Upcoming Events - Feb 16 - Feb 22, 2025

Stay informed about the latest events happening within the CEMSE Division. Here is what is coming up next week:

KAUST-CEMSE-ECE-PhD-Dissertation-Defense-Dhanu-Chetri-Ultrawide-Bandgap-Semiconductor-Devices-and-Monolithic-Circuits

Ultrawide Bandgap Semiconductor Devices and Monolithic Circuits

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B3 L5 R5220

This thesis investigates the potential of ultrawide bandgap (UWBG) semiconductors like gallium oxide (Ga2O3) and aluminium nitride (AlN) for high-power applications, focusing on the development of monolithic devices and circuits, including pseudo-CMOS inverters, bidirectional switches, and MOSFETs, demonstrating their potential for next-generation electronics operating in extreme environments.

mR_LWE-CP-ABE: a Recoverable CP-ABE protocol for Post-quantum Cryptography

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B9 L2 R2325

This talk presents a novel lattice-based revocable CP-ABE scheme built upon lattice-based cryptography to addresses the challenge of efficient user revocation in post-quantum secure cloud environments by leveraging semi-trusted mediators to enable multi-step decryption without costly re-encryption.

Conformal Mapping and Invariants – Motivating Numerical PDE Approaches

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B1 L3 R3119

This study explores conformal mappings and invariants, particularly capacity and hyperbolic length, using PDE-based numerical methods (hp-FEM and BIE) to solve geometric optimization problems, such as minimizing heat loss or maximizing it under distance constraints, for domains with complex geometries.

Convergence of Descent Methods under Polyak- Lojasiewicz-Kurdyka Properties

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B1 L3 R3119

This talk presents a novel convergence analysis for a class of descent methods in nonsmooth and nonconvex optimization under the Polyak-Lojasiewicz-Kurdyka properties, proving finite termination under certain conditions and revealing incompatibilities between these properties and gradient Lipschitz continuity in certain frameworks.
KAUST-CEMSE-AMCS-STAT-Graduate-Seminar-Boris-Mordukhovich-Derivative-free-nonconvex-opt-noise

Derivative-Free Methods in Nonconvex Optimization with and Without Noise

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B9, L2, R2325

This talk addresses the study of nonconvex derivative-free optimization problems, where only information of either smooth objective functions or their noisy approximations is available. General derivative-free methods are proposed for minimizing differentiable (not necessarily convex) functions with globally Lipschitz continuous gradients, where the accuracy of approximate gradients is interacting with stepsizes and exact gradient values.