Research
Machine Learning & Knowledge Representation (MlKr) Lab

The Machine Learning & Knowledge Representation (MlKr) lab focuses on theoretical and applied aspects of Machine Learning and Knowledge Representation in various domains.

Machine learning deals with the study of computational methods that enable machines to learn from data. Our research on machine learning covers supervised learning, unsupervised learning, reinforcement learning, and ensemble learning.

Knowledge representation is dedicated to the analysis of computational methods for representing information about a domain in a form that a computer system can utilize to solve complex problems. Our research in knowledge representation encompasses automated modeling, ontologies, and automated reasoning.

Though we intend to investigate a broad range of application areas, currently we are actively working in the following application domains:

  • Information Fusion
  • Context-aware Computing
  • Image Processing
  • Wearable Computing
  • Recommendation Systems
  • Indoor Navigation/Positioning
  • eHealthcare, uHealthcare and mHealthcare
  • Text Mining
  • Social Network Analysis
  • Human Computer Interaction
  • Internet of Things

MlKr lab has opened several fully funded PhD and postdoc positions in the areas of machine learning and knowledge representation. We encourage applications from outstanding candidates with an academic background in Computer Science or related fields, who are keen to do basic research in these areas and related applied domains. Scholarships and benefits are on a par with the most attractive international offers. Numerous opportunities are available for collaboration and exchange with other universities. In case you are interested, please send an email with your CV to the Head of the laboratory Prof. Adil Khan (a.khan@innopolis.ru).

Faculty Members:

MlKr is a new and exciting initiative. In the laboratory students will be supervised by Prof. Adil Mehmood Khan.

Dr. Adil Khan is an assistant professor at the Department of Computer Science at Innopolis University. He received his Ph.D. in Computer Engineering from Kyung Hee University, South Korea, in February 2011. After that, Dr. Khan joined the School of Information and Computer Engineering at Ajou University, South Korea, as an assistant professor, where he taught at both graduate and undergraduate schools. Furthermore, he served as a co-leader at the Knowledge Intensive Software Engineering, and Ubilife research labs at Ajou University. His research work comprising over 35 articles was presented at various international conferences and published in journals with high impact factors. He is also a reviewer of various international journals as IEEE, ACM, Elsevier etc. His primary research interests include machine learning, context-aware computing, wearable computing, image processing, and data mining.

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