Graduate Seminar

Seeing is Believing? Media Integrity in a Post-Truth World

Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering
Monday, January 27, 2020, 12:00
- 13:00
Building 9, Level 2, Hall 1
Contact Person
Elmootazbellah Elnozahy
The emergence of “fake news” along with sophisticated techniques using machine learning to create realistic looking media such as deepfakes, has led to a renewed interest in digital media forensics. In this talk, Professor Nasir Memon will broadly discuss how media is generated and manipulations have been traditionally detected. He will then look at new approaches using machine learning for creating media that are leading us to a world where images and video cannot be believed any more as they can evade traditional detection techniques. Professor Memon will end by discussing approaches that are being developed to return integrity and trust in digital media.
Tutorial

Amazon Web Services (AWS) ML Immersion Day

Mohib Khan, Hesham Abouelmagd, Shijaz Abdulla (AWS)
Monday, January 27, 2020, 08:30
- 16:15
Building 19, Hall 1
Contact Person
Marco Canini
The ML Hub with the support of the AI Initiative, is excited to be hosting the AWS ML Immersion Day! Join us for a full-day immersion tutorial and hands-on lab on Amazon’s ML tools. The program includes an introduction to AWS AI and machine learning services and hands-on module on Amazon Lex and SageMaker.
Seminar

Dictionary Attacks on Biometrics

Prof. Nasir Memon, Vice Dean for Academics and Student Affairs and Professor of Computer Science and Engineering at the New York University Tandon School of Engineering
Sunday, January 26, 2020, 16:00
- 17:00
Building 9, Level 2, Hall 2
Contact Person
Elmootazbellah Elnozahy
Contrary to the prevailing belief, we show that user authentication based on biometrics is vulnerable to dictionary attacks. We show the problem is particularly significant for partial prints used in smartphones and increasingly adopted for authentication tasks ranging from unlocking the devices screen up to payment authorization. We also show that speaker verification systems are also vulnerable to dictionary attacks. We then discuss ways to mitigate such attacks.
Research Conference

KAUST Research Conference Digital Health 2020

Monday, January 20, 2020, 08:00
- 17:00
Building 19, Level 2, Hall 1
Computational Bioscience Research Center at King Abdullah University of Science and Technology is pleased to announce the KAUST Research Conference on Digital Health 2020.
Seminar

BBDS: A Privacy-preserving Blockchain-Based Data Sharing System

Prof. Jin Li, Computer Science, Guangzhou University
Wednesday, January 15, 2020, 12:00
- 13:00
Building 1, Level 4, Room 4214
Contact Person
Elmootazbellah Elnozahy

Abstract

Nowadays, the devices in the Internet of Things have been widely used.

Seminar

PREMA: Principled Tensor Data Recovery from Multiple Aggregated Views

Faisal M. Almutairi, Ph.D. Candidate, Electrical and Computer Engineering, University of Minnesota
Wednesday, January 08, 2020, 12:00
- 13:00
B1 L4 Room 4214
Contact Person
Elmootazbellah Elnozahy
The proposed method, called PREMA, leverages low-rank tensor factorization tools to provide recovery guarantees under certain conditions. PREMA is flexible in the sense that it can perform the disaggregation task on data that have missing entries, i.e., partially observed. The proposed method considers challenging scenarios: i) the available views of the data are aggregated in two dimensions, i.e., double aggregation, and ii) the aggregation patterns are unknown. Experiments on real data from different domains, i.e., sales data from retail companies, crime counts, and weather observations, are presented to showcase the effectiveness of PREMA.
Seminar

Practical Cryptographic Framework to Manage Security and Privacy for IoT Endpoint devices

Prof. Chunhua Su, Computer Science, University of Aizu
Wednesday, January 01, 2020, 12:00
- 13:00
B1 L4 Room 4214
Contact Person
Elmootazbellah Elnozahy
In this talk, the speaker will provide a high-level introduction to his recent research on IoT endpoint security. Firstly, he will introduce requirements followed by a discussion on cryptographic algorithm implementation. He will mainly focus on an overview of efficient cryptography for IoT endpoints and system privacy issues. Then he will discuss security management approaches, positives, negatives and challenges to resolve, linking to the endpoint device security section with regards to realistic device needs/capabilities.
Seminar

