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Wenbo Cao
Program: Computer Science
Post-Doctoral Position: Standard and Poor's Quantitative Research Group
In 2001, I entered the CS PhD program at CUNY the Graduate Center, after I had a master degree in Atmospheric Physics. Working toward my PhD is a stimulating and rewarding journey. I got solid and systematic training on machine learning. More importantly, under the supervision of Professor Robert Haralick, I learned how to formulate and work out research problems, how to write research papers and present research ideas with confidence, and how to deal with stress and frustration. All of these are essential ingredients that help me to fulfill my dreams.
 
CUNY also gave me a great start to build my career path.  The finance industry of New York provides many opportunities for students to apply their knowledge and skill, and to solve industrial problems creatively. With the help of the CS program, I started to work on industrial research projects beginning in 2002. It not only deepened my understanding of machine learning, but also gave me very special opportunities to build personal networks and learn about the world outside the ivory tower.
 
In 2007, even though the job market began to deteriorate, I was able to secure a position in the Quantitative Research Group of Standard and Poor's.  For most of my time at Standard and Poor's, I had been developing multivariate conditional probabilistic models that better capture the joint behavior of economical variables. And due to my machine learning background, I also reviewed and enhanced a natural language processing project that help analysts to assign credit ratings to public firms.
 
In 2014, I joined the Quantitative Risk Management Team of TIAA-CREF as a director. Since then, I have been applying the most recent research results of machine learning field to build a complex probabilistic model to model hundreds of economical variables simultaneously. I believe that with the training that I received at CUNY, it would be a successful adventure that is full of challenges and fun.