Events

Mar 19 - Mar 25, 2023

Making federated learning practical and confidential

Dr. Ruichuan Chen, Distinguished Member of Technical Staff and a Tech Lead, Nokia Bell Labs

Mar 23, 15:30 - 16:30

B4 L5 R5209

Federated learning

Federated learning (FL) is increasingly deployed among multiple clients to train a shared model over decentralized data. To address the privacy concerns, FL systems need to protect the clients' data from being revealed during training, and also control data leakage through trained models when exposed to untrusted domains. However, existing FL systems (with distributed differential privacy) work impractically in the presence of client dropout, resulting in either poor privacy guarantees or degraded training accuracy. In addition, existing FL systems focus on safeguarding the privacy of training data, but not on protecting the confidentiality of the models being trained, which are increasingly of high business value. In this talk, I will present two pieces of our recent work that aim to address these aforementioned issues.
Interpretation, Verification and Privacy Techniques for Improving the Trustworthiness of Neural Networks

Arnaud Dethise, Ph.D. Student, Computer Science

Mar 22, 12:30 - 14:30

B1 L3 R3119

This presentation addresses the challenges associated with trusting Neural Networks due to their black-box nature and limited ability to answer important questions on how they behave. The thesis proposes techniques that increase the trustworthiness of Neural Network models by employing approaches to overcome their black-box nature. The techniques include efficient extraction and verification of weights and decisions to ensure correctness with regards to pre-existing properties, continuous and exact explanations of the model behavior, and scalable training techniques providing strong, theoretically provable guarantees of privacy. We provide strong, approximation-free guarantees about Neural Networks, improving their trustworthiness to make it more likely that users will be willing to deploy them in the real world.
Proximal Algorithms for Large-Scale Convex Non-smooth Optimization

Laurent Condat, Senior Research Scientist, Computer, Electrical and Mathematical Sciences and Engineering

Mar 21, 16:00 - 17:00

B2 L5 R5220

algorithms stochastic algorithm

Convex nonsmooth optimization problems, whose solutions live in very high dimensional spaces, have become ubiquitous. To solve them, the class of iterative fixed-point algorithms known as proximal splitting algorithms is particularly adequate: they consist of simple operations, handling the terms in the objective function separately. I will present a selection of recent primal-dual algorithms within a unified framework, which consists in solving monotone inclusions with well-chosen spaces and metrics.
Tackling credential abuse together

Michael Reiter, James B. Duke Distinguished Professor, Departments of Computer Science and Electrical & Computer Engineering, Duke University

Mar 20, 12:00 - 13:00

B9 L2 H2 H2.

data security

Despite long-ago predictions (e.g., see Bill Gates, 2004) that other user-authentication technologies would replace passwords, passwords remain not only pervasive but have flourished as the dominant form of account protection, especially at websites such as retailers that require a low-friction user experience. This talk will describe our research on methods to tackle three key ingredients of account takeovers for password-protected accounts today: (i) site database breaches, which is the largest source of stolen passwords for internet sites; (ii) the tendency of users to reuse the same or similar passwords across sites; and (iii) credential stuffing, in which attackers submit breached credentials for one site in login attempts for the same users' accounts at another.
On Fourier methods and machine learning techniques in Computational Finance.

Prof. Kees Oosterlee, Utrecht University

Mar 20, 12:00 - 13:00

B9 L3 R3128

In this presentation we will explain how we can solve linear, semi-linear as well as nonlinear partial differential equations by the concept of backward stochastic differential equations and Fourier cosine expansions. We will discuss the highly efficient pricing of financial options in the Fourier context by means of the COS method. Particularly, we also present a new jump-diffusion process, the Heston-Queue-Hawkes (HQH) model, combining the well-known Heston model and the recently introduced Queue-Hawkes (Q-Hawkes) jump process. Like the Hawkes process, the HQH model can capture the effects of self-excitation and contagion of stock prices.
5th KAUST CEMSE MaS Workshop on Modelling and Simulation

Speakers from KAUST, Melbourne, Utrecht, Karlsruhe, Erlangen, Brisbane, Langensteinbach, Lugano, Frankfurt, Italy

Mar 20, 09:00 - Mar 25, 17:30

B3 L5 R5209

Applied Partial Differential Equations

The workshop provides a forum for researchers to present and discuss recent progress in modelling and simulation research. Topics are Simulating unsteady CO 2 flow through brine-saturated cross-bedded sandstones: towards relative permeability curves for unstable displacement On Fourier methods and machine learning techniques in Computational Finance Pore-scale modelling of flow and transport in porous media Uncertainty Quantification of Density-Driven Groundwater Flow Simulation of unsaturated Density-Driven flow with a phreatic surface Modeling and Large-scale Simulation of Carbon Dioxide
Monolithic Quantum Devices on SOI for Silicon Photonics

Prof. Kei May Lau, Electronic Engineering, Chinese University, Hong Kong

Mar 19, 12:00 - 13:00

B9 L2 H2

Monolithic Quantum Devices

Si photonics has been developed to enable the next generation tele- and data-communications for its high performance, making use of the mature silicon CMOS technologies.

Mar 12 - Mar 18, 2023

Mar 5 - Mar 11, 2023

Feb 26 - Mar 4, 2023

Feb 19 - Feb 25, 2023

Feb 12 - Feb 18, 2023

Feb 5 - Feb 11, 2023