EDUCATIONAL DATA
SCIENCE

What We Do

Welcome to AI Lab

The AI Lab at ITU leverages the power of AI & Machine Learning for Social Goods to unlock the opportunities for positive impact, learn the use of ICT for the improved efficiency and sustainability of smart cities, implement NLP techniques to infer and analyze human language, process that textual information and make it accessible to the users, study educational data to have central control over the complete student record for evaluating the performance, deploy deep learning models to provides improved performance over the traditional models, and to extracts the high-level abstract features for classification.

AI Lab is concerned with the technology for social good, business synergies and growth by tapping into the power of machine learning and emerging big data technologies.

Flagship Project

AI Center for Emerging Big Data Applications has been working on several research projects. Among these programs, a key project is the ITU Quality Research Ranking of Muslim World, The 2019 version of ITU-QRR provides the key quality dimension of research performance by more than 450 universities and institutes of more than 30 countries from the Muslim World, using Scopus database. The results provide useful information to the scientific community, as well as to the higher education policy makers.Please find the details at the following URL: http://rankings.itu.edu.pk/

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Projects

Deep Stylometry and Lexical & Syntactic Features based Author ...


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In this paper, we address the problem of author attribution through unsupervised clustering using lexical and syntactic features and novel deep learning based Stylometric model. For this purpose, we download all available 158918 publications accessible till 1 July 2015 from PLOS.org – an open access digital repository of full text publications.

Altmetrics: A new way to measure social impact of scientific literature

altmatrics
In this project we measure the impact of scientific publications by deploying altmetrics indicators using the data from Google Scholar, Twitter, Mendeley, Facebook, Google-plus, CiteULike, Blogs and Wiki To capture the social impact of scientific publications,.

Detecting Target Text related to Algorithmic Efficiency in Full ..


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In this paper,We are observing an exponential growth of scientific literature since the last few decades. Tapping on the advancement of web-enabled tools and technologies, millions of articles are stored and indexed in the digital libraries. Among this archived scientific literature.

Identifying Important Citations using Contextual Information from Full Text

NEW

In this project we address the problem of classifying cited work into important and non-important to the developments presented in a research publication. This task is vital for the algorithmic techniques that detect and follow emerging research topics and to qualitatively measure the impact of publications in increasingly growing scholarly big data.

A bibliometric study of research activity in ASEAN related to the EU in FP7 priority areas

5 In this project Two relevant recent developments in the area of science and technology (S&T) and related policy-making motivate this article: first, bibliometric data on a specific research area’s performance becomes an increasingly relevant source for S&T policymaking and evaluation.

Small-world phenomenon of keywords network based on complex network

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This project Based on the network comprised of 111,444 keywords of library and information science that are extracted from Scopus, and taken into consideration the major properties of average distance and clustering coefficients, the present authors …

A bibliometric study of the world’s research activity in sustainable development and its sub-areas using scientific literature

7This project presents a bibliometric study of the world’s research activity in Sustainable Development using scientific literature. The study was conducted using data from the Scopus database over the time period of 2000–2010.

A bibliometric assessment of scientific productivity and international collaboration of the Islamic World in science and technology (S&T) areas

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 This project analyzes scientific research landscape of the Islamic World in order to access the research productivity, scholarly impact and international collaborations across all Science and Technology (S&T) areas over the time period of 2000–2011, using the Scopus database.

Robust hybrid name disambiguation framework for large databases

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In many databases, science bibliography database for example, name attribute is the most commonly chosen identifier to identify entities. However, names are often ambiguous and not always unique which cause problems in many fields..

Tapping into intra- and international collaborations of the Organization...

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This project analyzes the intra- and international collaboration of 11 member states of the Organization of Islamic Cooperation (OIC) in science and technology (S&T) disciplines in the period 1996–2010.by applying various bibliometric indicators along with publication and citation counts and our proposed .

Explaining the Transatlantic gap in research excellence

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The research has shown that European universities, despite their history and overall scientific production, fail to compete in both quality and volume faced with increasing competition at the top. We also show that in addition to the competition from the USA, there is also the competition from follower countries in Asia

Measuring international knowledge flows and scholarly...

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This project studies Intra and international collaboration of eleven member states of the Organization of Islamic Cooperation (OIC) in Science and Technology disciplines, by applying various bibliometric indicators, including our newly proposed ACS index that measures the intra collaboration strength of a region.

Knowledge Flows by Citation Context Analysis

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In this project we measure the knowledge flows among the countries by analyzing publication and citation data. We argue that not all citations are equally important, therefore, in contrast to existing techniques that utilize absolute citation countsto quantify knowledge …

Analyzing knowledge flows of scientific literature through semantic

In this project we present a new technique to semantically analyze knowledge flows across countries by using publication and citation data using our proposed topic model with distance matrix an extension of classic Latent Dirichlet Allocation model, .

A comprehensive examination of the relation of three citation-based ...

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Increasing investments in higher education and research, particularly in cutting edge science and technology, coupled with increasing public demand for accountability have driven the need for objective and quantitative measures of research performance. .

ITU Quality Research Rankings for Pakistani Universities & Institutes

itu
The ITU Quality Research Rankings provide the measure of key dimensions of research performance: output, scholarly impact, volume and quality. The publication based indicators measure output; the citation indicators measure scholarly impact; and the h-index combines both.

