Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.
During this hands-on workshop, you will be introduced to parallel and GPU computing in MATLAB for speeding up your applications and offloading computations. By working through common scenarios and workflows, you will gain an understanding of the parallel constructs in MATLAB, their capabilities, and some of the issues that may arise when using them.
Making the transition from nuclear and particle physics to data science is a scary yet rewarding journey. In this talk, I’ll share my personal experiences of transitioning outside of academia and working as a data scientist in the private sector. I’ll describe some specific projects I’ve worked on and explain what technical and soft skills a physics background has that are uniquely suited for doing this work, and additionally what I had to learn after my PhD.
The pursuit of particle physics, or any kind of discovery-driven research, requires a stable and prosperous society. Today, our society is increasingly threatened by global climate change. Human-influenced climate change has already impacted weather patterns, and global warming will only increase unless deep reductions in emissions of CO2 and other greenhouse gases are achieved.
In this talk, I will present our recent proposal of searching for axion dark matter with an optomechanical cavity filled with a material such as superfluid helium. Axion absorption converts a pump laser photon to a photon plus a phonon. The axion absorption rate is enhanced by the high occupation number of coherent photons or phonons in the cavity, allowing our proposal to largely overcome the extremely small axion coupling. The axion mass probed is set by the relative frequency of the photon produced in the final state and the Stokes mode.
Abstract TBA
Host: Karsten Heeger
Abstract TBA
Host: Karsten Heeger
An introductory course on using Yale’s High Performance Computing (HPC) resources
Hands on workshop, March 8th-9th , 11am-5pm
This workshop will focus on topics including big data analytics and machine learning with Spark, and deep learning using Tensorflow.
One of the biggest questions in fundamental particle physics is whether neutrinos are Dirac fermions, with distinct anti-particles, or Majorana fermions, for which the particles and anti-particles are identical. The best available probe of the neutrino nature is neutrinoless double beta decay (0νββ), a hypothetical process that require massive Majorana neutrinos. This discovery of this lepton number violating process would therefore reveal the neutrino nature and provide a window into physics beyond the Standard Model.
