Matthew Gilbert works as a quantitative analyst in a Global Macro group at CPPIB based out of Toronto, Canada. He has a master's degree in quantitative finance from Waterloo University and a bachelor's degree in applied mathematics and mechanical engineering from Queen's University.

Dr. Hari Shanker Gupta is a senior quantitative research analyst working in the area of algorithmic trading system development. Prior to this, he was a postdoctoral fellow at Indian Institute of Science (IISc), Bangalore, India. He has obtained his PhD in applied mathematics and scientific computation at IISc. He completed his MSc in mathematics from Banaras Hindu University (BHU), Varanasi, India. During his MSc, he was awarded four gold medals for his outstanding performance in BHU, Varanasi.

Hari has published five research papers in reputed journals in the field of mathematics and scientific computation. He has experience of working in the areas of mathematics, statistics, and computation. These include the topics: numerical methods, partial differential equations, mathematical finance, stochastic calculus, data analysis, time series analysis, finite difference, and finite element methods. He is very comfortable with the mathematics software Matlab, the statistics programming language R, Python, and the programming language C.

He has reviewed the books Introduction to R for Quantitative Finance and Learning Python Data Analysis for Packt Publishing.

Ratan Mahanta holds an MSc degree in computational finance. He is currently working at GPSK investment group as a senior quantitative analyst. He has 3.5 years of experience in quantitative trading and developments for sell side and risk consulting firms. He has coded algorithms on Github's open source platform for "Quantitative trading" areas. He is self-motivated, intellectually curious, and hard-working, and loves solving difficult problems that lie at the intersection of market, technology, research, and design. Currently, he is developing high-frequency trading strategies and quantitative trading strategies. He has expertise in the following areas:

Quantitative Trading: FX, Equities, Futures and Options, and Engineering on Derivatives.

Algorithms: Partial differential equations, Stochastic differential equations, Finite Difference Method, Monte-Carlo, and Machine Learning.

Code: R Programming, Shiny by RStudio, C++, Matlab, HPC, and Scientific Computing.

Data Analysis: Big-Data-Analytic [EOD to TBT], Bloomberg, Quandl, and Quantopian.

Strategies: Vol-Arbitrage, Vanilla and Exotic Options Modeling, trend following, Mean reversion, Cointegration, Monte-Carlo Simulations, Value at Risk, Stress Testing, Buy side trading strategies with high Sharpe ratio, Credit Risk Modeling, and Credit Rating.

He has also reviewed the book Mastering Scientific Computing with R, Packt Publishing, and currently, he is reviewing the book Machine Learning with R cookbook, Packt Publishing.