I am a statistician working for the Fifth Third (5/3) bank in Cincinnati (OH). In the bank, I am leading a team of quantitative analysts developing predictive models for the enterprise risk as well as working on annual submissions of Comprehensive Capital Analysis and Review (CCAR). Before joining the Fifth Third bank, I had worked for LexisNexis Risk Solutions, JPM Chase, and PayPal on various interesting areas, including database marketing, risk modeling, and fraud detection with machine learning.
I truly enjoy working on the data and concerting with other statisticians. In my spare time, I like learning new computing languages and reading good books and paper in statistics and machine learning.
Let’s link up on Linkedin
Developed R Packages
– R package “mob” (https://CRAN.R-project.org/package=mob), Monotonic Optimal Binning for the risk scorecard development
– R package “yager” (https://CRAN.R-project.org/package=yager), General Regression Neural Networks for functional approximation and classification
– R package “yap” (https://CRAN.R-project.org/package=yap), Probabilistic Neural Networks for pattern recognition
– Modeling Practices of Operational Loss Forecasts, SAS Analytics Conference, 2015
– Modeling Fractional Outcomes with SAS ®, SAS Global Forum, 2014
– Modeling Practices of Risk Parameters for Consumer Portfolio, SAS Analytics Conference, 2011
– Rapid Model Refresh in Online Fraud Detection Engine, SAS Data Mining Conference, 2010
– Generalizations of Generalized Additive Model: A Case of Credit Risk Modeling, SAS Global Forum, 2009
– A Class of Predictive Models for Multilevel Risks, SAS Global Forum, 2009
– Count Data Models in SAS, SAS Global Forum, 2008 (Best Contributed Paper)
– Adjustment of Selection Bias in the Marketing Campaign, INFORMS Marketing Science Conference, 2008
– Behavior-based Predictive Models, SAS Data Mining Conference, 2008
– Generalized Additive Model and Applications in Direct Marketing, Direct Marketing Association (DMA) Analytical Journal, 2008
– Improve Credit Scoring by Generalized Additive Model, SAS Global Conference, 2007
– Data Mining: Decision Trees, Multivariate Adaptive Regression Splines (MARS), Generalized Additive Models, Projection Pursuit Regression, Neural Networks, Bagging, Boosting, Bumping, and Decision Stump.
– Statistics: Generalized Linear Models, Count Outcome Models, Proportion Outcome Models, Longitudinal Models, Finite Mixture Models, Quantile Regression, Multivariate Analysis, and Time Series.
– Programming: R / S+, Python, Julia, Matlab / Octave, and SAS.
– Database: Teradata (BTEQ), Oracle (PL/SQL), DB2, SQL server (T-SQL), MySQL, SQLite, and MongoDB.
– Utilities: Linux, Cygwin, Emacs (ESS), Vim, SED, Shell, Pig Latin, and HDF5.
– Risk Modeling: Credit Risk Models (PD / EAD / LGD) and Scorecard Development.