Biomedical data poses multiple hard challenges that break conventional machine learning assumptions. In this talk, I will highlight the need to transcend our prevalent machine learning paradigm and methods to enable them to become the driving force of new scientific discoveries. I will present machine learning methods that have the ability to bridge heterogeneity of individual biological...
The European Spallation Source (ESS) is multi-disciplinary research facility based on neutron scattering under construction in Lund. The facility includes a superconducting linear proton accelerator, a rotating tungsten target wheel where neutrons are spalled off by the high energy protons and a suit of instruments for neutron scattering experiments.
ESS is a user facility designed and...
Magnetic confinement fusion research is at a threshold where the next generation of experiments are designed to deliver burning fusion plasmas with net energy gain for the first time. ML holds great promise in reducing the costs and risks of fusion reactor development, by enabling efficient workflows for scenario optimization, reactor design, and controller design. This talk reviews various...
The exploration of extrasolar planets, which are planets orbiting stars other than our own, holds great potential for unravelling long-standing mysteries surrounding planet formation, habitability, and the emergence of life in our galaxy. By studying the atmospheres of these exoplanets, we gain valuable insights into their climates, chemical compositions, formation processes, and past...
Beyond the well-known highlights in computer vision and natural language, AI is steadily expanding into new application domains. This Pervasive AI trend requires supporting diverse and fast-moving application requirements, ranging from specialized I/O to fault tolerance and limited resources, all the while retaining high performance and low latency. Adaptive compute architectures such as AMD...
How fast should your machine learning be? ideally, as fast as you can stream data to it.
In this presentation I will discuss the role of computing infrastructure in machine learning, and argue that to face the growing volume of data and support latency constraints, the best place for inference is within the network. I will introduce in-network machine learning, the offloading of machine...
Large Language Models (LLMs) will completely transform the way we interact with computers, but in order to be successful they need to be fast and highly responsive. This represents a significant challenge due to the extremely high computational requirements of running LLMs. In this talk, we look at the technology behind LLMs, its challenges, and why Groq's AI accelerator chip holds a...
