Session B30: Data Science, AI and ML for Industry and Applied Physics

Enabling analytics at scale

Meta relies on data to manage our infrastructure, improve our products, and identify opportunities for growth. This talk will provide and overview of the internal tools, systems and approaches that we use to enable teams to perform analytics using our exabyte scale data warehouse. I'll also discuss what it's like to work in analytics at a company like Meta, and share how interested physicists can transition to a career in the field.   

Analytics at Caterpillar

Caterpillar is synonymous with heavy machinery, and for almost 100 years, our products have helped our customers build a better, more sustainable world. We have also been driving industry-leading connectivity solutions for over 20 years, introducing our first telematics device in 1990. Nowadays, Caterpillar’s growing digital team is supporting the company’s digital strategy through advanced telematics and data science solutions. With onboard computers, sensors, and cameras, more than 1.4 million assets are transmitting data to Caterpillar to enable advanced analytics at scale. This data can include time-series data, machine health alerts, fuel usage, GPS coordinates and operator-specific usage. In this presentation, we will describe how powered by this data, Caterpillar’s analytics can provide customers value at a lower cost of ownership, increased productivity and predictability, safety and reduced maintenance costs.   

Tough Decisions

Decision making can be cast as an optimization problem. Whether career moves from physicist to data scientist or choosing which products to commercialize, there are goals that drive those decisions. The focus of this talk is about AI decision models my team creates that make complex, customer-focused, product development decisions in plant breeding. I will also touch on some of the hard decisions I made in my career path from physics to data science.   

Applying a physics education to a career as a hedge fund data scientist

My talk will focus on how research in physics or another scientific discipline prepares you for a career in data science. I will discuss the skills required to become a successful data scientist, including those that can be learned on the job as well as several invaluable skills that are developed through scientific research. I will also present some examples of data science projects relevant to the hedge fund industry; you may find that you are more prepared for a career in data science than you think!