As a Postdoctoral Associate, Ashkan is now working with the intelligent Health Lab (iHeal) at the University of Florida. The project which has been funded through an NSF STTR grant aims to use natural language processing and machine learning techniques to develop an intelligent highly tailored and personalized mental health therapy tool to create a paradigm shift to change how psychotherapy is provided.

As the Ph.D. work, he comprehensively analysed the inter-relations among funding, collaboration and scientific activities. A unique procedure was designed and proposed to collect the required data and a triangulation technique was employed to perform the analysis. In other words, various methods and methodologies (i.e. data and text mining, statistical analysis, social network analysis, bibliometrics, survey data analysis, and visualization techniques) were used to investigate the impact of influencing factors on researchers' performance, their amount of funding and collaboration patterns from different perspectives. Both quantitative and qualitative (e.g. interviews) approaches were applied. As the final component of the research, a machine learning framework was designed and suggested for predicting researchers' performance and their competence level of funding. He further extended the project at Concordia working as a Postdoctoral Fellow for six months where he employed machine learning techniques to cluster the scientific publications automatically and more accurately. In addition, an automatic gender detection system was designed and implemented that complemented the previously done analysis by incorporating the gender role in the highly inter-connected triangle of funding, collaboration and scientific performance.

In general, his research interests are as follows:

• Big Data Analysis
• Machine Learning
• Natural Language Processing
• Data Mining & Text Mining
• Statistical Data Analysis
• Social Network Analysis
• Scientometics
• Health Informatics