Graduate Students Seminar

Speaker 1: Fred Azizi

Title

Re-weighting for Improved Utility in Differentially Private Data Synthesis under Pseudo-Posterior Mechanism

Abstract

Balancing data utility and privacy is a fundamental challenge in modern data dissemination. Differential privacy (DP) provides strong formal privacy guarantees, but classical noise-injection approaches can substantially distort highly skewed data. An alternative DP-compliant approach is synthetic data generation through Bayesian pseudo-posterior mechanisms. In this talk, we follow Hu, Savitsky, and Williams (2025) and study record-specific weighted pseudo-posterior mechanisms for differentially private data synthesis. We focus on a simple re-weighting strategy that improves efficiency by redistributing record-level weights while preserving the same asymptotic privacy guarantee. Since the privacy bound depends only on the maximum Lipschitz value, this adjustment can improve utility without increasing the privacy budget. Simulation results illustrate the practical gains of this approach under skewed data.

Speaker 2: Jalil Ahmad

Title

Can Quantum Computers Be Used for Parameter Estimation in Epidemiological and Chaotic Dynamical Models?

Abstract

Parameter estimation is a fundamental challenge in the calibration of ordinary differential equation (ODE) modelas, where repeated numerical integration can lead to high computational cost. In this work, we investigate whether near-term quantum algorithms can be leveraged to assist parameter estimation in nonlinear dynamical systems. Focusing first on the susceptible–infected–recovered (SIR) model, we develop a hybrid classical–quantum framework that reformulates a data-assimilation–based parameter estimation problem as a combinatorial optimization task. Model dynamics and data assimilation are enforced entirely on the classical side, while the resulting parameter estimation objective is discretized and approximated by a quadratic unconstrained binary optimization (QUBO) surrogate. This surrogate is mapped to an Ising Hamiltonian and minimized using the Quantum Approximate Optimization Algorithm (QAOA). We further apply this approach to the Lorenz system in the chaotic regime. In this setting, the method is used to recover classical system parameters from partial state observations, illustrating its ability to handle chaotic dynamical systems. Numerical experiments with synthetic data show that the proposed approach accurately recovers parameters and reproduces observed dynamics while requiring substantially fewer ODE solves than classical iterative methods. The framework avoids quantum state tomography and is compatible with near-term quantum hardware, illustrating a viable pathway for integrating quantum optimization into data-driven parameter estimation for epidemiological and chaotic dynamical systems.