CEMSE Weekly Updates - September 9, 2025 Tue, Sep 9 2025 Newsletter Upcoming Events Stay informed about the upcoming events and the latest news from CEMSE. Optical Frequency Combs: From Conventional Techniques to Emerging Approaches Aram Mkrtchyan, Postdoctoral Research Fellow, Electrical and Computer Engineering Sep 14, 12:00 - 13:00 B9 L2 R2325 Optical frequency combs-optical spectra consisting of many equidistant lines — are powerful tools in modern photonics. The advancements in optical frequency comb generation using high-quality microring resonators (MRRs) have given rise to numerous breakthroughs in metrology, spectroscopy, communications, as well as in quantum computing, quantum data processing, and quantum sources. Under the right conditions, MRRs support dissipative cavity solitons: short optical pulses that circulate in the resonator while maintaining their shape thanks to a balance of dispersion, nonlinearity, gain, and loss. From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs Peter Richtarik, Professor, Computer Science Sep 15, 12:00 - 13:00 B9 L2 R2325 AI machine learning optimization algorithms LLM This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers. From the Ball-Proximal (Broximal) Point Method to Efficient Training of LLMs Peter Richtarik, Professor, Computer Science Sep 16, 16:00 - 17:00 B1 L3 R3119 AI machine learning optimization algorithms LLM This talk introduces the Ball-Proximal Point Method, a new foundational algorithm for non-smooth optimization with surprisingly fast convergence, and Gluon, a new theoretical framework that closes the gap between theory and practice for modern LMO-based deep learning optimizers. Geometric Sensor Fusion for Pose Estimation on Riemannian Manifolds Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech) Sep 18, 11:00 - 12:00 B1 L3 R3119 This seminar introduces a novel geometric sensor fusion framework that integrates inertial measurements with acoustic ranging on Riemannian manifolds to achieve robust, high-accuracy pose estimation for autonomous systems in GPS-denied environments. Neural Methods for Amortized Inference with Models for Spatial Extremes Raphaël Huser, Associate Professor, Statistics Sep 18, 12:00 - 13:00 B9, L2, R2325 Neural Bayes estimators are neural networks that approximate Bayes estimators. Once trained, these estimators are not only statistically efficient, but also extremely fast to evaluate and amenable to rapid uncertainty quantification. Neural Bayes estimators thus have compelling advantages when used with spatial models that have a computationally intractable likelihood function, as often the case when modeling spatial extremes. In this talk, I will showcase the power of neural Bayes estimators for spatial extremes in a range of climate-related and geo-environmental data applications.
Mohammed Hussain AlSharif, Postdoctoral Research Fellow, California Institute of Technology (Caltech)
