Mohamed Tawhid



Professor M. Tawhid Mohamed A. Tawhid got his PhD in Applied Mathematics from the University of Maryland Baltimore County, Maryland, USA. From 2000 to 2002, he was a Postdoctoral Fellow at the Faculty of Management, McGill University, Montreal, Quebec, Canada. Currently, he is a full professor at Thompson Rivers University. His research interests include nonlinear/stochastic/heuristic optimization, operations research, modelling and simulation, data analysis, and wireless sensor network. He has published in journals such as Computational Optimization and Applications, J. Optimization and Engineering, Journal of Optimization Theory and Applications, European Journal of Operational Research, Journal of Industrial and Management Optimization, Journal Applied Mathematics and Computation, etc. Mohamed Tawhid published more than 50 referred papers and edited 4 special issues in J. Optimization and Engineering (Springer), J. Abstract and Applied Analysis, J. Advanced Modeling and Optimization, and International Journal of Distributed Sensor Networks. Also, he has served on editorial board several journals. In last two years, he has started consulting with industry for which he received two NSERC Engage grants. These new projects are significant in many aspects. First, it demonstrates knowledge transfer from the university to industry. The projects also provide an internship for an M.Sc. student, research associates and Post-doc researchers, demonstrating the training of highly qualified personnel. The 1st project was related to social network analytics in which we used statistical tools to answer the following questions: When are people most likely to comment or like a post on a wall in Facebook? When are people most likely to retweet content? Is there a language pattern between content receiving more tweets, likes or comments? Which region is more likely to leave comments? The 2nd project was related to portfolio management investment system. This project will develop static single period and dynamic multi-period portfolio selection models and computer implement and test them with simulated data and live investment data.
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