High-Energy Physics Phenomenology

We develop precision QCD predictions for collider observables across jet physics and resummation, small‑x dynamics within the CGC framework, and top‑quark phenomenology, leveraging soft‑collinear effective theory where appropriate. We emphasize systematically improvable calculations with quantified uncertainties, rigorous factorization, and interfaces to global analyses and forthcoming programs at the LHC and EIC.

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AI/ML for High-Energy Physics

We develop machine‑learning methods for theory calculations and inference, including neural emulators for evolution equations and differentiable pipelines for global fits. These tools accelerate high‑fidelity predictions and enable principled uncertainty propagation and sensitivity studies in collider phenomenology.

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Software & Tools

Our research utilizes various tools and frameworks in high-energy physics:

• QCD evolution: BK/JIMWLK, LHAPDF
• Monte Carlo: Pythia, Herwig, Sherpa, MadGraph
• Machine learning: TensorFlow, PyTorch, scikit-learn
• Symbolic computation: Mathematica, SymPy, Julia