Creation and the consumption of scientific knowledge across regions.

dfffIn this project we present an innovative research that integrates the creation and the consumption of scientific knowledge across regions. From a human behavior point of view this is significant, since it provides an advanced decision making layer for bringing together researchers from all over the world

The global research benchmarking system

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To support strategic decision making, universities require research benchmarking data that is sufficiently fine-grained to show variation among specific research areas and identify focused areas of excellence; is objective and verifiable; and provides meaningful comparisons ..  .

Leadership

Saeed Ul Hassan
Director, AI Lab

AI for Social Good, Scientometrics, Altmetrics, Educational Data Science, Applied Machine Learning

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Peter Haddawy
Scientific Advisor

Decision-Theoretic Problem Solving, Medical and Public Health Informatics, AI, Scientometrics

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Raheel Nawaz
Scientific Director

Applied Machine Learning, Scientometrics, AI, Digital Technologies, Information Mining, Industry 4.0

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Anna Visvizi
International Collaborator

AI, ICT, Smart Cities, International Political Economy

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Timothy D. Bowman
International Collaborator

Altmetrics, Scholarly Communication, Sociology, Impression Management

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Matthew Shardlow
International Collaborator

Natural Language Processing, Machine Learning, Lexical Simplification, Text Mining

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Core Research Team

Iqra Safder
Researcher

Text Mining, Information Retrieval, Machine Learning, Deep Learning

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Anwar Said
Researcher

Social Network Analysis, Representation Learning, Graph Theory

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Hajra Waheed
Researcher

Educational Data Science, Data Science, Learning Analytics

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Farooq Zaman
Researcher

Text Summarization, Text Simplification. Machine Translation, Deep Learning

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Sehrish Iqbal
Researcher

Educational Data Science, Text Mining, Machine Learning, Citation Context Analysis

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Momin Ali
Research Associate

Deep Learning, Data Mining, Natural Language Processing

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Hadia Irshad
Research Associate

Deep Learning, Information Retrieval, Big Data, Text Mining

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Mahrukh
Research Associate

NLP, Deep Learning, Educational Data Mining, Information Retrieval

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Muhammad Sohaib Khalid
Research Associate

Information Retrieval, Recommendation Systems, Machine Learning, Text Mining

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AI Lab Team

Alumni

Raheem Sarwar
Research Associate

Scientific Data Management, Large-Scale Machine Learning, Scientometrics

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Mubashir Imran
Research Associate

Algorithms, Graph Theory, Data Science, Machine Learning, Information Retrieval

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Junaid Sarfraz
Research Associate

Application Development, Data Mining, Information Retrieval, Machine Learning

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Syed Uzair
Research Associate

Scientometrics, Altmetrics, Machine Learning, AI

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Zunaira Jamil
Research Associate

Scientometrics, Applied Machine Learning, Deep Learning

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Awais Asghar
Research Associate

Application Development, Data Scrapping and Crawling

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Imran Aslam
Research Associate

User-Centered Design, Interaction Design, Accessibility, HTML/CSS, Internet Marketing

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Tajwar Nasir
Intern

Computational Semantics, Cognitive Computing, Algorithmic Complexity

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Muhammad Junaid Pahat
Research Associate

Data Sciences, Text Mining, Deep Learning, Data Warehouse

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Hafsa Batool
Research Associate

Machine learning, Deep learning, Data Science, Text Summarization

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Salman Ahmed
Research Associate

Computer Vision, Deep Learning, Robotics and Embedded Systems

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Shafaq Malik
Research Associate

Data Science, Big Data Analytics, Machine Learning, AI, Deep Learning

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Ali Shahid
Research Associate

Information Retrieval, Text Mining, Big Data, Machine Learning

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Rutaba Niazi
Research Associate

Natural Language Processing, Deep Learning, Big Data

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Muhammad Rauf Tabassam
Research Associate

Deep Learning, Data Mining, Text Analysis, Machine Learning

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Muhammad Burhan
Research Associate

Deep Learning, Text Analysis, Data-Driven Business Strategies using Machine Learning

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Fatima Farooq Bhatti
Research Associate

Applied Machine Learning, Text Mining, Information Retrieval 

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Scientists | Researchers | Analysts

Fundings

  1. Building partnerships for hemophilia awareness and treatment provision [ May 1, 2017 to April 30, 2019 ]  Funds = $76070, Funding Organization: Novo Nordisk Foundation.
  2. Ranking for Muslim World Universities, ITU Quality Research Rankings (ITU-QRR) [ July 1, 2016 to June 30, 2017 ]  Funds = ITU Funded, Funding Organization: Information Technology University
  3. ITU Quality Research Rankings (ITU-QRR)  [ July 1, 2015 to June 30, 2016 ]  Funds = ITU Funded, Funding Organization: Information Technology University
  4. Dragon – Sustaining Technology and Research (EU-China Collaboration) [ July 1, 2013 to June 30, 2015 ]  Funds = $20000, Funding Organization: European Commissione
  5. UNESCO UIS – Higher Education, Science and Technology in East and South Asia [ Jan 1, 2012 to Dec 30, 2012 ]  Funds = $30000, Funding Organization: UNESCO
  6. Global Research Benchmarking System (GRBS) [ Jan 1, 2011 to Dec 30, 2013 ]  Funds = $60000, Funding Organization: Elsevier

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