Department of Computer Science

Dr. Taimoor Khan

Dr. Taimoor Khan

Assistant Professor
(HEC Approved)
Ext.
taimoor.khan@nu.edu.pk
Office No , Academic, 160 Industrial Estate, Hayatabad, Peshawar

I am working as an Asst. Professor at FAST-NUCES Peshawar since August 2014. My research focus is on information extraction from text documents by exploring their semantic and contextual structures. I am working with knowledge-based models that can transfer their knowledge across a sequence of learning tasks. This broadly lies in the field of computational linguistics.

Education

  • Ph.D. (Computer Science), Bahria University Islamabad Campus, Pakistan (2018)
  • M.S (Information Technology), Swinburne University Of Technology, Australia (2010)
  • BS (Computer and Information Sciences), Pakistan Institute of Engineering & Applied Sciences (PIEAS), Pakistan (2007)
  • Asst. Professor (Computer Science Dept) at FAST-NUCES Peshawar. (Aug2014 - present)
  • Lecturer (Computer Science Dept) at COMSATS IIT Attock. (Dec 2011 - Jul 2014)
  • Web Developer (IT Section) at MediaEdge Source (e-Magazine publishing), Melbourne. (May 2011 - Nov 2011)
  • Web Developer (IT Section) at NetworkVideos (Video retail business), Melbourne. (Feb2011 - Apr2011)
  • Web Developer (IT Section) at StandoutVideo (Ads & video CV production), Melbourne. (Sept2010 - Jan2011)
  • Web Developer (IT Section) at JinTech (e-Solution providing company), Islamabad. (Jul2007 - Nov2007)

