My Toolset

Following bullet points summarize my skills. Taken relevant courses are graduate level courses that built up top of my undergraduate level courses.

  • Modeling and Simulation

    Modeling and simulation is a discipline for understanding system states for different levels. Most of the engineering fields using modeling and simulation to understand or evaluate a system. So, as an engineer and researcher I am using at least a mathematical model and simulation for my research. I have taken significant amount of simulation courses to improve my knowledge on different type of simulations.

    Taken Relevant Courses:

    1. Introduction to Modeling and Simulation
    2. Agent Based Simulation
    3. Discrete Simulation
    4. Continuous/Real Time Simulation
    5. Simulation Formalism
    6. Modeling Global Events
    7. Systems Modeling

  • Transportation Engineering

    We build infrastructure such as roads, bridges, tunnels, etc. to connect physical world. However, this requires planning, operation, maintenance, rehabilitation, performance and evaluation. All of these needs an attention so, a transport engineer deals with these problems using technology and scientific principles. As an engineer I am applying my knowledge of modeling, simulation and programming skills to solve above mentioned problems.

    Taken Relevant Courses:

    1. Simulation in Transportation Networks
    2. Transportation Fundamentals I
    3. Transportation Fundamentals II
    4. Transportation Planning
    5. Transportation Safety

  • Programming

    My programming journey started in my bachelor years. I am proficient in various programming languages and statistical analysis tools including but not limited Java, C++, Python, JavaScript, PHP, SQL, PostGIS, ArcGIS, Hadoop, Spark, VISSIM, R, SPSS, Matlab and Android development. In addition to my programming and statistical skills, I am proficient on dedicated and cloud base server development. I am always learning new languages as needed. I prefer my applications to run in dedicated server because I have full control of it. Meantime some of my applications run on cloud server as well.

    Taken Relevant Courses:

    1. High Performance Computing
    2. Simulation Design

  • Statistical Analysis

    The field of statistics is the science of learning from the data. It is an essential for a statistician that s/he knows which model should apply the on hand data. However, to produce trustworthy conclusion from the data, all stages must be correct. The stages are includes, data collection, cleaning, exploration, and making an assumption. As dealing with the data all in my work, I am proficient on mentioned stages.

    Taken Relevant Courses:

    1. Analysis 1 & 2
    2. Multivariate Statistics

  • Data Mining, Web Mining, and Machine Learning

    Statistical analysis is not enough alone to get an insightful meaning from the data. We need to mine data to extract hidden phases. Sometimes on hand data is not enough so we search on the web and we gather third party data which can extend your data journey more further. On the other hand, we should let our algorithm to talk for us using machine learning techniques.

    Taken Relevant Courses:

    1. Data Mining
    2. Machine Learning

  • Big Data Engineering

    Data is everywhere in our daily life, we generate it nonstop when we are walking, driving, watching, talking, browsing on the web. All of these data storing in some places where you know or not. However, storing these data is not a problem, the main problem is that how do you process all of these data. Recently many frameworks has been developed to deal with big data. I am using those frameworks when necessary. For example, Hadoop, Spark, etc.

  • Databases

    As nature of my work I am dealing with always with data such as, sensor, social, geospatial, online data. In order to use these data effectively it requires a database management system. So, databases are best place to save and organize my data. Since I am dealing with different type of data, each requires different type databases. Such as, for Twitter data I am using NoSQL type database, for sensor data I am using relational database, and for geospatial data I am using PostGIS extension. I should also say that, some of these data is not small, it is a big one which is terabytes of data, therefore I also use big tables such as “Hive”. I think, databases are life savers if one deals with so many data.