Kazuhiro Terao (SLAC, USA)
I am an experimental neutrino physicist at SLAC National Accelerator Laboratory working on Short Baseline Neutrino program and DUNE in the U.S.. My current research focus is R&D of a machine learning based data reconstruction and analysis techniques for high resolution particle imaging neutrino detectors in order to maximize physics output and accelerate discoveries. I collaborate with scientists and industrial researchers across the fields of science for pushing the state of the art of machine learning applications in science.
Luc Hendriks (Radboud University, Netherlands)
I use machine learning techniques to tackle dark matter related problems. My main contributions are in machine learning applied to gamma-ray astronomy, asteroseismology, high energy physics and black hole physics. This includes parameter estimation using (convolutional) neural networks, event generation using variational autoencoders, Bayesian deep learning and genetic algorithms for finding optima in high dimensional parameter spaces. Apart from my research I also cofounded a company in machine learning applied to personal photos to create photo books. (Website)
François Lanusse (CNRS, France)
François is a CNRS researcher at CEA Saclay and member of the LSST Dark Energy Science Collaboration (DESC). Most of his research research has been focused on measuring and exploiting the gravitational lensing effect with the development of novel tools and methodologies based on sparse signal representations, convex optimization, and deep learning. François holds a PhD in astrophysics from Paris University, which he followed by two postdocs at Carnegie Mellon University and UC Berkeley.
Shiro Ikeda (ISM, Japan)
Shiro Ikeda received his Ph.D. in engineering from the University of Tokyo in 1996. After 5 years of postdoc at RIKEN, he joined Kyushu Institute of Technology as an associate professor and moved to the Institute of Statistical Mathematics in 2003. where he is currently serving as a professor. His interests cover but not restricted to statistics, signal processing, information geometry, and machine learning. He recently works for multiple projects of astronomy and astrophysics. Since 2013 he has been in the Event Horizon Telescope collaboration which has received the National Science Foundation Diamond Achievement Award, 2020 Breakthrough Prize in Fundamental Physics, and Smithsonian 2019 American Ingenuity Award in Physical Sciences.
Yuta Nakashima (IDS, Osaka University, Japan)
Yuta Nakashima is an associate professor, working on computer vision, pattern recognition, and natural language processing, at Institute for Datability Science, Osaka University, Japan. Currently he is interested in how knowledge helps visual question answering system and how knowledge is represented. Asides from his personal research interests, he is also enjoying interdisciplinary research projects hosted by Institute for Datability Science, collaborating with researchers in various fields incluging medicine, pharmacy, literature, physics, etc. (Website)
Francois Drielsma (SLAC, USA)
I am an experimental high energy physicist who wishes to modernize the field’s paradigm for data analysis by leveraging state-of-the-art reconstruction techniques. My early experience analysing the phase-space density of non-linear muon beams first brought me to explore novel nonparametric density estimation techniques. I am now postdoctoral scholar at the SLAC National Accelerator Laboratory with a specific focus on developping machine learning techniques to tackle the significant challenge of reconstructing Liquid Argon Time Projection Chamber data in SBN and DUNE.
Aashwin Ananda Mishra (SLAC, USA)
Aashwin Mishra is a Project Scientist at the Machine Learning Initiative at the SLAC National Laboratory. His research interests include uncertainty quantification for deep learning, integration of physics-based constraints in machine learning, anomaly and change point detection, and adversarial learning. He has developed and taught courses in statistical learning and high performance computing at Stanford University, Texas A&M University, the University of Texas at Austin, SLAC, etc.