What survived the noise, written down. The full record keeps itself on ORCID and INSPIRE.
Probabilistic inference implemented directly on-chip at the sensor edge for charged-particle tracking — pushing AI/ML into the detector front-end under extreme latency and power constraints.
An end-to-end tool for hardware-aware compression of machine-learning models — structured pruning and quantization tuned for deployment under real-time, resource-constrained conditions.
Modeling quasi-periodic oscillations in accreting compact sources as a nonlinear hydrodynamical resonance, and using them to constrain the spin of the central compact object.
An asymptotically flat rotating black-hole vacuum solution in a modified theory of gravity, with implications for how deviations from general relativity could show up around spinning black holes.
On-Detector Machine Learning for Beam-Induced Background Rejection at a 10 TeV Muon Collider
arXiv preprint2026
Robustness of the smartpixels classifier for different simulated sensor geometries and non-ideal detector conditions
Nucl. Instrum. Meth. A 10922026
Performance measurements of the electromagnetic calorimeter and readout electronics system for the DarkQuest experiment
Nucl. Instrum. Meth. A 10802025
Sensor Co-design for smartpixels
arXiv preprint2025
Characterization of a 28 nm smartpixels ASIC With On-Chip ML for Particle Tracking Detectors
arXiv preprint2025