November 2023 AI, Machine Learning & Data Science Meetup

Zoom Link
https://voxel51.com/computer-vision-events/november-ai-machine-learning-data-science-meetup/

Foundation Models for Electronic Health Records (EHRs)
Hospitals generate an average of 50 petabytes of data per year. Unfortunately, almost none of this data is used to improve patient care, and it shows; despite spending over $4 trillion on healthcare annually, the US ranks dead last in health outcomes among high-income countries. Foundation models — large-scale AI models trained on large amounts of unlabeled data — offer a promising approach for harnessing this data to improve the efficacy of our healthcare system. In this talk, I will motivate the need for developing foundation models for electronic health records (EHRs), highlight some initial work in the space, and outline key unsolved challenges and opportunities for ML researchers hoping to make a meaningful impact with their work.

Speaker: Michael Wornow is a computer science PhD student at Stanford University advised by Nigam Shah and Chris Re. He is supported by an NSF Graduate Research Fellowship and Stanford HAI Graduate Fellowship, and his research focus is on developing and operationalizing foundation models for health systems.

Exploring the Two Headed Classifier Use Case
Let’s examine some practical applications of computer vision tasks. Although the classic classification problem may appear straightforward at first, in the real-world we’ll likely encounter numerous constraints, such as the model’s speed, size, and its ability to operate on mobile devices. Additionally, multiple tasks may need to be performed, and it may not always be advisable to employ a separate model for each task. Whenever possible, it is preferable to optimize the system’s architecture and employ fewer models, while still maintaining accuracy. Therefore, when considering all of these constraints and optimizations, the task suddenly becomes more complex. In this talk we’ll work through an example classification problem with several classes that may not appear visually similar. We’ll see how a two-headed model can assist us in this challenge.

Speaker: Argo Saakyan is a computer vision engineer with 5+ years of experience in data science. Argo has deployed numerous models to production and worked with classification/detection/segmentation tasks in both research and optimized deployment sides.

Using AI to Test Software, Techniques and Tools
AI has innovated the way lot of people work. One of these ways is software testing. From unit testing, integration testing, and end to end testing, AI can help developers test better throughout the testing pyramid. During this session we will dive into a variety of methods and tools developers can use AI to help them test better.

Speaker: Justin Trugman is the VP of Software Development at Caregility, leading the engineering teams developing a revolutionary telehealth solution for a customer base of over 1000 hospitals around the globe. Before Caregility, he was at Loon, an Alphabet subsidiary. Justin is actively involved in the Hoboken & New York City startup community where he actively mentors & judges at hackathons