Events

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

Nov 19 - Nov 25, 2023

Nov 12 - Nov 18, 2023

Multiscale Learning of High-dimensional Cell Fate

Prof. Qing Nie, Department of Mathematics, Department of Developmental and Cell Biology, NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine

Nov 16, 15:30 - 17:00

B9 L4 R4225

Abstract Cells make fate decisions in response to dynamic environments, and multicellular structures emerge from multiscale interplays among cells and genes in space and time. The recent single-cell genomics technology provides an unprecedented opportunity to profile cells for all their genes. While those measurements provide high-dimensional gene expression profiles for all cells, it requires fixing individual cells that lose many important spatiotemporal information. Is it possible to infer temporal relationships among cells from single or multiple snapshots? How to recover spatial
How do periodic structures affect nonlinear waves?

David Ketcheson, Professor, Applied Mathematics and Computational Sciences

Nov 16, 12:00 - 13:00

B9 L2 H2

We are all familiar with the tendency of water waves to break in shallow water, for instance at the beach. Indeed, breaking is a universal behavior of solutions to first-order nonlinear hyperbolic PDEs, and manifests itself in phenomena ranging from traffic jams to shock waves.
Tensor-train Methods for Partial Differential Equations and its application to a Neutron Transport Problem

Daniele Boffi, Associate Dean for Faculty, Computer, Electrical and Mathematical Sciences and Engineering

Nov 14, 15:30 - 17:00

B9 L4 R4225

Partial Differential Equations

Tensor network techniques are known for their ability to approximate low-rank structures and beat the curse of dimensionality. They are also increasingly acknowledged as fundamental mathematical tools for efficiently solving high-dimensional Partial Differential Equations (PDEs). In this talk, we present a novel method that incorporates the Tensor Train (TT) and Quantized Tensor Train (QTT) formats for the computational resolution of time-independent Boltzmann Neutron Transport equations (BNTEs) in Cartesian coordinates.
The Partition of Unity Finite Element Method for the Schrödinger Equation

Francesca Gardini, Associate Professor, Dipartimento di Matematica, Università di Pavia, Italy

Nov 14, 14:30 - 15:30

B9 L4 R4225

A Schrödinger equation for the system’s wavefunctions in a parallelepiped unit cell subject to Bloch-periodic boundary conditions must be solved repeatedly in quantum mechanical computations to derive the materials’ properties.
Efficient Algorithms for Vision and Language Learning

Jun Chen , Ph.D. Student, Computer Science

Nov 14, 12:15 - 14:15

B1 L3 R3426

The development of advanced vision-language models necessitates considerable resources, both in terms of computation and data. There is growing interest in training these models efficiently and effectively and leveraging them for various downstream tasks. This dissertation presents several contributions aimed at improving both learning and data efficiency in vision-language learning, and how to leverage them into downstream tasks.
Experimental Design Statistical Workshop

Hernando Ombao, Professor, Statistics

Nov 14, 09:00 - Nov 15, 17:00

B1 L4 R4102

This workshop will cover the statistical essentials to designing experiments including power analysis, sample size calculation, regression, and variance analysis. A statistically sound experimental design is essential in applying for research grants. Detailed syllabi will be sent to those who register. Limited seats. Sign up now!
The SCION Research and Education Network

Adrian Perrig, Professor, the Department of Computer Science, ETH Zürich, Switzerland

Nov 13, 11:30 - 12:30

B9 L2 H2 H2

cybersecurity internet architecture

Imagining a new Internet architecture enables us to explore new networking concepts without the constraints imposed by the current Infrastructure. In this presentation, we invite you to join us on our 14-year-long expedition of creating the SCION next-generation secure Internet architecture.
Family of Mean-Mixtures of Multivariate Normal Distributions

Prof. Narayanaswamy Balakrishnan, Department of Mathematics and Statistics, McMaster University

Nov 12, 15:30 - 16:30

B1 L4 R4102

In this talk, I will describe the family of mean-mixtures of multivariate normal distributions and establish many of its properties, stochastic representations, moments, distributional shape characteristics, etc.
Learning Sequential Modeling Algorithms for Motion Forecasting, Reinforcement Learning, and Vision-Language Understanding

Deyao Zhu, Ph.D. Student, Computer Science

Nov 12, 15:00 - 16:30

B1 L4 R4214

Sequential modeling algorithms have made significant strides in a variety of domains, facilitating intelligent decision-making and planning in complex scenarios. This dissertation explores the potential and limitations of these algorithms, unveiling novel approaches to enhance their performance across diverse fields, from autonomous driving and trajectory forecasting to reinforcement learning and vision language understanding.
Models and Inference for Non-Gaussian Random Vectors and Fields

Sagnik Mondal, Ph.D. Student, Statistics

Nov 12, 12:30 - 14:30

B5 L5 R5209

The multivariate Gaussian distribution is widely used in many statistical applications due to its appealing features. However, real-world data often violate its assumptions, showing skewness and/or tail-thickness.
Mathematical Control Theory in Aeronautical Engineering: Boosted Maneuverability in Stall and Unconventional Stability of Insect Flight

Haithem Taha, Visiting Professor, Computer, Electrical and Mathematical Sciences and Engineering, KAUST

Nov 12, 12:00 - 13:00

B9 L2 H2

Aeronautical Sciences

The great Russian Mathematician Vladimir Arnold once said, “Mathematics is the part of physics where experiments are cheap”. In this talk, I will present two modern applications in aeronautical engineering where simple, but rigorous, mathematical control theory led to game-changing results exploiting nonlinearities and time-variation rather than obviating them.

Nov 5 - Nov 11, 2023