Events

Feb 25 - Mar 2, 2024

Critical Phenomena in Financial Markets and Social Networks

Prof. Christof Schmidhuber, ZHAW School of Engineering

Feb 27, 16:00 - 17:00

B9 L2 R2322

Financial Market Social Network Analysis Critical Phenomena Arbitrageurs

Analogies between financial markets and critical phenomena have long been observed empirically. So far, no convincing theory has emerged that can explain these empirical observations. Here, we take a step towards such a theory by modeling financial markets as a lattice gas.
Programming Theory in Security Analysis: A Tripartite Framework for Vulnerability Specification

Yinxi Liu, PhD Student, Computer Science and Engineering, the Chinese University of Hong Kong

Feb 27, 09:00 - 10:00

B9 L4 R4225

Living in a computer-reliant era, we’re balancing the power of computer systems with the challenges of ensuring their functional correctness and security. Program analysis has proven successful in addressing these issues by predicting the behavior of a system when executed.
PhD Dissertation Defense - Interface engineering for GaN HEMTs

Chuanju Wang, Ph.D. Student, Electrical and Computer Engineering

Feb 26, 17:00 - 19:00

B3 L5 R5209

GaN-based electronics Metal-GaN interfaces Dielectric-GaN interfaces

The research focuses on improving metal-GaN and dielectric-GaN interfaces for high-performance GaN-based electronics. For metal-GaN, the damage caused by e-beam evaporation was mitigated using Ti3C2Tx MXene films, achieving a record ION/IOFF current of 1013 and low subthreshold swing.
Learning with Longitudinal Electronic Health Record Data

Ricardo Henao, Associate Professor (former), Bioengineering

Feb 26, 11:30 - 12:30

B9 L2 H2

Abstract Longitudinal data extracted from electronic health record (EHR) data presents numerous opportunities for the development methodology of clinical decision support tools aimed at improving the delivery of healthcare. However, EHR data also pose many modeling challenges due to the intrinsic nature of such data, for instance, incompleteness, (not at random) missingness, temporal scale resolution, etc. Though all of these make it difficult to directly apply existing machine learning methodology to address problems of representation, identification, and prediction, we can leverage recent
Principled Scaling of Deep Neural Networks

Soufiane Hayou, Postdoc, Simons Institute, UC Berkeley

Feb 26, 09:00 - 10:00

B9 L4 R4225

Neural Networks

Neural networks have achieved impressive performance in many applications such as image and speech recognition and generation. State-of-the-art performance is usually achieved via a series of engineered modifications to existing neural architectures and their training procedures. However, a common feature of these systems is their large-scale nature: modern neural networks usually contain Billions - if not 10's of Billions - of trainable parameters, and empirical evaluations (generally) support the claim that increasing the scale of neural networks (e.g. width and depth) boosts the model performance if done correctly. However, given a neural network model, it is not straightforward to address the crucial question `how do we scale the network?'. In this talk, I will show how we can leverage different mathematical results to efficiently scale neural networks, with empirically confirmed benefits.
Smart Grids Communications: Protocols and Cybersecurity

Suhail Shaik, Electrical Engineering Department, King Fahd University of Petroleum & Minerals

Feb 25, 12:00 - 13:00

B9 L2 H2 H2

smart grids communication protocols ICT

The conventional power system is transforming into a smart grid through the integration of information and communication technologies (ICT). The incorporation of advanced ICT in the smart grid enhances its control and operational efficiency.

Feb 18 - Feb 24, 2024

Feb 11 - Feb 17, 2024

Feb 4 - Feb 10, 2024

Dependence and Causality in Functional Brain Networks

Hernando Ombao, Professor, Statistics

Feb 8, 12:00 - 13:10

B9 L2 H2 H2

Brain activity over the entire network is complex. A full understanding of brain activity requires careful study of its multi-scale spatial-temporal organization. Motivated by these challenges, we will explore some characterizations of dependence between components of a multivariate time series and then apply these to the study of brain functional connectivity.
Matrix-free Monolithic Multigrid Methods for Stokes and Generalized Stokes Problems

Prof. Ulrich Langer, Institute of Numerical Mathematics, Johannes Kepler University, Linz

Feb 7, 16:00 - 17:00

B2 L5 R5209

stokes equation

We consider the widely used continuous $Q_{k+1}-Q_k$ quadrilateral or hexahedral Taylor-Hood elements for the finite element discretization of the Stokes and generalized Stokes systems in two and three spatial dimensions.
Fast Solution Methods for Wave Propagation Problems: From Classical Domain Decomposition Solvers to Learning

Prof. Dr. Victorita Dolean, Mathematics and Computer Science, Scientific Computing, TU Eindhoven

Feb 6, 16:00 - 17:00

B2 L5 R5220

wave propagation numerical methods

Wave propagation and scattering problems are of huge importance in many applications in science and engineering - e.g., in seismic and medical imaging and more generally in acoustics and electromagnetics.
Diffusion with Heterogeneous Persistence and Permeability

