Although Kinetic Monte Carlo (KMC) methods are widely used for simulating stochastic events during nucleation and deposit growth, their speed is seriously compromised when surface diffusion rates are rapid and KMC time steps are small. Such conditions arise, for example, under conditions of high ratios of surface-diffusivity-to-deposition-rate that result in widely spaced deposit clusters. While numerical coarse-graining and other approximate methods for speedup are available, it is, however, often also essential to retain accuracy at the atomic scale in order to simulate nucleation and early stages of deposit growth.  In this work we report on development of the “Exact Lattice First Passage Time” (ELFPT) algorithm, which allows for the accurate and efficient multi-scale numerical simulation of surface diffusion processes.  We will show that ELFPT can improve the efficiency of sub-monolayer nucleation and growth simulations up to 100-fold over conventional KMC.  These preliminary results were for a pristine physical system that are lacking in several important features required for the application to real-world electrodeposition systems.  We also report recent results obtained with a next-generation ELFPT method that includes the following additional features: (1) treatment of edge diffusion in a First Passage (FP) fashion, (2) adaptation to additional lattice configurations beyond  (100) to include the (111) family, (3) multi-layer growth for systems expressing Volmer-Weber and Stranski-Krastanov growth, (4) treatment of heteroepitaxial growth, with different reaction rates on substrate and deposit material, (5) consideration of  defects (kinks, edges, voids), and  (6) alloy systems. Atomic-scale resolution is essential in certain nucleation systems for which the template for extended growth is defined at the atomic scale. The ELFPT approach has proven to be particularly efficient for situations of high ratios of surface-diffusivity-to-deposition-rate, which allow for the simulation of large domains in the range of a few micrometers without compromising the atomic-scale resolution.  Such lengths scales are necessary in order to obtain statistically significant comparisons with experimental data such as obtained by SEM or AFM. [1] A. Bezzola, B. Bales, R. Alkire, L. Petzold, “An Exact and Efficient First Passage Time Algorithm for Reaction-Diffusion Processes on a 2D-Lattice”, submitted to J. Comput. Phys.    Secure Control Of Cyber-Physical Systems  Fabio Pasqualetti Cyber-physical systems integrate physical processes, computational resources, and communication capabilities. Examples of cyber-physical systems include transportation networks, power generation and distribution networks, water and gas distribution networks, and cooperative robotic systems. As recently highlighted by various security incidents, cyber-physical systems are prone to failures and attacks on their physical infrastructure, as well as cyber attacks on their data management and communication layer. This talk discusses several aspects of cyber-physical security. I describe a control-theoretic framework for cyber-physical systems and attacks. I model a cyber-physical system under attack as a descriptor system subject to unknown inputs affecting the state and the measurements. For this model I derive fundamental limitations of attack detection and identification, design possible countermeasures, and present illustrative examples. Bio: Fabio Pasqualetti is a Postdoctoral Researcher in Mechanical Engineering at the University of California, Santa Barbara. He received a Doctor of Philosophy degree in Mechanical Engineering from the University of California, Santa Barbara, in 2012, a Laurea Magistrale degree ``Summa cum laude'' in Automation Engineering from the University of Pisa, Pisa, Italy, in 2007, and a Laurea degree ``Summa cum laude'' in Computer Engineering from the University of Pisa, Pisa, Italy, in 2004. His main research interest is in secure control systems, with application to multi-agent networks, distributed computing and power networks. Other interests include vehicle routing and combinatorial optimization, with application to distributed patrolling and camera surveillance.