SLATE: Design of a Modern Distributed and Accelerated Dense Linear Algebra Library

Wednesday, December 11, 2019, 16:00
- 17:00
Building 2, Level 5, Room 5220
Contact Person
David Keyes
The SLATE (Software for Linear Algebra Targeting Exascale) library is being developed to provide fundamental dense linear algebra capabilities for current and upcoming distributed high-performance systems, both accelerated CPU–GPU based and CPU based.
Graduate Seminar

Proximal Splitting Methods for Convex Optimization: An Introduction

Monday, December 02, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
Contact Person
Peter Richtarik
This talk will be a gentle introduction to proximal splitting algorithms to minimize a sum of possibly nonsmooth convex functions. Several such algorithms date back to the 60s, but the last 10 years have seen the development of new primal-dual splitting algorithms, motivated by the need to solve large-scale problems in signal and image processing, machine learning, and more generally data science. No background will be necessary to attend the talk, whose goal is to present the intuitions behind this class of methods.
Graduate Seminar

Enabling smart and connected systems

Sunday, December 01, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
The talk will discuss how recent advances in wireless computing and communication nodes can be harnessed to serve the multitude of deployment scenarios required to empower communities of the future with smart and connected systems. In this talk, we address fundamental questions that should be asked when contemplating future smart and connected systems, namely, How, Where and What? (1) How can we design computing and communication nodes that best utilize resources in a way that is cognizant of both the abilities of the platform, as well as the requirements of the network? (2) Where are the nodes deployed? By understanding the context of deployment, one can architect unique solutions that are currently unimaginable. With the transformation to diverse applications such as body area networking, critical infrastructure monitoring, precision agriculture, autonomous driving, etc., the need for innovative solutions becomes even more amplified. (3) What benefit can be inferred from the data gathered by nodes in the capacity of computing, communication, and sensing?
Dean's Distinguished Lecture

Hidden Backdoors in Deep Learning Systems

Prof. Ben Zhao, Computer Science, University of Chicago, USA
Monday, November 25, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
In this talk, I will describe two recent results on detecting and understanding backdoor attacks on deep learning systems. I will first present Neural Cleanse (IEEE S&P 2019), the first robust tool to detect a wide range of backdoors in deep learning models. We use the idea of perturbation distances between classification labels to detect when a backdoor trigger has created shortcuts to misclassification to a particular label.  Second, I will also summarize our new work on Latent Backdoors (CCS 2019), a stronger type of backdoor attack that is more difficult to detect and survives retraining in commonly used transfer learning systems. Latent backdoors are robust and stealthy, even against the latest detection tools (including neural cleanse).
Seminar

Network Function Virtualization: What, Why and How?

Ahmed E. Kamal, Department of Electrical and Computer Engineering, Iowa State University, Ames, IA, USA.
Sunday, November 24, 2019, 11:00
- 12:00
B1, L3, Conference Room 3119
Contact Person
Basem Shihada
The European Telecom Standards Institute (ETSI) introduced the concept of Network Function Virtualization (NFV) with the aim of efficient network architecture and network system operation. In traditional networks, network functions are implemented in dedicated physical machines which are designed for single functionalities. Network services have been provided by connecting these physical machines, so the network architecture has been highly rigid and hard to change. NFV environment provides a more flexible and scalable network configuration and implementation through the softwarization of physical network functions. Network functions are transformed to Virtual Machines (VMs) so that Virtualized Network Functions (VNFs) can be implemented in commodity servers built for common uses, including public clouds.
Graduate Seminar

Eliminating Waiting Time Inequities for Deceased Donor Kidney Transplantation in the USA

Dr. Joris van de Klundert, Professor of Operations Management, Prince Mohammad Bin Salman College (MBSC) of Business & Entrepreneurship
Monday, November 18, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
Contact Person
Michal A. Mankowski
This research formally models, analyzes and maximizes equity of transplant waiting times and probabilities using queuing theory and network flows, based on Rawls' theory of justice. The presented formal models address inequities resulting from blood type incompatibilities, which are interrelated to ethnic differences in patient and donor rates.
Graduate Seminar

Langevin Monte Carlo as an optimization algorithm

Monday, November 11, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
Contact Person
Peter Richtarik
Adil Salim is mainly interested in stochastic approximation, optimization, and machine learning. He is currently a Postdoctoral Research Fellow working with Professor Peter Richtarik at the Visual Computing Center (VCC) at King Abdullah University of Science and Technology (KAUST).
Workshop

Reproducibility in Computer Science - Part 3

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Wednesday, November 06, 2019, 16:00
- 17:00
Building 9, Level 3, Room 3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise.

