- Joined
- 9/7/18
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Hi, my name is Abhishek and I am planning to pursue MSFE programs from universities like Columbia, UC Berkeley, UCLA, NYU etc. I was curious to know which colleges will be ambitious and which be safe for applying with my current profile.
GRE 316: Quant 167. Verbal 149.
Undergraduate CGPA: 7.6/10
Research Experience:
1. Global alpha researcher at Trexquant (US hedge fund) for 1 year. Work involves implementation of trading signals by reading various research papers.
2. Part time Quant Researcher at Worldquant (US hedge fund) for 3 years. Work is nearly same as above but focuses more on model implementations and challenges.
Full time Work Experience:
Samsung Research Institute (2 years) as a software developer.
Area of interests:
Computational mathematics, statistical learning, machine learning, probability, algorithmic trading, statistical arbitrage.
Current research work:
1. analysis of midi files to understand inherent structure and patterns in music using LSTM. Using those learned structures to generate new sequences
2. Building models to implement high sharpe (>2.5) alphas in Asia’s top 150 universe.
3. Using institutional ownership data along with analyst forecasts to find meaningful and high returns trading signals in US market.
4. Taking part in Quantiacs algorithmic trading competition.
GRE 316: Quant 167. Verbal 149.
Undergraduate CGPA: 7.6/10
Research Experience:
1. Global alpha researcher at Trexquant (US hedge fund) for 1 year. Work involves implementation of trading signals by reading various research papers.
2. Part time Quant Researcher at Worldquant (US hedge fund) for 3 years. Work is nearly same as above but focuses more on model implementations and challenges.
Full time Work Experience:
Samsung Research Institute (2 years) as a software developer.
Area of interests:
Computational mathematics, statistical learning, machine learning, probability, algorithmic trading, statistical arbitrage.
Current research work:
1. analysis of midi files to understand inherent structure and patterns in music using LSTM. Using those learned structures to generate new sequences
2. Building models to implement high sharpe (>2.5) alphas in Asia’s top 150 universe.
3. Using institutional ownership data along with analyst forecasts to find meaningful and high returns trading signals in US market.
4. Taking part in Quantiacs algorithmic trading competition.