00
Introduction
Noise is not merely an obstacle. It is a clue about which mathematical language a system wants us to use.
Astronomical surveys observe the sky at irregular intervals. Quantum models encode information in complex amplitudes that cannot be read directly. Cryptographic systems must remain secure while their underlying algorithms change. In each case, the raw object is not yet the useful object.
My research asks how to transform these systems without erasing the signal that matters. The figures in this atlas are compact explanations of that process.
01
Finding rare supernovae in irregular light curves
A telescope does not hand us a smooth movie. It gives us scattered measurements, uneven in time, with uncertainty attached to every point.
Type Ibn supernovae are rare stellar explosions whose light curves can be confused with more common transients. The task is to turn sparse photometry into a representation that a classifier can compare across thousands of objects.
Interactive figure
From scattered observations to a candidate score
What to notice: the fast rise and narrower post-peak evolution remain visible after interpolation, giving the classifier a comparable shape representation.
Why the representation matters
A naive model can mistake missing observations for astrophysical behavior. A Gaussian process instead treats the light curve as a distribution over plausible continuous functions. From that distribution, we can compute features at consistent times and pass them to a tree-based classifier.
02
Rewinding quantum models to expose anomalies
An anomaly can be defined as a pattern that a model cannot cleanly undo.
Active galactic nuclei vary over time in ways that are noisy, stochastic, and astrophysically rich. Quantum variational rewinding asks a different question from ordinary classification: after learning a representation of typical behavior, how well can a parameterized quantum process reverse an encoded signal?
Interactive figure
Encode → evolve → rewind → score
Classical signal
Variational circuit
Interpretation
Consistent with learned dynamics
The circuit approximately returns the encoded state toward its reference, producing a low anomaly score.
The research question
The important question is not whether a quantum circuit can draw a complicated boundary. It is whether the circuit learns a representation of temporal structure that is useful under realistic noise, data volume, and classical baselines. The project therefore treats quantum advantage as an empirical hypothesis—not an assumption.
03
Designing quantum systems people can trust
A powerful algorithm is not yet a dependable system. Trust comes from knowing what can change, what must remain invariant, and how failure is contained.
My work in quantum information and post-quantum security spans blind quantum computing, error-correction ideas, random-circuit simulation, and crypto agility. These topics share a concern with structure under changing conditions.
Interactive figure
Resilience is an architecture, not an algorithm
Separate interface from implementation
Systems adapt faster when the surrounding workflow does not depend on one algorithm forever.
Measure what survives intervention
A representation is more convincing when it remains meaningful under ablation, noise, or substitution.
Make uncertainty visible
Trustworthy technical work exposes assumptions, baselines, and failure modes rather than hiding them.
04
The throughline
Find the representation that preserves what matters—and makes the next decision possible.
This is the thread connecting my research and the systems I want to build: scientific models that reveal hidden structure, infrastructure that can adapt without breaking, and institutions that help more people work at the frontier.