We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Workshop

Reproducibility in Computer Science - Part 2

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Tuesday, November 05, 2019, 16:00
- 17:00
Building 9, Level 3, Room 3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise.

We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Seminar

Mixed graphical-basis models for large nonstationary and multivariate spatial data problems

Dr. William Kleiber, Associate Professor of Applied Mathematics, University of Colorado, USA
Tuesday, November 05, 2019, 14:00
- 15:00
Building 1, Level 4, Room 4102
Contact Person
Elmootazbellah Elnozahy
In this talk, we explore a graphical model representation for the stochastic coefficients relying on the specification of the sparse precision matrix. Sparsity is encouraged in an L1-penalized likelihood framework. Estimation exploits a majorization-minimization approach. The result is a flexible nonstationary spatial model that is adaptable to very large datasets.
PhD Dissertation Defense

Interactive High-Quality Visualization of Large-Scale Particle Data

Tuesday, November 05, 2019, 14:00
- 15:00
Building 2, Level 5, Room 5209
Contact Person
Mohamed Ibrahim
Large-scale particle data sets, such as those computed in molecular dynamics (MD) simulations, are crucial to investigating important processes in physics and thermodynamics. The simulated atoms are usually visualized as hard spheres with Phong shading, where individual particles and their local density can be perceived well in close-up views. However, for large-scale simulations with 10 million particles or more, the visualization of large fields-of-view usually suffers from strong aliasing artifacts, because the mismatch between data size and output resolution leads to severe under-sampling of the geometry.
Workshop

Prototyping Multiscale Cellular Visualization & Modeling Techniques for Hypothesis Generation, Communication & Learning

Graham Johnson, Allen Institute for Cell Science, USA
Tuesday, November 05, 2019, 09:00
- 10:00
B9, Lecture Hall 2, Room 2325

Abstract

The visual analysis, assembly, and

Workshop

Reproducibility in Computer Science - Part 1

Roy Maxion, Research Professor, Computer Science Department, Carnegie Mellon University
Monday, November 04, 2019, 16:00
- 17:00
Building 9, Level 3, Room 3223

Roy Maxion will give three lectures focusing broadly on different aspects of an increasingly important topic: reproducibility. Reproducibility tests the reliability of an experimental result and is one of the foundations of the entire scientific enterprise.

We often hear that certain foods are good for you, and a few years later we learn that they're not. A series of results in cancer research was examined to see if they were reproducible. A startling number of them - 47 out of 53 - were not. Matters of reproducibility are now cropping up in computer science, and given the importance of computing in the world, it's essential that our own results are reproducible -- perhaps especially the ones based on complex models or data sets, and artificial intelligence or machine learning. This lecture series will expose attendees to several issues in ensuring reproducibility, with the goal of teaching students (and others) some of the crucial aspects of making their own science reproducible. Hint: it goes much farther than merely making your data available to the public.

Graduate Seminar

Accelerating biomedical discovery with an Internet of FAIR data and services

Dr. Michel Dumontier, Distinguished Professor of Data Science at Maastricht University, The Netherlands
Monday, November 04, 2019, 12:00
- 13:00
Building 9, Level 2, Hall 1, Room 2322
Contact Person
Robert Hoehndorf
In this talk, I will discuss our work to create computational standards, platforms, and methods to wrangle knowledge into simple, but effective representations based on semantic web technologies that are maximally FAIR - Findable, Accessible, Interoperable, and Reuseable - and to further use these for biomedical knowledge discovery. But only with additional crucial developments will this emerging Internet of FAIR data and services enable automated scientific discovery on a global scale.
PhD Dissertation Defense

Machine Learning Models for Context Aware Recommendation Systems

Monday, November 04, 2019, 10:00
- 11:00
Building 3, Level 5 , Room 5209
Contact Person
Basmah Altaf
The goal of this thesis is to pave the way towards the next generation of recommendation systems tackling such real-world challenges to improve the user experience while giving good recommendations.