Dr Sasan Barak

Research groups

Research interests

• Financial Machine Learning (including Portfolio Optimisation, Algorithmic Trading)
• Forecasting (Model Selection, Meta-Learning, Deep Learning)
• Statistical and Machine Learning Algorithms in Analytics

Current research

Sasan's main area of research revolves around forecasting model selection using Meta-learning and Deep Learning approaches and implementing statistical and machine learning algorithms for business time series analysis.

Moreover, Sasan is keen on machine learning applications in finance, while he has a research group and a startup related to this topic. 

His past work includes studies on Forecasting energy consumption; Evaluating stocks by features selections and data mining methods; Fusion of multiple diverse predictors, and Evaluation and selection of clustering methods. He is also interested in Multi-Criteria Decision Making and Multi-objective optimization, while he obtained some international research grants related to these topics.

He has been an active researcher in the field of data analytics and decision making, and the outcome of his research has appeared in leading analytics journals such as European Journal of Operational Research, International Journal of Production Economics, Expert System with Applications, Information Fusion, and International Journal of Production Research. He is the principal investigator of two European grant projects funded by the Czech Science Foundation (GACR).

Primary research centres:

• CORMSIS
• Centre for Digital Finance (CDF)

Department:

Department of Decision Analytics and Risk

Member of:

• International Institute of Forecasters (IIF)
• International Association of Engineers (IAENG)
• Britain Operations Research Society (ORS)
• European Institute of Industrial Engineering (IIE)

Reviewer:

• “Applied Soft Computing”,
• “European Journal of Operational Research”,
• “Expert systems with applications”,
• “Journal of Cleaner Production” 

PhD Research Grants:

• ESRC Grant for Advance Quantitative Method Project (AQM) at Lancaster University, UK

• Full scholarship from Università degli studi di Bergamo

Grants:

Principal Investigator:

• Southampton Business School Small Research Fund: Internet of Things (IoT) Time Series Analysis Using Deep learning

• GACR - Multi-Objective Optimization Application in Flexible Manufacturing and Project Scheduling Problems: Theory and Applications (2018-2020) Grant No. GA 18-15530S (125,000GBP)

• GACR - Multiple Criteria Decision Making Modelling: Novel Weighting Methods and Hybrid Approaches (2017-2018) Grant No. GA 17-22662S (55,000GBP)

Research Team:

European project – Operational Programme Education for Competitiveness. Ministry of Education, Youth and Sport of the Czech Republic:  OP VK (Project No. CZ.1.07/2.3.00/20.0296): Research team member for modelling of economic and financial processes

Research Grant for GACR project (GA18-13951S): New approaches to financial time series modelling based on soft computing

Research Grant for GACR project (GA16-17810S): Selective measures in data envelopment analysis: Theory and Applications

Research Grant for GACR project (GA 15-23699S): Risk Probability Functional and Ordering Theory Applied to International Financial Markets and Portfolio Selection Problems

Research Grant for GACR project (GA 14-31593S): Examining allocative efficiency through network data envelopment analysis

Other achievements :

• Best PhD Thesis Award in Czech Business Schools 

• Winner of the 2013 Iranian National Elites Foundation award (INEF)

Research projects