Prof. Yong-Jung Kim, KAIST, Korea

Feb 6, 14:00 - 15:30

B2 L5 R5209

A subtle difference in a diffusion model can lead to an opposite conclusion. We need to understand how each component involved in the diffusion phenomenon contributes to the diffusion model. In this talk, we will discuss how nonconstant persistence and permeability play a role in the diffusion phenomenon.
Harnessing Liquid Metal for Next-Generation Soft Sensors, Actuators, and Reconfigurable Systems

Wedyan Babatain, Postdoc, MIT

Feb 6, 10:30 - 11:30

B9 L4 R4225

Liquid metal(LM)-based electronics have the potential to shape the future of intelligent systems, soft robotics, and wearable technologies by leveraging their sensing, actuation, and computational capabilities. This talk will discuss methods to harness the unique properties of liquid metal for applications in wearable sensors, soft actuators, and reconfigurable electronic platforms.
Recent advances in doubly nonlinear equations

Professor Vincenzo Vespri, of Mathematics Department at the University of Florence, Italy

Feb 5, 16:00 - 17:00

B1 L3 R3119

The term doubly nonlinear refers to the fact that the diffusion part depends nonlinearly both on the gradient and the solution itself. Such equations describe several physical phenomena and were introduced by Lions and Kalashnikov. These equations have an intrinsic mathematical interest because they represent a natural bridge between the more natural generalizations of the heat equation: the p-Laplacian and the porous medium equation.
Towards Transparency of IoT Device Presence

Gene Tsudik, Distinguished Professor of Computer Science, the University of California, Irvine (UCI)

Feb 5, 11:30 - 12:30

B9 L2 H2 H2

cybersecurity IoT

As many types of IoT devices worm their way into numerous settings and many aspects of our daily lives, awareness of their presence and functionality becomes a source of major concern. Hidden IoT devices can snoop (via sensing) on nearby unsuspecting users, and impact the environment where unaware users are present, via actuation.
Trainable Decompositions: Addressing Device Heterogeneity

Prof. Samuel Horvath, Machine Learning at Mohamed bin Zayed University of Artificial Intelligence (MBZUAI)

Feb 4, 15:00 - 16:00

Buliding 4 L5 R5220

In the first part of the talk, we introduce Ordered Dropout, a mechanism that achieves an ordered, nested representation of knowledge in deep neural networks (DNNs).
Damage-free pixel definition technology for future microLED display

Zhiyuan Liu, Ph.D. Student, Electrical and Computer Engineering

Feb 4, 12:00 - 13:00

B9 L2 H2

Micro-LEDs Selective thermal oxidation(STO) device performance

Recently, InGaN-based blue, green, and red (RGB) micro-LEDs have garnered significant attention and interest due to their exceptional features such as high contrast, intense brightness, excellent energy efficiency, and long device lifetimes, positioning them as strong contenders as the next-generation display technology.

Jan 28 - Feb 3, 2024

Jan 21 - Jan 27, 2024

Jan 7 - Jan 13, 2024

Leading the world to 5G and beyond: A prototyping perspective

Dr Amira Alloum

Jan 7, 13:30 - 14:30

B1 L3 R3119

The talk will introduce the 5G advanced standard vision for vertical industries and relate it to the latest prototyping effort released by the Qualcomm Research teams.

Dec 17 - Dec 23, 2023

Efficient Machine Learning across Hardware, Software, and Algorithms

Prof. Mohamed Abdelfattah, Electrical and Computer Engineering Department at Cornell University

Dec 17, 14:00 - 15:30

B2 L5 R5209

Efficient machine learning Hardware software algorithms

Deep neural networks (DNNs) are revolutionizing computing, necessitating an integrated approach across the computing stack to optimize efficiency. In this talk, I will explore the frontier of DNN optimization, spanning algorithms, software, and hardware. We'll start with hardware-aware neural architecture search, demonstrating how tailoring DNN architectures to specific hardware can drastically enhance performance.

Dec 10 - Dec 16, 2023

Refined Vehicle Perception: An Innovative Framework for Robust Pedestrian Intent Prediction in Challenging Environments

Khalid Elgazzar, Professor, Engineering and Applied Science, Ontario Tech University

Dec 14, 10:00 - 11:00

B1 L3 R3119

In this talk, I will present an innovative framework we developed to address these limitations and accurately predict pedestrian crossing intentions. At the core of the framework, we implement an image enhancement pipeline to enable the detection and rectification of various defects that may arise during unfavorable weather conditions. Following this, we employ a transformer-based network with a self-attention mechanism to predict the crossing intentions of pedestrians. This pipeline enhances the model's robustness and accuracy in classification tasks. We assessed our framework using the famous JAAD dataset. Performance metrics indicate that our model achieves state-of-the-art results while ensuring significantly low inference times.
From Academia to Management Consulting and Startups

Dr. Gareth Guvanasen, Director of AI and Data Analytics Division, Partner at Terra Digital Ventures

Dec 11, 15:00 - 16:00

B3 L5 R5209

In my lecture, I will share personal insights on transitioning from academia to management consulting and creating startups. I will discuss how to assess if a career in management consulting suits your goals and skills, and provide practical advice on interviewing successfully with consulting firms.
Resistance is Futile: Balancing Opportunities and Security Challenges of Federated Learning

Prof. Ahmad-Reza Sadeghi, Distinguished Professor of Computer Science, the Technical University of Darmstadt, Germany.

