Applied ai startup in table tennis (have raised funds, pending incorporation, startup name ideas welcome! :D)

Exploring practical application of deep learning to financial markets, video games and sports.Interested in conducting research on AGI along the lines of work being done at deepmind/openai (computer vision+deep rl)Studying:Deep reinforcement learning (Udacity)NLP (Coursera)Studying (completed):Self driving cars (udacity)Quantum computing (mitxpro)Tensorflow in practice specialization (Coursera)

Analyzed data from Firebase , google cloud big query and sql databases to understand user behavior Created a recommendation engine for videos using content filtering and regionwise customer segmentation

• Writing a research paper on application of artificial intelligence/machine learning to beat the stock market by using fundamental and technical factors as inputs• Consulting Motilal Oswal on a quantitative fund• Used R to build/validate credit risk models• Completed various courses on Data Analytics/Big Data on Coursera/EdX and Udacity

• Conceived asset allocation strategy based on market valuations: MOVI Index • Analyzed Motilal Oswal M50’s existing quant model; suggested qualitative and quantitative improvements• Created a new quant strategy with theoretical returns of 25% CAGR over past 10 years, 45% CAGR over past 5 years, beta of 0.7 and alpha of 7.8%. Out of sample return (March-September 2014): 51% vs. index return of 30%.• Portfolio optimization in MATLAB: Mean Variance Optimization, Mean CVar, Mean CVar using Monte Carlo simulations (Used copulas to simulate asset returns: incorporated covariance of asset returns and first four moments: mean, std dev, kurtosis, skewness)

• Analyzed credit worthiness of over 120 companies from a Basel II, Bank loan rating perspective• Conducted analysis of industry, business, financial and management risk to arrive at a bank loan rating recommendation• Prepared rating reports published on CRISIL’s website• Worked as a Business analyst on the rating process improvement team: automated routine tasks, improved rating processes, identified and debugged major issues in online rating note generation platform

• Developed, back-tested and traded mechanical trading systems with annual returns of 40-60% and standard deviation of 17%. • Developed software for chart pattern recognition, arbitrage detection, automated multiple-timeframe Fibonacci analysis and risk management