2021-2024 IAIFI Fellows
Research Interests: Anna Golubeva’s main research focus is on developing a theory of deep learning using approaches from theoretical physics. Her goal is to contribute towards understanding the tools of AI and leveraging them to advance both AI and the physical sciences. Her projects include both the application of deep learning methods for quantum many-body problems, as well as a theory-based analysis of deep learning systems, exploiting approaches from information theory, statistical learning theory, and statistical physics. She is currently particularly interested in the subject of sparsity in neural networks.
Biographical Sketch: Anna Golubeva obtained her PhD in 2021 at the Perimeter Institute for Theoretical Physics and the University of Waterloo, where she was advised by Roger Melko. During her PhD, she was also a graduate affiliate at the Vector Institute for AI in Toronto. Previously, she completed the Perimeter Scholars International master’s program (2017), a MSc in Theoretical Physics with focus on computational approaches to quantum many-body systems (2016), and a BSc in Biophysics (2014) at the Goethe University in Frankfurt, Germany.
Research Interests: Di Luo is working on research in the intersection of quantum many-body physics, quantum information science, and artificial intelligence. He is interested in understanding nature from the perspectives of information and computation as well as developing intelligence inspired by ideas from physics. Di Luo has been developing machine learning algorithms and quantum algorithms for condensed matter physics, high energy physics, and quantum information science.
Biographical Sketch: Di Luo received his undergraduate degree with majors in physics and mathematics from the University of Hong Kong in 2016 and pursued his Ph.D. in physics at the University of Illinois, Urbana-Champaign. Di Luo will become an IAIFI Fellow at the NSF AI Institute for Artificial Intelligence and Fundamental Interactions in 2021.
Research Interests: Siddharth Mishra-Sharma is interested in developing novel statistical methods for accelerating the discovery of new physics in astrophysical and cosmological observations at all accessible scales. He is especially focused on developing analysis techniques based on machine learning that enable new ways of searching for signatures of physics beyond the Standard Model, in particular the nature of dark matter, using data from ongoing and upcoming cosmological surveys. Towards this end, Siddharth is interested in incorporating physical insights, symmetries, and laws into machine learning algorithms as well as extracting physical insights, symmetries, and laws using machine learning algorithms from noisy physics datasets.
Biographical Sketch: Prior to joining the NSF AI Institute for Artificial Intelligence and Fundamental Interactions as an IAIFI Fellow, Siddharth Mishra-Sharma was a postdoctoral associate at the Center for Cosmology and Particle Physics at New York University from 2018-2021. He received his Ph.D. in Physics from Princeton University in 2018. He read Natural Sciences and Mathematics at the University of Cambridge, where he was a member of Peterhouse, receiving his undergraduate degree in 2013. Siddharth grew up in Moscow, Russia.
Research Interests: Ge Yang’s research involves two sets of related problems. The first is to make distributed representation in a neural net generalizable by finding ways to automatically discover causal structures that are equivariant to symmetry transformations. The second is to solve key open problems in deep reinforcement learning, including exploration and ways to learn off-line from static datasets, using search and planning.
Biographical Sketch: Ge Yang grew up on the north side of Beijing, graduated in 2010 with his undergraduate degree in Physics and Mathematics from Yale, and received his Ph.D. in Physics from the University of Chicago. He visited UC Berkeley to work with Pieter Abbeel in 2018, followed by a research internship at Facebook AI Research with Roberto Calandra, and one at Google DeepMind in London with Volodomyr Mnih. He is currently a postdoctoral fellow at the NSF AI Institute for Artificial Intelligence and Fundamental Interactions.