Upcoming Doctoral Dissertations
School of Engineering and Science
DISSERTATIONS IN OCTOBER
October 22, 2026 - Nida Ahsan
Candidate | Nida Ahsan |
Date | Thursday, October 22, 2026 |
Time | 2:30 PM (Eastern) |
Title | Dynamics-Informed Reinforcement Learning for Adaptive Control of Nonlinear Dynamical Systems |
Location | EAS 229A |
"Many engineering systems, including robotic systems and ocean wave energy devices, exhibit nonlinear dynamics that make reliable control challenging. These nonlinearities can cause small changes in operating conditions, system parameters, or initial states to produce substantially different responses, including transitions between distinct stable behaviors." Read more
October 22, 2026 - Muhammad Bilal Khan
Candidate | Muhammad Bilal Khan |
Date | Thursday, October 22, 2026 |
Time | 2:30 PM – 4:30 PM (Eastern) |
Title | Dynamics of Nonlocal Elastic Metamaterials |
Location | EAS 229A |
"Elastic metamaterials enable unprecedented control over wave propagation by tailoring structural architecture rather than material composition. While conventional designs rely primarily on nearest-neighbor coupling, such local interactions limit the achievable dispersion characteristics, bandwidth, and the realization of nonreciprocal behavior. This dissertation details a comprehensive framework for the inverse design, implementation, and application of one-dimensional nonlocal elastic metamaterials capable of realizing prescribed, multi-band dispersion relations and unconventional wave phenomena." Read more
October 28, 2026 - Yifeng Peng
Candidate | Yifeng Peng |
Date | Wednesday, October 28, 2026 |
Time | 11:00 AM (Eastern) |
Title | Quantum for AI and AI for Quantum: Toward Future High-Performance Hybrid Computing |
Location | Babbio Center 503 |
"Artificial intelligence (AI) and quantum computing offer two directions for designing hybridcomputational systems: quantum circuits can provide task-specific components within AI models,while AI methods can help determine how quantum circuits are initialized, trained, and compiled.Realizing this reciprocal relationship requires connecting the quality of a quantum component'scontribution to the resources needed to obtain it. This dissertation investigates that connectionthrough six studies organized around Quantum for AI and AI for Quantum, and develops a co-designframework for future high-performance hybrid computing." Read more
To view past Doctoral Dissertations, please visit this website.