• Applied game theory

    Applied game theory

    My studies focus mainly on the relationships between the corporations and governments and the goal is to encourage them to follow Eco-friendly strategies using game-theoretic approaches.

    Game theory is “the study of mathematical models of conflict and cooperation between intelligent rational decision-makers”. Game theory is mainly used in economics, political science, and psychology, as well as logic, computer science, and biology. Originally, it addressed zero-sum games, in which one person’s gains result in losses for the other participants. Today, game theory applies to a wide range of behavioral relations and is now an umbrella term for the science of logical decision making in humans, animals, and computers.
    The modern game theory began with the idea regarding the existence of mixed-strategy equilibria in two-person zero-sum games and its proof by John von Neumann. Von Neumann’s original proof used the Brouwer fixed-point theorem on continuous mappings into compact convex sets, which became a standard method in game theory and mathematical economics. His paper was followed by the 1944 book Theory of Games and Economic Behavior, co-written with Oskar Morgenstern, which considered cooperative games of several players. The second edition of this book provided an axiomatic theory of expected utility, which allowed mathematical statisticians and economists to treat decision-making under uncertainty.
    This theory was developed extensively in the 1950s by many scholars. Game theory was later explicitly applied to biology in the 1970s, although similar developments go back at least as far as the 1930s. Game theory has been widely recognized as an important tool in many fields. With the Nobel Memorial Prize in Economic Sciences going to game theorist Jean Tirole in 2014, eleven game-theorists have now won the economics Nobel Prize. John Maynard Smith was awarded the Crafoord Prize for his application of game theory to biology.

  • Hub Location and Allocation

    Hub Location and Allocation

    My masters thesis is based on the hub networks and tries to maximize customers' satisfaction and reliability of the system.

    Hub location problems (HLPs) are used in systems that have many origins and destination nodes, where connecting all nodes in the networks is impossible or expensive. In such systems, flows consolidate at the hub nodes and it becomes possible to route all transportation through the hubs. Also, costs are reduced because of economic advantages scale for transportation flows between hub nodes. The aim of HLPs is to locate facilities in potential hub nodes in networks and allocate other nodes (non-hub) to these hubs.

  • Data mining and Machine learning

    Data mining and Machine learning

    I am highly interested in applying data mining methods on managerial applications, especially, identifying patterns, and clustering.

    Data mining is the computing process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. It is an interdisciplinary subfield of computer science. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. Data mining is the analysis step of the “knowledge discovery in databases” process or KDD.

  • Big data and data analysis

    Big data and data analysis

    As unbelievable benefits of the huge amount of the data which was useless before are revealing, Big data is attracting more and more attention among the managers. A new topic in this field is high dimensional big data that is fascinating me and has enormous applications in management. Also, I have some experience in visualization and representing big data.

    Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. Big data challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating and information privacy.
    Lately, the term “big data” tends to refer to the use of predictive analytics, user behavior analytics, or certain other advanced data analysis methods that extract value from data, and seldom to a particular size of data set. “There is little doubt that the quantities of data now available are indeed large, but that’s not the most relevant characteristic of this new data ecosystem.” Analysis of data sets can find new correlations to “spot business trends, prevent diseases, combat crime and so on.” Scientists, business executives, practitioners of medicine, advertising and governments alike regularly meet difficulties with large data-sets in areas including Internet search, fin-tech, urban informatics, and business informatics. Scientists encounter limitations in e-Science work, including meteorology, genomics, connectomics, complex physics simulations, biology and environmental research.
    Data sets grow rapidly – in part because they are increasingly gathered by cheap and numerous information-sensing Internet of things devices such as mobile devices, aerial (remote sensing), software logs, cameras, microphones, radio-frequency identification (RFID) readers and wireless sensor networks. The world’s technological per-capita capacity to store information has roughly doubled every 40 months since the 1980s; as of 2012, every day 2.5 exabytes (2.5×10^18) of data are generated. One question for large enterprises is determining who should own big-data initiatives that affect the entire organization.
    Relational database management systems and desktop statistics- and visualization-packages often have difficulty handling big data. The work may require “massively parallel software running on tens, hundreds, or even thousands of servers”. What counts as “big data” varies depending on the capabilities of the users and their tools, and expanding capabilities make big data a moving target. “For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration.”

  • Healthcare management

    Healthcare management

    I have some experience in scheduling, assiging and routing care givers according to the patients and care givers objectives.

    Health systems management or health care systems management describes the leadership and general management of hospitals, hospital networks, and/or health care systems. In international use, the term refers to management at all levels. In the United States, management of a single institution (e.g. a hospital) is also referred to as “medical and health services management”, “healthcare management”, or “health administration”.
    Health systems management ensures that specific outcomes are attained, that departments within a health facility are running smoothly, that the right people are in the right jobs, that people know what is expected of them, that resources are used efficiently and that all departments are working towards a common goal.