Research Interests

  • Research Interests
  • Computational linguistics, NLP, Knowledge-based systems, adaptive models, machine learning, text analysis, topic modeling, sentiment and social behavior analysis.
  • Journal Publication
  • 1. Khan, M. T., Azam N., Khalid S., and Yao J. “A Three-way Approach for Learning Rules in Automatic Knowledge-based Topic Models”, International Journal ofApproximate Reasoning, Vol-82, pages 210-226, 2017, 2.845IF
  • 2. Abbas S. M., Riaz M. U., Rauf A., Khan M. T. and Khalid S., “Improved Contextaware YouTube Recommender System with User Feedback Analysis”, Bahria University Journal of Information and Communication Technologies, Vol-10 (II), pages 1-8, 2017, HEC-Y Category
  • 3. Durrani, M. Y., Khan S., Khan M. T., Ali A., Ali M. and Khalid S., “Signature Identification Through Decision Envelope a Novel Approach.”, Journal of Computationaland Theoretical Nanoscience 14.2 (2017): 1204-1209 pages, 2017. 1.3IF
  • 4. Khan, M. T., Durrani M. Y., Khalid S., and Aziz F., “Online Knowledge-BasedModel for Big Data Topic Extraction,”, Computational Intelligence and Neuroscience, vol. 2016, Article ID 6081804, 10 pages, 2016. doi:10.1155/2016/6081804 (1.215IF)
  • 5. Khan, M. T., Durrani M. Y., Khalid S., Aziz, F., “Lifelong aspect extraction frombig data: knowledge engineering”, Complex and Adaptive Systems Modeling 4.1 (2016):1. (ISI Indexed)
  • 6. Khan M. T., Durrani M. Y., Ali A., Inayat I., Khalid S., Khan K., “Sentiment Analysis and the complex Natural Language”, Complex and Adaptive Systems Modeling,4(1), 2016. (ISI Indexed)
  • 7. Khan M. T., Durrani M. Y., Ali A., Inayat I., Khalid S., Khan K., “Sentiment Analysis and the complex Natural Language”, Complex and Adaptive Systems Modeling,4(1), 2016. (ISI Indexed)
  • 8. Khan M.T., Durrani M. Y., Ali A., Khan K., Khalid S., “Aspect-based SentimentAnalysis on Large-scale data: Topic Models are the Preferred Solution”, BUJICT, 8(2), 2015. (HEC Recognized Cat-Y)
  • 9. Khan M. T., Khalid S. “Sentiment Analysis for health care”, International Journal of Privacy and Health Information Systems, 3(2), pg: 80-94, 2015. (International Journal)
  • 10. Khan, M. T. and Usman A., “Technique detection software for Sparse Matrices.”, Annals Computer Science Series, 7(2), pg:57-66, 2009.
  • 11. Khan M. T., Ali A., Durrani M.Y., and Siddiqi I. “Survey of holistic crowd analysis models”, Journal of Computer Sciences and Communication, 1(1), pg: 1-9, 2015.
  • Conference Publications
  • 1. Khan, M. T., Khalid S. and Aziz F., “Graph Clustering Based Size Varying Rules for Lifelong Topic Modeling”, 7th International Conference on Computer Technology and Science, 7-9th December, ICCTS (Ho Chi Minh City, Vietnam) 2018
  • 2. Khalid, S., and Khan, M. T.. “A Novel Opinion Reason Mining Framework Exploiting Linguistic Associations”, 6th IEEE International Conference on Advances in Computing, Communication and Information Technology, 28-29th April, CCIT (Zurich, Switzerland) 2018
  • 3. Khalid, S., and Khan, M. T.. “Opinion Reason Mining: Implicit Aspects beyond Implying aspects”, 21st Saudi Computer Society - National Computer Conference, 25-26th April, SCS-NCC (Riyadh, Saudi Arabia) 2018
  • 4. Khan, M. T., and Khalid S., “Trends and Challenges in Lifelong Machine Learning opic Models”, 2nd IEEE International Conference on Computing, Electronics and Electrical Engineering, 12-13th Nov., ICE CUBE (Quetta) 2018
  • 5. Khan, M. T., and Khalid S., “Paradigmatic and Syntagmatic Rule Extraction for lifelong Topic Models”, 6th IEEE International Conference on Electrical Engineering and Informatics, 25-27th Nov., ICEEI (Langkawi, Malaysia) 2017
  • 6. Abbas M., Rauf A., Riaz M. U., Khan, M. T., and Khalid S., “Context-aware Youtube Recommender System”, 7th International Conference on Information and Communication Technologies, 30-31st Dec., ICICT (Karachi, Pakistan) 2017
  • 7. Khan, M. T., Yar S., Khalid S., and Aziz F., “Evolving long-term dependency rules in Lifelong Learning Models”, 3rd IEEE International Conference on Knowledge Engineering and Applications, 28-30Sept., ICKEA (Singapore) 2016
  • 8. Khan, M. T., Yar S., Khalid S., and Aziz F., “Evolving long-term dependency rules in Lifelong Learning Models”, 3rd IEEE International Conference on Knowledge Engineering and Applications, 28-30Sept., ICKEA (Singapore) 2016
  • 9. Muhammad I. M., Muhammad J. M., Khan, M. T. and Khalid S., “Self-refining Targeted Readings Recommender System”, 2nd IEEE International Conference on Artificial Intelligence and Robotics, 1-2nd Nov., ICRAI (Rawalpindi, Pakistan) 2016
  • 10. Khan, M. T. and Khalid S., “Unsupervised Aspect Extraction from Large Scale Data”, 14th IEEE International Conference on Frontiers of Information Technology, 19-21st Dec., FIT (Islamabad, Pakistan) 2016
  • 11. Khan, M. T. and Khalid S., “Multimodal Rule Transfer into Automatic Knowledge Based Topic Models”, 19th IEEE International Multi-Topic Conference, 5-6th Dec., INMIC (Islamabad, Pakistan) 2016
  • Khan, M. T., Yar S. and Khalid S., “Histogram Based Rule Verification in Lifelong Learning Models”, 19th IEEE International Multi-Topic Conference, 5-6th Dec., INMIC (Islamabad, Pakistan) 2016 Normal 0 false false false EN-US X-NONE X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:8.0pt; mso-para-margin-left:0cm; line-height:107%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri",sans-serif; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:"Times New Roman"; mso-bidi-theme-font:minor-bidi;}
  • Books and Book Chapters
  • Chapter 31: Sentiment Analysis for Health Care Information Resources Management ssociation and others. (2016). Big Data: Concepts, Methodologies, Tools, and Applications. IGI Global Publishing Hershey, PA, USA. ISBN: ISBN:1466698403 9781466698406, 1st Edition, pg 676 - 689.
  • Research Supervision
  • [2019 - present] MS thesis: Research papers classification based on language and style features
  • [2018 - present] PhD thesis: Classifying unknown classes through lifelong deep learning

Experience

  • Courses Taught
  • Basic: Introduction to computing, Introduction to programming, Programming for engineers, Programming fundamentals, Object oriented programming
  • Intermediate: Web programming, Web engineering, Web technologies, Object-oriented analysis and design, Software engineering, Database systems
  • Advance: Data mining and data warehousing, Artificial intelligence, Human computer interaction, Text mining, Information retrieval and text mining, Social networks analysis
  • FYP Supervision
  • 2019: Fake reviews detection
  • 2018: Narrow folksonomy for social research groups
  • 2017: Exploring hierarchy in topics using formal concept analysis
  • 2017: Context-aware youtube video recommender system
  • 2017: Analyzing reasons for online social conflicts
  • 2016: Personal analytics for activities management
  • 2016: Government Policy Monitoring through Semi-supervised Aspect-based Sentiment Analysis
  • 2016: Lifelong Machine Learning for Aspect Extraction
  • 2015: Unsupervised Network Analysis of Textual Data
  • 2015: Contextual Categorization of Implicit Bookmarking for Revisitation
  • 2014: Sentiment Analysis using Hadoop
  • 2013: Automatic timetable generation through controlled random approach
  • 2012: Health Management System
  • Administrative Responsibilities
  • Member of web development society