High-Energy Theory Group, Institute of Physics
Research Field
My work focuses on the computational challenges of high-energy processes in astroparticle physics. During my PhD at CERN (Switzerland) and KIT (Germany), I specialized in event generators and extensive air shower simulations, which led to the development of Matrix-Cascade Equations (MCEq). This open-source software is widely used to calculate atmospheric neutrino fluxes for experiments such as IceCube, KM3NeT, and Super-/Hyper-K. Over time, my research at DESY Zeuthen (Germany) shifted toward Astroparticle Theory, focusing on modeling multi-messenger emissions from Ultra-High Energy Cosmic Ray (UHECR) sources and their transport through intergalactic space. I continued exploring UHECR phenomenology and atmospheric neutrinos at the Institute for Cosmic Ray Research (ICRR) as a JSPS fellow.
Since 2021, I have been an Assistant Research Fellow at the Institute of Physics, Academia Sinica. During this time, I rejoined the IceCube Observatory in Antarctica, where I study the impact of flux systematics on data analysis. In 2024, I also became part of the Telescope Array Collaboration—an UHECR observatory in Utah, USA—focusing on reconstructing UHECR mass composition with deep neural networks and applying other machine learning techniques to UHECR data.
The High-Energy Group at the Institute of Physics, Academia Sinica focuses on theoretical and computational research in particle physics, heavy-ion physics, cosmology, and astroparticle physics. The group has five principal investigators, around ten postdoctoral researchers from different countries, and several master’s and undergraduate students. We hold regular seminars and welcome many visitors from around the world each year.
The research topics in my group are:
- New atmospheric neutrino calculations using codes like MCEq, MUTE, and daemonflux
- Low-energy neutrino flux systematics for neutrino mass ordering
- Data analysis and reconstruction of UHECR events from the Telescope Array Observatory using Machine Learning
- Data analysis in the IceCube Neutrino Observatory
- Simulations and data analysis for the IceCube and IceCube-Gen2 Hot Water Drill located at the South Pole
- Development of a new event generator for elementary particle and nuclear interactions for cosmic ray and LHC applications
Gentner Scholarship (CERN), Humboldt Feodor-Lynen Scholarship (Germany), JSPS Fellow (Japan)
- Master in Electrical Engineering and Computer Science (RUB, Bochum, Germany)
- Master in Physics (RUB, Bochum, Germany)
- PhD in Physics (KIT, Germany)
Job Description
Possible projects include:
- Modeling of particle transport in the atmosphere using the open-source code MCEq
- Data Analysis of the Telescope Array Experiment's data using modern Bayesian methods
- Development of reconstruction algorithms for a novel underwater neutrino detector concept using traditional likelihood approaches and machine learning methods
- Experimental Machine Learning methods for the reconstruction of detector observables using modern data augmentation techniques and present-day architectures such as transformers or graphs
- Simulation of neutrino detectors and geometry optimization
- Modeling interactions of particles and nuclei, such as helping in developments of new cross section models and Monte Carlo event generation
Preferred Intern Educational Level
- At least 2nd year undergrad
- Physics, Electrical or Computer Engineering, Applied Physics
Skill sets or Qualities
- Computer experience and interest in a deeper understanding of computational work
- Comfortable working with mathematical context and computers
- Advantage but not required: courses completed on special relativity, numerical math, data science, particle physics, astrophysics