Re your chosen topic, the article attachment I posted in this thread
may be of interest.
It's unlikely you'll be able to get up to speed in the field to produce something really novel in 2 months.
Here is a suggestion for a topic. Learn how to produce the so-called risk-neutral distribution [$]Q(S_T)[$], which is the market's distribution outlook for the future stock price [$]S_T[$] (adjusted for risk), for the S&P500 Index. Here [$]T[$] is a future date -- say 1 month away. You deduce [$]Q(S_T)[$] from current SPX option chain data, namely using options expiring in one month.
This will require you to learn how to automate the acquisition of the option prices, convert them to Black-Scholes implied volatilities, fit those implied volatilities to a smooth function vs. strike K (I suggest Gatheral's SVI fit), insert that function back into the Black-Scholes formula, and then take two K-derivatives (analytically). This last step uses what is called the Breeden-Litzenberger formula. All of this should be something learnable with your background, will likely be novel enough for your instructor, and I believe it's quite doable in a couple months.