Synthetic controls are widely applied to estimate the effects of policy interventions and other treatments of interests. The DataX Workshop on synthetic control methods seeks to provide an introduction to synthetic control methods for non-experts as well as an opportunity for researchers working on synthetic control methods to communicate new…

Many scientific experiments generate large, multi-modal datasets, often in the form of time-series of different dimensionality. A particular challenge that scientists face in their workflows is comparing experiments to model and simulation, determining how close experiments match expected theory. The various analyses that scientists perform on…

Each year, our campus comes together at Princeton Research Day to celebrate the research and creative endeavors of early-career researchers and scholars.

Patrick Wendell ’11, co-founder and vice president of engineering at Databricks, will join Jen Rexford ’91, chair of Princeton’s Department of Computer Science, in a conversation about his entrepreneurial journey from computer science major at Princeton to co-founder of a pioneering company used by businesses around the world to manage and…

Research on machine learning (ML) algorithms, as well as on their ethical impacts, has focused largely on mathematical or computational questions. However, for algorithmic systems to be useful, reliable, and safe for human users, ML research must also wrangle with how users’ psychology and social context affect how they interact with algorithms…

This workshop will give an overview of several modern supervised and unsupervised machine learning methods. We will discuss the advantages and limitations of each and explore what types of problems each is best suited to address.

Topic covers:  Basics of Gaussian Process Regression to build predictive models and…

Learn how to train and build a ML model on SageMaker, then how to deploy the inference end points on tools like AWS Greengrass or Serverless applications.

Learn the basics of building serverless applications and microservices like AWS Lambda, AWS Step Functions, Amazon API Gateway, Amazon DynamoDB, Amazon Kinesis, and Amazon S3. You'll learn to build and deploy your own serverless application using these services for common use cases like web applications, analytics, and more.

Learn about different ways to run fully managed Jupyter Notebook, several types of serverless environments and specific workloads. 

Learn about core AWS services for compute, storage, database and networking. Hands-on lab to launch AWS virtual machines with storage buckets.