Dec 10, 12:00 - 13:00

B4 L5 R5220

artificial intelligence Federated learning

The rapid growth of Artificial Intelligence (AI) and Deep Learning mirrors an infectious phenomenon. While AI systems promise diverse applications and benefits, they bear substantial security and privacy risks. Indeed, AI represents a goldmine for the security and privacy research domain.

Dec 3 - Dec 9, 2023

Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience

Dec 7, 12:00 - 13:00

B9 L2 H2

In this talk, we consider Bayesian parameter inference associated to a class of partially observed stochastic differential equations (SDE) driven by jump processes. Such type of models can be routinely found in applications, of which we focus on the case of neuroscience.
A Brief History of Artificial Intelligence: Its Past and Its Future

Dr. Jeffrey M. Bradshaw, Institute for Human and Machine Cognition (IHMC), Florida, USA

Dec 6, 14:00 - 15:00

B1 L4 R4102

Over the past decades, Jeff has been lucky enough to follow some of these developments from a front-row seat. He will share selected perspectives on current and past research including the various pendulum swings over the years between extremes, such as expertise-based knowledge engineering vs. data-centric machine learning, autonomous vs. mixed-initiative systems, publicly available chatbot capabilities vs. personalized, policy-governed multi-agent systems. In this talk, I will show what past research in AI has to teach us about the future.
Nonlinear energy stability for phase-field models: Numerics and analysis

Prof. Tao Tang

Dec 5, 16:00 - 17:00

B5 L5 R5209

phase-field models

Coffee Time: 15:30 - 16:00. The phase-field model, a powerful modeling tool for dealing with interface problems, has been widely used in various fields such as computational physics, computational biology, materials engineering, and even image processing. The dissipation of free energy is an important and fundamental property of the phase-field model.
Error Estimates for Deep Learning Methods in Fluid Dynamics

Professor, Suleyman Ulusoy, School of Arts and Sciences, American University of Ras Al Khaimah

Dec 5, 14:30 - 15:30

B1 L4 R4214

We provide error estimates and stability analysis of deep learning techniques for certain partial differential equations including the incompressible Navier-Stokes equations. In particular, we obtain explicit error estimates (in suitable norms) for the solution computed by optimizing a loss function in a Deep Neural Network approximation of the solution, with a fixed complexity. This is a joint work with A. Biswas and J. Tian.
Gradient Estimates for weak solutions of Elliptic PDE's - Seminar

Giuseppe Di Fazio, Professor, Mathematics and Computer Sciences, University of Catania, Italy

Dec 5, 13:00 - 14:00

B1 L4 R4214

Let us consider an elliptic equation of second order in variational form i.e. div(A(x)∇u) = divf in a bounded domain Ω ⊂ Rn where the function f belongs to some suitable function space.
RC3 Advisory Board Distinguished Lectures Workshop

RC3 Advisory Board

Dec 5, 08:30 - 12:30

B5 L5 R5220

cybersecurity resilience Computer science

Machine learning (ML) has witnessed remarkable advancements in recent years, demonstrating its effectiveness in a wide array of applications, including intrusion detection systems (IDS). However, when operating in adversarial environments, ML-based systems are susceptible to a range of attacks.
Nonlinear stability of splitting methods for Cahn-Hilliard equations

Prof. Tao Tang's personal website

Dec 4, 15:30 - 17:00

B3 L5 R5220

nonlinear stability splitting methods Cahn-Hilliard equations

Splitting methods have been shown to a useful tool in solving phase field equations. However, rigorous nonlinear stability analysis has not been available. In this short course, we will discuss some recent development in this direction.
Splitting methods: merits and pitfalls

Prof. Alexander Ostermann

Dec 3, 16:00 - 17:00

B2 L5 R5220

splitting methods numerical integration computational efficiency pitfalls

Coffee Time: 15:30 - 16:00. Splitting methods are a well-established tool for numerically integrating time-dependent partial differential equations. These methods split the vector field into disjoint components, which are integrated separately using an appropriate time step. The individual flows are then combined to obtain the desired numerical approximation.
High speed BLDC motor control method

Prof. Marcin Baszynski

Dec 3, 12:00 - 13:00

B9 L2 H2

power electronics

This talk presents a method for obtaining good accuracy of the measured rotational speed and discusses the control algorithms and modulation technique of the high-speed BLDC motor.

Nov 26 - Dec 2, 2023