Charalampos Chelmis

University at Albany, The State University of New York | UAlbany · Department of Computer Science

Improving the homelessness system and evaluating the effectiveness of delivered services are critical to achieve optimal usage of limited social resources as well as to improve the outcomes of the homelessness system. In this context, an increasing number of data science and machine learning methods have been recently applied to the domain of homel...

This article shares the experiences and lessons learned from a community project that aims to develop a technology-based solution to improve communications between service users and service providers. Through this multi-year project in the Capital District of New York State, a team of social workers and engineers created a mobile app prototype base...

In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that is most informative for each test instance individually may not only improve prediction accuracy, but also the overall interpretability...

We present a prototype decentralized transactional platform designed to improve the transparency of homeless serving organizations and facilitate their accountability and oversight. In the proposed system, the complete history of transactions between organizations offering homelessness services (e.g., shelters, transitional housing) and individuals...

In a typical supervised machine learning setting, the predictions on all test instances are based on a common subset of features discovered during model training. However, using a different subset of features that is most informative for each test instance individually may not only improve prediction accuracy, but also the overall interpretability...

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data-driven approaches, with emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well-defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over...

In this paper, a dynamic instance-wise joint feature selection and classification framework during testing is presented. Specifically, the proposed framework sequentially selects features one at a time for each data instance, given previously selected features, and stops this process to classify the instance once it determines that including additi...

Homelessness presents a long-standing social problem for nearly every community across the world. A key goal of homelessness service provision is to reduce the number of individuals who experience repeated episodes of homelessness. The goal of this work is to determine the feasibility of an automated recommendation system designed to carefully matc...

In this paper, the problem of detecting accidents using speed sensors distributed spatially on a freeway is considered. Due to the significant impact of road accidents on health and development, early and accurate detection of accidents is crucial. To address this issue, a novel Bayesian quickest change detection formulation is introduced, which co...

This article examines service coordination patterns across various service areas in Albany, the capital city of the New York State. Based on 42 in-person interviews with executive directors at various human service agencies, inter-organizational network was constructed and analyzed. The network displayed sparse and multipolar connectivity, suggesti...

Joint feature selection and classification in an online setting is essential for time-sensitive decision making. However, most existing methods treat this coupled problem independently. Specifically, online feature selection methods can handle either streaming features or data instances offline to produce a fixed set of features for classification,...

Introduction. Cyberbullying, as a form of abusive online behavior, although not well-defined, is a repetitive process, i.e., a sequence of harassing messages sent from a bully to a victim over a period of time with the intent to harm the victim. Numerous automated, data-driven approaches have been developed for the automatic classification of cyber...

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data–driven approaches, with emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well–defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over...

Human service providers play a critical role in improving well–being in the United States. However, little is know about (i) how service seekers find the services they are looking for by navigating among available service providers, and (ii) how such organizations collaborate to meet human needs. In this paper, we report the first outcomes of our o...

The potentially detrimental effects of cyberbullying have led to the development of numerous automated, data-driven approaches, with emphasis on classification accuracy. Cyberbullying, as a form of abusive online behavior, although not well-defined, is a repetitive process, i.e., a sequence of aggressive messages sent from a bully to a victim over...

The Cambridge Handbook of Technology and Employee Behavior - edited by Richard N. Landers February 2019

Civic engagement platforms such as SeeClickFix and FixMyStreet have revolutionized the way citizens interact with local governments to report and resolve urban issues. However, recognizing which urban issues are important to the community in an accurate and timely manner is essential for authorities to prioritize important issues, allocate resource...

Accurate traffic accident detection constitutes a key step in a variety of processes ranging from improving road safety conditions and route navigation, to making informed decisions in urban planning. This paper proposes a Bayesian quickest change detection framework for accurate detection in near–real–time of freeway accidents based on speed senso...

In smart oilfields, a large volume of data is being generated related to assets, personnel, environment, and other production and business-related processes on a daily basis. Storing vast amounts of data is only justifiable if it leads to the discovery of actionable insights which can then be translated into improvements in operational efficiency a...

