About

Nil Hande Ergin is an Associate Professor of Systems Engineering and the Professor-in-Charge of the Master of Engineering in Systems Engineering at Penn State Great Valley. Prior to joining Penn State in 2009, she worked within the Research Institute for Manufacturing and Engineering Systems at the University of Texas at El Paso and was a Postdoctoral Fellow at the University of Missouri-Rolla. She earned her Ph.D. in systems engineering and M.S. in engineering management from the University of Missouri-Rolla, and holds a B.S. in environmental engineering from Istanbul Technical University, Turkey. Her teaching involves systems engineering, systems verification, validation and testing, requirements engineering, and systems and software architecture. Her research interests include system of systems engineering, complex adaptive systems, model-based systems engineering, and multi-agent systems. Dr. Ergin is affiliated with the Systems Engineering Research Center (SERC), a DoD funded University Affiliated Research Center, where she served as the investigator for Penn State University in collaborative research on modeling aspects of system of systems acquisition. She is a member of IEEE and INCOSE and has received awards such as the Research and Scholarship Excellence Award at Penn State Great Valley and recognition for her publications at various conferences.

Research topics

• Computer Science
• Political Science
• Artificial Intelligence
• Computer Security
• World Wide Web
• Data science
• Knowledge management
• Programming language
• Internet privacy
• Mathematics education
• Pedagogy

Selected publications

• Analysis of IoT Privacy Policies in Smart Transportation Systems

  Conference on systems engineering research series · 2024-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Toward Deriving a Digital Ontology for Systems Engineering and Acquisition Groups

  Conference on systems engineering research series · 2024-01-01

  book-chapter1st authorCorresponding
  PublisherDOI

• Knowledge elicitation methodology for evaluation of Internet of Things privacy characteristics in smart cities

  Systems Engineering · 2023 · 9 citations

  1st authorCorresponding
  • Computer Science
  • Computer Security
  • Computer Science

  Abstract One of the impediments to transforming urban cities into smart cities is the security and privacy concerns that arise due to use of Internet of Things (IoT) devices in various smart city applications. While IoT device vendors publish their security and privacy policies, manual evaluation of these policies is tedious and prone to misinterpretation as there is a lot of variability in the language used across IoT vendors. Local administrations and policy analysts are faced with understanding the implications of integrating IoT devices with differing security and privacy characteristics but lack methods that support them in analysis of privacy characteristics from a holistic perspective. In this paper, a methodology for knowledge elicitation from textual information is outlined to evaluate privacy characteristics of IoT devices. The methodology includes natural language processing and deep learning techniques to evaluate the relevance of IoT privacy policies to the National Institute of Standards and Technology (NIST) security and privacy framework 5 . Based on the analysis, text similarity scores are calculated for each IoT privacy policy document and each section of the policy document is labeled to NIST categories and functions. Analysis of these resulting labels and scores helps analysts to gain insights on each privacy policy as well as provide a holistic perspective of the privacy characteristics of IoT devices used in smart city applications. For example, all the policy documents used in the study talk about Protect domain and half of the documents cover Detect domain. However, most of the policies contain gaps regarding the Identify , Respond , and Recover domains. The study has implications for policy analysts, IoT vendors, and smart city administrators in terms of understanding the privacy gaps in IoT devices with respect to the NIST framework which can ultimately support policy alignment to address privacy concerns for smart cities.

  PublisherOA PDFDOI

• Best Fits, Dark Horses, and Cognitive Style: Investigating Differences in Design Solution Perceptions

  2023-10-29

  article

  Abstract In industry, engineering design teams can be asked to produce design solutions that clearly follow given specifications and constraints of a problem (i.e., Best Fit solutions), or they may be encouraged to provide higher risk design solutions that challenge those constraints, but offer other potential rewards (i.e., Dark Horse solutions). This study utilized a self-assessment tool to investigate designers’ perceptions of their teams’ Best Fit and Dark Horse solutions. Kirton’s Adaption-Innovation theory of cognitive style provided the framework to explore the impacts of cognitive style on design solution perceptions. The study involved 17 design teams of 3–5 individuals (64 participants) from five different professional organizations, with each team generating one Best Fit solution and one Dark Horse solution in response to the same design prompt. Participants were then asked to place their team’s Best Fit and Dark Horse solutions onto a “FUN diagram,” which is a ternary-style triangular diagram where the vertices correspond to Feasibility, Usefulness, or Novelty, respectively. The analysis of the responses showed that most adaptive and innovative individuals held distinct perceptions of their Best Fit and Dark Horse solutions, as reflected by their FUN diagram placements. While Best Fit solutions were more often perceived as being Feasible or Neutral, Dark Horse solutions were perceived as being Novel. More adaptive individuals perceived their Best Fit solutions as Feasible, whereas more innovative individuals perceived Best Fit solutions as Neutral. However, there was no apparent relationship between cognitive style and Dark Horse solution perceptions. Understanding more about how individuals perceived their Best Fit and Dark Horse solutions can enable engineering educators and industry practitioners to identify ways to support designers and teams more effectively.

