Events

In this workshop, we will discuss probabilistic programming in Python. We'll first go over the basics of and motivations behind probabilistic programming. After this brief review, we will learn how to use Python probabilistic programming libraries to build example statistical models and inference pipelines.

Pre-requisites: Attendees should be familiar with Python and Bayesian data analysis.

Presented by David Wright.

This workshop is targeted at a broad audience of researchers who want to learn how to be more efficient and effective in their data analysis and computing, whatever their career stage.

The lesson is inspired by and based on the paper, Good Enough Practices in Scientific Computing (Wilson et al., 2017): “a set of good computing practices that every researcher can adopt, regardless of their current level of computational skill”.

These practices encompass data management, programming, collaborating with colleagues, organizing projects, tracking work, and writing manuscripts.

Prerequisites: There are no specific prerequisites. Learners should be participating or intending to participate in scientific research that involves computing. A basic knowledge of a coding language will help with the section on code & software.

Presented by Nandan Tandon, Ph.D.

In this hands-on workshop, participants will dive into the world of prompt engineering—a crucial skill for effectively interacting with AI models like ChatGPT. Tailored for beginners to intermediates, this session will explore the art and science of crafting prompts that yield accurate, creative, and useful outputs. By the end of the workshop, attendees will have a solid understanding of prompt structuring techniques, practical applications, and best practices to harness AI's full potential in various contexts.

Presented by Vivek Sabbani

Published computational research should be reproducible today, tomorrow, and ten years from now. In this workshop, we will go over tools and practices you can employ to ensure that this is the case. Topics will include containerization, bit-for-bit reproduction of software environments, and software design practices for reproducible pipelines.

Presented by David Wright.

Discover How Google Generative AI Can:

• Accelerate Discovery: Uncover patterns and correlations in your data that would take years to find manually.
• Scale Innovation: Generate novel hypotheses, design innovative experiments, and explore new research avenues.
• Enhance Collaboration: Streamline literature reviews, co-author papers, and communicate findings more effectively.
• Secure Funding: Craft compelling grant proposals and showcase the impact of your research with data-driven visualizations.

Session Will Cover The Following Topics:

• Introduction to Google Cloud
  • 10x Thinking with Google! Google Culture Crash Course
  • Alphabet, Inc. Key Advantages, Research and Tools Available
  • The Types of Solutions Google Provides to R1 Research Institutions 

• Open Discussion
  • Tell Google Your Superpower!
  • Let’s Focus on You! Skill Sets, Experience, and Strategic Imperatives 

• Lab One (1): Google BigQuery ML democratizes ML by letting users create, train, evaluate, and predict with ML models using existing SQL tools and skills.
  • Overview of Google’s Data Dominance. Native Data Catalog in BigQuery, Data Assets and Models such as Alphafold from Deepmind, more.
  • You will learn what makes a good model by cleaning, governing, and extracting value from Enterprise Data to build trustworthy AI.

• Lab Two (2): Google's Vertex AI Platform
  • AutoML: train tabular, image, text, or video data without code
  • Governance and Interoperability: Control over the end to end training process, use your preferred ML framework, write your own training code, and choose your own hyperparameters
  • Google Cloud Model Garden: Discover, test, customize, and deploy Google LLM's, APIs, and select open-source (OSS) models and assets.
  • Generative AI: access to 150+ models from multiple companies and across multiple modalities (text, code, images, speech). Tune Google's LLMs to meet your needs, and deploy AI-apps

• Solutions Showcase:
  • Art of the Possible Demos from Google!

Challenges and Considerations:

While the potential of generative AI in research is immense, it's important to be aware of ethical considerations, potential biases in data, and the need for human oversight to ensure the responsible and effective use of these powerful tools.

Who Should Attend:

Researchers, scientists, data analysts, and anyone interested in leveraging cutting-edge AI to advance their work. 

Limited Seats Available!

Don't miss this opportunity to learn from Google experts, network with fellow researchers, and see firsthand how generative AI is revolutionizing research across disciplines.

Presented by Pratap Ramamurthy