AI and data analysis techniques is yielding surprisingly accurate cancer predictions.

‘Structural poverty’ maps could steer help to world’s neediest.

Leveraging national surveys, big-data advances and machine learning, Cornell researchers have piloted a new approach to mapping poverty that could help policymakers identify the neediest people in poor countries and target resources more effectively.

A color photo showing an overpopulated village with huts for homes.

New method could help estimate wildlife disease spread.

A new method could be used by biologists to estimate the prevalence of disease in free-ranging wildlife and help determine how many samples are needed to detect a disease.

Research Excellence

Home to IMS Fellows and NSF CAREER recepients, Statistics and Data Science is widely recognized for research excellence.

7

IMS Fellows

5

NSF CAREER Awards

1

IMS Medallion

1

Sloan Fellowships

Statistics and Data Science Research Areas

Asymptotic Statistics

Asymptotic statistics studies the properties of statistical estimators, tests, and procedures as the sample size tends to infinity, and finds approximations that can be used in practical applications when the sample size is finite.

Bayesian Analysis

Bayesian statistics provides a mathematical data analysis framework for representing uncertainty and incorporating prior knowledge into statistical inference.

Causal Inference

The goal of causal inference is to develop a formal statistical framework that answers causal questions from the real world data.

Clinical Trials

Clinical trials are research studies designed to test the safety and effectiveness of medical treatments, drugs, or medical devices.

Econometrics

Econometrics applies statistical methods to analyze and model economic data. It provides a way to test economic theories and make predictions about economic events.

Empirical Processes

Providing a rigorous mathematical basis for central limit theorems, large deviation theory, weak convergence, and convergence rates — empirical process theory is widely used in many areas of statistics and has applications in fields such as machine learning, probability theory, and mathematical finance.

Functional Data Analysis

Functional data analysis is a branch of statistics that analyzes data providing information about curves, surfaces, or anything else varying over a continuum.

Graphical Models

A graphical model, or probabilistic graphical model, is a statistical model which can be represented by a graph. The vertices correspond to random variables, and edges between vertices indicate a conditional dependence relationship.

High-dimensional Statistics

In statistical theory, the field of high-dimensional statistics studies data whose dimension is larger than typically considered in classical multivariate analysis.

Machine Learning

A field of inquiry devoted to understanding and building methods that “learn”— that is, methods that leverage data to improve performance on some set of tasks.

Model Selection

Model selection is the task of selecting a statistical model from a set of candidate models with data. Instances include tuning parameter selection, feature selection in regression, and classification, pattern recovery, and nonparametric estimation.

Spatial Analysis or Spatial Statistics

The study of models, methods, and computational techniques for spatially-referenced data, with the goal of making predictions of and drawing inferences about spatial processes.

Statistical Genetics

Using techniques from statistics, computer science and bioinformatics, statistical geneticists help gain insight into the genetic basis of phenotypes and diseases.

Stochastic Processes

A stochastic process is a family of random variables, where each member is associated with an index from an index set. The type of variable is general, but common specifications are scalar, vector, matrix, or function valued random variables.

Statistical Optimal Transport in High Dimensions

The study of its high dimensional aspects is born from the need to address modern challenges in Statistical Optimal Transport, and involves developing new notions of optimal transport that avoid the curse of dimensionality and the computational burden associated with classical approaches.

Elevating research across disciplines.

man in colorful shirt holding a remote that controls a tablet on a platform

Student doing research

UNDERGRADUATE RESEARCH

BOWERS RESEARCH OFFICE