Ahmed K Farahat

Ahmed K Farahat
Hitachi America, Ltd. · Industrial AI Lab

Ph.D.

About

49
Publications
9,943
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
1,036
Citations
Additional affiliations
November 2014 - May 2020
Hitachi America Ltd
Position
  • Group Leader
January 2013 - November 2014
University of Waterloo
Position
  • PostDoc Position
Education
September 2007 - December 2012
University of Waterloo
Field of study
  • Computer Engineering

Publications

Publications (49)
Conference Paper
Full-text available
Given a very large data set distributed over a cluster of several nodes, this paper addresses the problem of selecting a few data instances that best represent the entire data set. The solution to this problem is of a crucial importance in the big data era as it enables data analysts to understand the insights of the data and explore its hidden str...
Article
Full-text available
This paper defines a generalized column subset selection problem which is concerned with the selection of a few columns from a source matrix A that best approximate the span of a target matrix B. The paper then proposes a fast greedy algorithm for solving this problem and draws connections to different problems that can be efficiently solved using...
Article
Full-text available
The kernel k-means is an effective method for data clustering which extends the commonly-used k-means algorithm to work on a similarity matrix over complex data structures. The kernel k-means algorithm is however computationally very complex as it requires the complete data matrix to be cal- culated and stored. Further, the kernelized nature of the...
Article
Full-text available
The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on devel...
Article
Full-text available
The Nyström method is an efficient technique for obtaining a low-rank approximation of a large kernel matrix based on a subset of its columns. The quality of the Nyström approximation highly depends on the subset of columns used, which are usually selected using random sampling. This paper presents a novel recursive algorithm for calculating the Ny...
Preprint
Several machine learning and deep learning frameworks have been proposed to solve remaining useful life estimation and failure prediction problems in recent years. Having access to the remaining useful life estimation or likelihood of failure in near future helps operators to assess the operating conditions and, therefore, provides better opportuni...
Preprint
Traditionally, fault detection and isolation community has used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many comple...
Preprint
Full-text available
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time series models, for a considered data analytics task, provides useful guidance on model selection for data analytics practitioners. Data scarcity is a universal issue that occ...
Article
Time series data have grown at an explosive rate in numerous domains and have stimulated a surge of time series modeling research. A comprehensive comparison of different time series models, for a considered data analytics task, provides useful guidance on model selection for data analytics practitioners. Data scarcity is a universal issue that occ...
Preprint
Full-text available
Deep learning classifiers are assisting humans in making decisions and hence the user's trust in these models is of paramount importance. Trust is often a function of constant behavior. From an AI model perspective it means given the same input the user would expect the same output, especially for correct outputs, or in other words consistently cor...
Article
Full-text available
Traditionally, fault detection and isolation community have used system dynamic equations to generate diagnosers and to analyze detectability and isolability of the dynamic systems. Model-based fault detection and isolation methods use system model to generate a set of residuals as the bases for fault detection and isolation. However, in many compl...
Article
Full-text available
Many techniques for prognostics depend on estimating then forecasting health indicators that reflect the overall health or performance of an asset. For vibration data, health indicators are typically calculated by combining various vibration measurements along with derived features extracted from time, frequency or time-frequency domain analysis. H...
Preprint
Prognostics is concerned with predicting the future health of the equipment and any potential failures. With the advances in the Internet of Things (IoT), data-driven approaches for prognostics that leverage the power of machine learning models are gaining popularity. One of the most important categories of data-driven approaches relies on a predef...
Chapter
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the equipment. In recent years, solutions for failure prediction have evolved from building complex phy...
Preprint
Prognostics and Health Management (PHM) is an emerging engineering discipline which is concerned with the analysis and prediction of equipment health and performance. One of the key challenges in PHM is to accurately predict impending failures in the equipment. In recent years, solutions for failure prediction have evolved from building complex phy...
Article
Full-text available
The failures among connected devices that are geographically close may have correlations and even propagate from one to another. However, there is little research to model this prob- lem due to the lacking of insights of the correlations in such devices. Most existing methods build one model for one de- vice independently so that they are not capab...
Preprint
Remaining Useful Life (RUL) of an equipment or one of its components is defined as the time left until the equipment or component reaches its end of useful life. Accurate RUL estimation is exceptionally beneficial to Predictive Maintenance, and Prognostics and Health Management (PHM). Data driven approaches which leverage the power of algorithms fo...
Chapter
Predictive Maintenance (PdM) is gaining popularity in industrial operations as it leverages the power of Machine Learning and Internet of Things (IoT) to predict the future health status of equipment. Health Indicator Learning (HIL) plays an important role in PdM as it learns a health curve representing the health conditions of equipment over time,...
Preprint
Full-text available
