Academic staff
Adil Khan

Adil Khan

Adil Khan
  • PhD
    Kyung Hee University, South Korea
    Associate Professor
    Insitute of Data Science and Artificial intelligence
    Head, Lab of Machine Learning and Knowledge Representation
  • Education: Computer Engineering
  • University Role: Associate Dean (Teaching)
Dr. Adil Khan is a Professor of Computer Science in the School of Information and Computer Engineering, Ajou University, South Korea. He received his Ph.D. in Computer Engineering from Kyung Hee University, South Korea. He has over 8 years of experience in academic research and teaching. His primary research interests include machine learning, data analytics, data modeling, context-aware computing, context recognition by means of wearable and vision sensors, and human-aware mobile application development. His research work, comprising over 30 articles, is published in various international conferences and journals. He is also a reviewer for various IEEE, ACM, Elsevier and other international journals. Dr. Khan has contributed to various research projects: Context-Aware Middleware System for Ubiquitous Systems (funded by Ministry of Commerce, Industry, and Energy, Korea), Ontology-based text Mining for eHealth Data (funded by Microsoft Research Asia), East-West Neo Medical u-Life Care IT Research Center (funded by Ministry of Knowledge Economy, Korea), and Mining Minds Core Technology Exploiting Personal Big Data (funded by Ministry of Trade, Industry and Energy, Korea). He is also assisting a Korean company in designing lightweight machine learning algorithms for their power-efficient low-cost physical activity trackers. He is the co-leader of the UbiLife Research Group and a member of the Knowledge-intensive Software Engineering Research Group at Ajou University.
  • Machine learning
  • Data analytics
  • Data modeling
  • Context-aware computing
  • Context recognition by means of wearable and vision sensors
  • Human-aware mobile application development
  • Computational intelligence
  • Khattak, A.M., Akbar, N., Aazam, M., Ali, T., Khan, A. M., Jeon, S., Hwang, M., Lee, S., . Context Representation and Fusion: Advancements and Opportunities." .
  • Khan, A. M., Khattak, A.M., and Laine, T. H.. Activity Recognition on Smartphones via Sensor-Fusion and KDA-Based SVMs..
  • Saputri, T.R.D., Khan, A. M., and Lee, S.-W.. User Independent Activity Recognition via Three-Stage GA-Based Feature Selection.. 2014.
  • Siddiqui, H. M., Lee, S., Lee, Y.-K., Khan, A. M., and Truc, P.T.H.. Hierarchical Recognition Scheme for Human Facial Expression Recognition Systems.. 2013.
  • Khan, A. M., Siddiqui, H. M., and Lee, S.-W.. Exploratory data analysis of acceleration signals to select light-weight and accurate features for real-time activity recognition on smartphones. 2013.
  • Siddiqui, H. M., Khan, A. M., and Lee, S.-W.. Active Contours Level Set Based Still Human Body Segmentation from Depth Images for Video-based Activity Recognition. 2013.
  • Siddiqui, H. M., Lee, S.-W., and Khan, A. M.. Weed image classi_cation using wavelet transform, Stepwise Linear Discriminant Analysis, and Support Vector Machines for an automatic spray control system.. 2013.
  • Siddiqi, M.H, Ali, R., Khan, A.M., Park, Y.-T., Lee, S. Human Facial Expression Recognition Using Stepwise Linear Discriminant Analysis and Hidden Conditional Random Fields . Institute of Electrical and Electronics Engineers Inc.. 1386-1398. 2015.
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