Scientific Motivation
My long-term goal is to establish a quantitative thermodynamic description of strongly interacting matter by combining event-by-event fluctuations, particle correlations, electromagnetic probes, and intelligent inference methods.
One of the central challenges of modern nuclear physics is understanding how the fundamental theory of strong interactions gives rise to the rich phase structure of strongly interacting matter.
Heavy-ion collisions provide unique access to these phases under extreme conditions of temperature and density. However, the relevant thermodynamic properties cannot be observed directly. They must be reconstructed from fluctuations, correlations, and rare electromagnetic probes emerging from finite, rapidly evolving systems.
Program structure
Research Pillars
I. Event-by-Event Thermodynamics
Fluctuations of conserved charges provide direct access to thermodynamic susceptibilities of QCD matter. Building upon the Identity Method and its extensions, this program will develop fluctuation observables capable of extracting thermodynamic information with unprecedented precision.
II. Intelligent Inference for Nuclear Physics
A central objective is the development of hybrid frameworks that combine Fuzzy Logic, probabilistic inference, machine learning, and explainable AI. These approaches preserve physical interpretability while enabling efficient extraction of thermodynamic information from increasingly complex experimental datasets.
III. Electromagnetic Tomography of QCD Matter
Dileptons and photons escape the medium without strong interactions and therefore provide direct information about its space-time evolution.
IV. Quantitative Discovery of the QCD Critical Point
Rather than relying on isolated observables, the objective is a global framework combining fluctuations, correlations, identified-particle measurements, electromagnetic probes, and theoretical constraints to establish quantitative criteria for discovery.
Theory–experiment bridge
Connecting Experiment and First-Principles QCD
A central objective of this research program is the establishment of quantitative links between experimental observables and the fundamental properties of strongly interacting matter predicted by Quantum Chromodynamics.
Fluctuations of conserved charges, particle correlations, and electromagnetic probes provide unique access to thermodynamic susceptibilities, transport coefficients, and the equation of state of QCD matter.
By combining precision measurements with modern theoretical approaches, including lattice QCD, effective field theories, statistical models, conservation-law baselines, and dynamical descriptions, I aim to transform relativistic heavy-ion collisions into quantitative laboratories for studying the phases of strongly interacting matter.
The ultimate goal is to reconstruct the thermodynamic properties of QCD matter directly from experimental data and establish robust criteria for identifying critical phenomena and phase transitions.
Experimental landscape
A program anchored in major international facilities
The research program will be realized through a broad experimental portfolio spanning current and future heavy-ion facilities. Measurements at ALICE/CERN, HADES and CBM at GSI/FAIR, and complementary energy-scan programs will provide access to different regions of the QCD phase diagram and to complementary observables.
Long-term vision
Precision QCD Thermodynamics
The ultimate outcome of this program will be the establishment of a new discipline in which relativistic heavy-ion collisions become quantitative laboratories for studying the emergence of collective phenomena in strongly interacting matter.
By combining innovative observables, advanced statistical inference, and electromagnetic probes, this research will provide a comprehensive experimental framework for understanding the phases of QCD and the origin of visible matter in the Universe.
Why this matters
The questions addressed by this program lie at the intersection of nuclear physics, statistical mechanics, complex systems, and artificial intelligence. The methodologies developed within this research have the potential to influence a broad range of disciplines where information must be extracted from incomplete, noisy, and high-dimensional data.