One of the key challenges in predictive maintenance is to predict the impending downtime of an equipment with a reasonable prediction horizon so that countermeasures can be put in place. Classically, this problem has been posed in two different ways which are typically solved independently: (1) Remaining useful life (RUL) estimation as a long-term...
Article
Full-text available
Data-driven Remaining Useful Life (RUL) estimation for systems with abrupt failures is a very challenging problem. In these systems, the degradation starts close to the failure time and accelerates rapidly. Normal data with no sign of degradation can act as noise in the training step, and prevent RUL estimator model from learning the degradation pa...
Article
Full-text available
Model-based diagnosis methods rely on a model that defines nominal behavior of a dynamic system to detect abnormal behaviors and isolate faults. On the other hand, data-driven diagnosis algorithms detect and isolate system faults by operating exclusively on system measurements and using very little knowledge about the system. Recently, several rese...
Article
Full-text available
In today’s dynamic environment, there naturally exist two types of novelties: emerging and evolving. Emerging novelties are represented by concepts which are completely different from previously seen instances, while evolving novelties are characterized by relatively new aspects of existing concepts. Most existing algorithms for novelty detection f...
Article
OVERVIEW: Through the proliferation of sensors, smart machines, and instrumentation, industrial operations are generating ever increasing volumes of data of many different types and our customers are demanding solutions that provide business value over this collected data. In our interactions with customers across verticals, we have discovered that...
Conference Paper
Full-text available
The Kernel Mean Matching (KMM) is an elegant algorithm that produces density ratios between training and test data by minimizing their maximum mean discrepancy in a kernel space. The applicability of KMM to large-scale problems is however hindered by the quadratic complexity of calculating and storing the kernel matrices over training and test data...
Conference Paper
With the massive growth of social data, a huge attention has been given to the task of detecting key topics in the Twitter stream. In this paper, we propose the use of novelty detection techniques for identifying both emerging and evolving topics in new tweets. In specific, we propose a locally adaptive approach for density-ratio estimation in whic...
Conference Paper
Short documents are typically represented by very sparse vectors, in the space of terms. In this case, traditional techniques for calculating text similarity results in measures which are very close to zero, since documents even the very similar ones have a very few or mostly no terms in common. In order to alleviate this limitation, the representa...
Article
Full-text available
Dictionary learning and sparse representation (DLSR) is a recent and successful mathematical model for data representation that achieves state-of-the-art performance in various fields such as pattern recognition, machine learning, computer vision, and medical imaging. The original formulation for DLSR is based on the minimization of the reconstruct...
Conference Paper
Full-text available
Detecting topics in Twitter streams has been gaining an increasing amount of attention. It can be of great support for communities struck by natural disasters, and could assist companies and political parties understand users’ opinions and needs. Traditional approaches for topic detection focus on representing topics using terms, are negatively aff...
Patent
Full-text available
A method for learning a process behavior model based on a process past instances and on one or more process attributes, and a method for detecting an anomalous process using the corresponding process behavior model.
Article
Full-text available
In today's information systems, the availability of massive amounts of data necessitates the development of fast and accurate algorithms to summarize these data and represent them in a succinct format. One crucial problem in big data analytics is the selection of representative instances from large and massively-distributed data, which is formally...
Conference Paper
The Kernel Mean Matching (KMM) algorithm is a mathematically rigorous method that directly weights the training samples such that the mean discrepancy in a kernel space is minimized. However, the applicability of KMM is still limited, due to the existence of many parameters that are difficult to adjust. This paper presents a novel method that autom...
Article
The covariate shift is a challenging problem in supervised learning that results from the discrepancy between the training and test distributions. An effective approach which recently drew a considerable attention in the research community is to reweight the training samples to minimize that discrepancy. In specific, many methods are based on devel...
Article
Reducing the dimensionality of the data has been a challenging task in data mining and machine learning applications. In these applications, the existence of irrelevant and redundant features negatively affects the efficiency and effectiveness of different learning algorithms. Feature selection is one of the dimension reduction techniques, which ha...
Conference Paper
In data mining applications, data instances are typically described by a huge number of features. Most of these features are irrelevant or redundant, which negatively affects the efficiency and effectiveness of different learning algorithms. The selection of relevant features is a crucial task which can be used to allow a better understanding of da...
Article
Full-text available
Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic...
Conference Paper
Full-text available
Different document representation models have been pro-posed to measure semantic similarity between documents using corpus statistics. Some of these models explicitly esti-mate semantic similarity based on measures of correlations between terms, while others apply dimension reduction tech-niques to obtain latent representation of concepts. This pa-...
Conference Paper
Document clustering algorithms usually use vector space model (VSM) as their underlying model for document representation. VSM assumes that terms are independent and accordingly ignores any semantic relations between them. This results in mapping documents to a space where the proximity between document vectors does not reflect their true semantic...

Network

Cited By