  PublisherDOI

• An Integrative View of Teams: Team Feedback Dashboards

  2022-08-14

  article1st authorCorresponding

  Abstract In this research-to-practice paper, we present parts of a visual team feedback dashboard generated for 12 industry engineering design teams from various technical fields. All teams were asked to generate conceptual prototypes in response to the same design prompt. Multiple streams of data were collected and analyzed to create feedback dashboards for these engineering design teams. The feedback dashboard aims to capture an integrative view of teams by providing feedback on three levels, including design outcome level, team level, and personal level. The study results have implications for educators, practitioners, and team leaders in terms of stimulating discussion among team members, reinforcing future team behavior, and extracting strategies to improve future team performance, ultimately leading towards more engaged teams and a more highly skilled workforce.

  PublisherDOI

• Semantic Fusion with Deep Learning and Formal Ontologies for Evaluation of Policies and Initiatives in the Smart City Domain

  Applied Sciences · 2021-10-26 · 5 citations

  articleOpen access1st authorCorresponding

  Decision makers and policy analysts at different administrative levels often lack a holistic view of the problem as there are semantic variations in policy documents due to domain-specific content. For example, smart city initiatives are derived from national and international initiatives which may influence the incentives for local participants, but local initiatives reflect the local contextual elements of the city. Balanced assessment of smart city initiatives should include a systemic evaluation of the initiatives at multiple levels including the city, the country in which the city resides as well as at international level. In this paper, a knowledge elicitation methodology is presented for multi-granularity evaluation of policies and initiatives. The methodology is demonstrated on the evaluation of smart city initiatives generated at different administrative levels. Semantic networks are constructed using formal ontologies and deep learning methods for automatic semantic evaluation of initiatives to abstract knowledge found in text. Three smart city initiatives published by different administrative levels including international, national, and city level are evaluated in terms of relevance, coherence, and alignment of multi-level smart city initiatives. Experiments and analysis ultimately provide a holistic view of the problem which is necessary for decision makers and policy analysts of smart cities.

  PublisherOA PDFDOI

• Smart City Document Evaluation to Support Policy Analysis

  2022 IEEE International Systems Conference (SysCon) · 2020 · 3 citations

  1st authorCorresponding
  • Computer Science
  • Computer Science
  • Data science

  Local administrations face many challenges in their shift to smart city development. One of the main challenges is in the area of governance and identification of policy strategies that ensure successful integration of new technologies. In this article we explore a methodology for knowledge elicitation from textual information to identify smart city development trends. Our goal is to evaluate and predict the relevance of documents, especially press releases, to different areas of smart city development. Our methodology includes natural language processing techniques to possibilistically evaluate the relevance of a text to several smart city development areas. Relevant concepts are further associated to domain ontologies and expanded using data fusion techniques to evaluate the relevance of information in text to the areas of smart city development. We test our approach on a large number of documents from the smart city development community.

  PublisherDOI

• Applications of Natural Language Techniques to Enhance Curricular Coherence

  Procedia Computer Science · 2020 · 7 citations

  Senior authorCorresponding
  • Computer Science
  • Computer Science
  • Mathematics education

  An effective and highly efficient curriculum is the foundation of an optimal content delivery to students and may lead to the success of the academic program. Ensuring the effectiveness of curricular processes goes beyond the successful design of individual courses and it is subject to careful planning in accordance to appropriate policies and procedures that enable a high level of curriculum coherence. Such curriculum can guarantee that students are presented with a complete set of interrelated topics related to the area of study that can enable students to connect topics in individual courses and apply them in professional settings. The goal of this article is to evaluate the curriculum coherence of an Information Science program using ontologies and natural language processing techniques. Curricular coherence is evaluated using a possibilistic approach which evaluates academic gaps and overlaps that exist in the core courses offered in the degree. The use of ontologies enables us to perform a qualitative study of curricular coherence at different levels of ontological abstraction using mereotopological principles. The knowledge inferred from this process can be used at the universities to optimize their course offering and content for better academic content.

  PublisherDOI

• Multi-level Evaluation of Smart City Initiatives Using the SUMO Ontology and Choquet Integral

  Smart innovation, systems and technologies · 2020-05-29 · 2 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Adaptability, Agility, and Resilience Toolset

  Auerbach Publications eBooks · 2019-10-30 · 1 citations

  book-chapter1st authorCorresponding

  This chapter provides a basis for project managers and engineers of today’s complex systems in terms of understanding three important system properties: adaptability, resilience, and agility. Adaptability is a system property that deals with the changes and uncertainty in operational environments. When considering adaptability, several concepts should also be considered to deal with unpredictable events. Principles of self-organization is a key concept for engineering of adaptiveness. The most important role of a project manager is to manage the project from inception to deployment by ensuring that the project stays within budget and is delivered on time and with high quality. While agility is embraced widely in software development, other industries also apply agile principles. Resilience definitions vary depending on the context. For some systems, resilience includes returning to the original steady state; for other systems, resilience means rapidly maintaining an acceptable level of operation after a disturbance; and for other systems, resilience means self-organization and adaptation.

  PublisherDOI

Frequent coauthors

• Adrian S. Barb

  11 shared

• Ci̇han H. Dağli

  11 shared

• Colin J. Neill

  Pennsylvania State University

  10 shared

• Raghvinder S. Sangwan

  Carnegie Mellon University

  9 shared

• Paulette Acheson

  4 shared

• Satish Srinivasan

  Pennsylvania State University

  3 shared

• Kathryn Jablokow

  Pennsylvania State University

  3 shared

• John M. Colombi

  U.S. Air Force Institute of Technology

  2 shared

Awards & honors

• 2015–2016 Research and Scholarship Excellence Award, Penn St…