Understanding the spread of information in complex networks is a key problem. Content sharing in popular online social networks such as Facebook and Twitter has been well studied, however, the future trajectory of a cascade has been shown to be inherently unpredictable. Nonetheless, cascade virality has recently been studied as a classification pro...

Knowledge Graphs (KGs) are becoming essential to information systems that require access to structured data. Several approaches have been recently proposed, for obtaining vector representations of KGs suitable for Machine Learning tasks, based on identifying and extracting relevant graph substructures using uniform and biased random walks. However,...

Enormous amount of data from physical objects, such as devices comprising Internet of Things (IoT), is being made available through Web APIs on a daily basis. Manual discovery and integration of relevant data sources can be cumbersome. A unified view of relevant data sources is desirable for creating applications for monitoring and decision making....

The combination of data, semantics, and the Web has led to an ever growing and increasingly complex body of semantic data. Accessing such structured data requires learning formal query languages, such as SPARQL, which poses significant difficulties for non-expert users. To date, many interfaces for querying Ontologies have been developed. However,...

Detecting outliers and anomalies in data is vital in numerous applications in areas such as security, finance, health care, and online social media. Such dynamic systems can be modeled as graphs that change over time. Even though considerable work has been performed on finding points in time at which a network notably differs from its past, little...

Demand Response (DR) is a widely used technique to minimize the peak to average consumption ratio during high demand periods. We consider the DR problem of achieving a given curtailment target for a set of consumers equipped with a set of discrete curtailment strategies over a given duration. An effective DR scheduling algorithm should minimize the...

The accurate estimation of system state variables at buses in the power-grid is crucial for determining the operational state of the power system. Spoofing attacks on meters at buses can bypass bad data detectors in Supervisory Control and Data Acquisition (SCADA) systems and undetectably manipulate state estimates. Existing methods for protection...

Prosumers or proactive consumers are steadily on the rise in emerging Smart Grid systems. These consumers, apart from their traditonal role of using energy from the grid, also are actively involved in individually transferring stored energy from renewable sources such as wind and solar, to the grid. The large-scale integration of renewable generati...

Accurate estimation of complex voltage phase angles at buses in the power-grid is crucial for determining the operational state of the power system. Existing methods for protection of the state estimate of critical buses against data injection attacks focus on design time assuming a static set of critical buses. We formulate a set of optimal protec...

Subgraph isomorphism is a fundamental graph problem with many applications. Due to its NP-Hard nature, subgraph isomorphism in large dynamic graphs is considered as a challenging problem. In this paper, we present a distributed graph pruning algorithm (D-IDS) for dynamic graphs to enable efficient subgraph isomorphism. D-IDS continuously maintains...

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underly...

Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascade scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most “influential” nodes in the network. The majority of prio...

We present an integrated system that automatically collects historical and current data from heterogeneous sources, performs analytics to identify telltale signatures of Loss of Containment (LOC) events, and makes asset behavior predictions as an asset ages. Our semantically enhanced system of record stores and manages heterogeneous data introduced...

Several applications including community detection in social networks and discovering correlated genes involve finding large subgraphs of high density. We propose the problem of finding the largest subgraph of a given density. The problem is a generalization of the Max-Clique problem which seeks the largest subgraph that has an edge density of 1. W...

The study of information dissemination on a social network has gained significant importance with the rise of social media. Since the true dynamics are hidden, various diffusion models have been exposed to explain the cascading behavior. Such models require extensive simulation for estimating the dissemination over time. In an earlier work, we prop...

As Smart Grids move closer to dynamic curtailment programs, Demand Response (DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction...

The increasingly large number of sensors and instruments in the oil and gas industry, along with novel means of communication in the enterprise has led to a corresponding increase in the volume of data that is recorded in various information repositories. The variety of information sources is also expanding: from traditional relational databases to...

Often in marketing, political campaigns and social media, two competing products or opinions propagate over a social network. Studying social influence in such competing cascades scenarios enables building effective strategies for maximizing the propagation of one process by targeting the most "influential" nodes in the network. The majority of pri...

