I have been thinking about a second PhD for the last year. I am currently a doctoral candidate in civil engineering at University of Texas at Austin (ranked 6th in my discipline) and working as a student statistical consultant at the university consulting center. I am working on spatio-temporal modeling of count data using Bayesian hierarchical models, with computationally efficient techniques for my dissertation. (I will have publications on this very soon; two of them are in review). I do have 2 publications in my area but their topic is the application of statistical models. I am also going to get a Masters in statistics next semester along with my PhD in civil engineering. I have done many courses related to Bayesian statistics including graduate level mathematical statistics, theoretical MCMC, stochastic volatility and time series models, statistical consulting, advanced econometrics (non-Bayesian), discrete choice modeling and one course on data mining (graduate level). In my field, I see massive datasets but very minimal statistical expertise, particularly on the big data side. This has motivated me to pursue something beyond my Phd and beyond my discipline.
I am very interested in handling large datasets and perhaps, machine learning applications. I would like to know whether a PhD in machine learning is going to help me realise my dreams. I do not have any formal research experience in data mining or machine learning. But, I do a lot of Bayesian hierarchical modeling on smaller datasets. Given my experience, I am not sure whether I can secure admission to a good program in the machine learning area. I appreciate any suggestions and advice on whether to pursue another PhD and the feasibility of securing admission to a good program in a PhD machine learning track. I am assuming basic financial assistance for any PhD program.
Thanks much in advance.