Email: ajabkhan@gmail.com

Extention #: 1032

Contact #: +923459876543


Additional Information
Curriculm Vitae



Dr.Ajab Khan

Lab Engineer

Department of Computer Science & IT


      Joined UOM in 2011 as Assistant Professor, Dr. Salman holds Ph.D. in Finance from I.A.E, Graduate School of Business, University Aix Marseille, France. His research interests primarily focus on investment and portfolio management. Presently, he is working in the area of liquidity and liquidity risk(s). His teaching interests spread over 5 years with local as well as international exposure in the area of Investment and Portfolio Management (IPM), Corporate Finance, Risk Management, Entrepreneurip


Education

  • Jul-2017 new record notttt Aland Islands
  • Apr-2015 alphabets University of Malakand pakistan
  • Feb-1996 bs chemistry University of Malakand pakistan

Experience

  • Feb-2013 to Mar-2014 as teacher school United States.

Research Interest

      My research interests are in the areas of machine learning, statistical learning theory, and reinforcement learning. I work on the theoreticalanalysis of computationally efficient methods for large or otherwise complex prediction problems. One example is structured prediction problems, where there is considerable complexity to the space of possible predictions. Such methods are important in a variety of application areas, including natural language processing, computer vision, and bioinformatics. A second area of interest is the analysis of prediction methods in a deterministic, game-theoretic setting. As well as being of interest in areas such as computer security, where an adversarial environment is a reasonable model, this analysis also provides insight into the design and understanding of prediction methods in a probabilistic setting. A third area of interest is the design of methods for large scale sequential decision problems, such as control of Markov decision processes. Again, computational efficiency is a crucial requirement. This is a common feature in all of these areas: the interplay between the constraint of computational efficiency and the statistical properties of a method.