Accurate estimation and evaluation of consumption reduction achieved by participants during Demand Response is critical to Smart Grids. We perform an in-depth study of popular estimation methods used to determine the extent of consumption shedding during DR, using a real-world Smart Grid dataset from the University of Southern California campus mic...

Demand response (DR) is a technique used in smart grids to shape customer load during peak hours. Automated DR offers utilities a fine grained control and a high degree of confidence in the outcome. However the impact on the customer's comfort means this technique is more suited for industrial and commercial settings than for residential homes. In...

The use of AMI in Smart Grids has resulted in huge volumes of energy consumption data being collected. We design a provably efficient online clustering technique based on algorithmic theory to analyze high volume, high dimensional energy consumption data at scale, and on the fly. Unlike prior work, we study the consumption properties of the whole p...

The advent of smart meters and advanced communication infrastructures catalyzes numerous smart grid applications such as dynamic demand response, and paves the way to solve challenging research problems in sustainable energy consumption. The space of solution possibilities are restricted primarily by the huge amount of generated data requiring cons...

Existing Big Data streams coming from social and other connected sensor networks exhibit intrinsic interdependency enabling unique challenges to scalable graph analytics. Data from these graphs is usually collected on various geographically distributed data servers making it suitable for distributed processing on clouds. While numerous solutions fo...

The Oil & Gas industry always seeks to prevent loss of containment (LOC). To prevent such incidents, engineers rely on inputs from various asset databases and software tools to make important safety-related assessments and decisions on a daily basis. One cause of LOC in offshore platforms is external corrosion. The state of corroding assets is exte...

Applications in sustainability domains such as in energy, transportation, and natural resource and environment monitoring, increasingly use sensors for collecting data and sending it back to centrally located processing nodes. While data can usually be collected by the sensors at a very high speed, in many cases, it can not be sent back to central...

Big data applications such as in smart electric grids, transportation, and remote environment monitoring involve geographically dispersed sensors that periodically send back information to central nodes. In many cases, data from sensors is not available at central nodes at a frequency that is required for real-time modeling and decision-making. Thi...

Growing demand is straining our existing electricity generation facilities and requires active participation of the utility and the consumers to achieve energy sustainability. One of the most effective and widely used ways to achieve this goal in the smart grid is demand response (DR), whereby consumers reduce their electricity consumption in respo...

Popular social networking sites have revolutionized the way people interact on the Web, enabling rapid information dissemination and search. In an enterprise, understanding how information flows within and between organizational levels and business units is of great importance. Despite numerous studies in information diffusion in online social netw...

Regulating the power consumption to avoid peaks in demand is a known method. Demand Response is used as tool by utility providers to minimize costs and avoid network overload during peaks in demand. Although it has been used extensively there is a shortage of solutions dealing with real-time scheduling of DR events. Past attempts focus on minimizin...

Increasing instrumentation of the modem digital oilfield produces streams of data from sensors that monitor the functioning of different components in the field. This data should be converted to actionable information rapidly in order to respond to events as they happen or are predicted. The challenge is therefore to develop technologies that can p...

Smart grids are becoming popular with the advent of sophisticated smart meters. They allow utilities to optimize energy consumption during peak hours by applying various demand response techniques including voluntary curtailment, direct control and price incentives. To sustain the curtailment over long periods of time of up to several hours utiliti...

We describe a novel method for electricity load disaggregation based on the machine learning method of time series shapelets. We frame the electricity disaggregation problem as that of event detection and event classification from time series data. We use existing shapelet-based algorithms to separate appliance activity periods (caused by switching...

Recent years have seen an increasing interest in providing accurate prediction models for electrical energy consumption. In Smart Grids, energy consumption optimization is critical to enhance power grid reliability, and avoid supply-demand mismatches. Utilities rely on real-time power consumption data from individual customers in their service area...

Business processes are generally fixed and enforced strictly, as reflected by the static nature of underlying software systems and datasets. However, internal and external situations, organizational changes and various other factors trigger dynamism, which is reflected in the form of issues, complains, Q & A, opinions, reviews, etc., over a plethor...