dr. G. Sedrakyan (Gayane)

Publicaties

• 192399979 - Final Project BIT
• 194100060 - Master Thesis IE&M
• 201300058 - Research Topics BIT
• 201300059 - Internship BIT
• 202000434 - BSc Research Assignment
• 202000565 - Innovation & Entrepreneurship
• 202001464 - Thesis Preparation

• 192399979 - Final Project BIT
• 194100060 - Master Thesis IE&M
• 201300058 - Research Topics BIT
• 201300059 - Internship BIT
• 202000402 - Business Intelligence & Databases
• 202000434 - BSc Research Assignment
• 202000565 - Innovation & Entrepreneurship
• 202001068 - Business Intelligence and Databases
• 202001071 - Academic Skills M3
• 202001464 - Thesis Preparation
• 202001549 - LCAD

Current projects

           Principal researcher

• How educational feedback needs changed during the times of Covid pandemic and what are the long-term effects?  (BMS funding) While it is evident that digitalization will be pivotal for accomplishing a transition to post-pandemics educational environments, where hybrid classroom/campus uniting the physical and digital learning experiences will most likely define the new norms, the field lacks insights to guide informed decisions in the domain of feedback digitalization. Despite the importance of this transition world-wide, still questions such as “what is the type of digital feedback that worked best during lockdown education?”, “which new formats used by teachers proved effective among students?”, “are there preferences in these new formats/elements of feedback to continue even when the lockdown education disappears?” remain unanswered. 

• TeToMoCo: The goal of the project is three-fold: 1. TeToMoCo (Text-To-Model-To-Code) framework that combines the state-of-the-art natural language processing approaches and techniques for identifying potential architecture elements candidates out of business requirements articulated in natural language textual description. 2. A subsequent prototype implementation that can assist a knowledge construction process through (semi-) automatic generation and validation of UML models. 3. Automatic web application code generation (backend/frontend/UI) out of generated UML/XML models following principles of Model Driven Engineering.

          Collaborator

• High-tech & data-driven agri-food system of the future (4TU project)
• Next generation educational chatbots 

Recently completed research

• PROFEELEARN: Process-oriented assessment and feedback based on learning behavior/process data analytics grounded on the links between information/data analytics and learning sciences (Postdoctoral research funding)

• CITADEL H2020: CITADEL is a European H2020 Project involving twelve partners. These are research institutes, universities, public sector entities and IT companies from five different European countries. The project’s objective is to create an ecosystem of best practices for a transparent, innovative and cooperative public sector and to provides more efficient and inclusive tools to respond to citizen requirements. The CITADEL ecosystem combines and promotes a set of technologies (e.g. semantics, mobile, analytics, sentiment analysis, open linked data) to both empower Public Administrations (PAs) to improve their offering and the engagement of citizens, as well as to foster cooperation among PAs and users of public services in local, regional and national environments. Main contributions as a partner included: 1. an ecosystem architectural guidelines, 2. design and development of a semantic dashboard to support improving public services for e-government at EU public administration organizations.  

• HOBBIT H2020: Holistic Benchmarking of Big Linked Data aims at abolishing the barriers in the adoption and deployment of Big Linked Data by European companies, by means of open benchmarking reports that allow them to assess the fitness of existing solutions for their purposes. These benchmarks are based on data that reflects reality and measures industry-relevant Key Performance Indicators (KPIs) with comparable results using standardized hardware. Main contributions as a partner included: 1. benchmark on query answering features for live time series in the form of multidimensional interfaces 2. establishing a taskforce subgroup for Benchmarking under the umbrella of Big Data Value Association with the aim to provide a scalable and FAIR benchmarking platform for data-driven solutions with a focus on AI (especially ML) solutions, corresponding benchmarks, key performance indicators, benchmarking tools and services for the independent, repeatable and scalable benchmarking of data-driven (especially AI) technologies, detecting potential use cases and categories of users as well as potential synergies with existing benchmarking organizations.

Verbonden aan

• Faculty of Behavioural, Management and Social Sciences (BMS), Cognition, Data and Education (CODE)
• Faculty of Behavioural, Management and Social Sciences (BMS), Industrial Engineering & Business Information Systems (IEBIS)