Programming: C/C++, MATLAB, bash
Operating Systems: Linux, Windows
Specialties: Time Series Analysis, Digital Signal Processing, Bayesian Inference, Quantitative Research
Highlights:
• Wrote approximately 20,000 lines of compilable code (~12,250 for real-world/experimental data, ~7,750 for synthetic data)
• The largest data set worked with: 11TB images of 12 years of Astrophysical object observations
• Automated mass-processing of data from 2500+ experiments by developing code and data-handling conventions for the research group in MRELab at UofM
• Provided patent landscape analysis and technical assessment of over 150 new University inventions in the fields of numerical algorithms, engineering analysis software, and physical sciences
Please click on a project’s title above its thumbnail for a detailed report/demonstration.
• Developed a bootstrapping procedure for Bayesian model selection in design of bandwidth-adaptive FIR filters to predict the unknown external input of dynamical systems from their response time-series
• As an application to fluid-dynamic systems, utilized time-frequency analysis and trend filtering on the estimated external forces acting on cylinders in flow-induced oscillations
• Extracted nonlinear, nonstationary patterns of driving processes pertinent to higher harmonics of fluid-structure interaction that are excluded in conventional linear theory
Structure Discovery And Pattern Recognition Using Gaussian Process Regression
• Developed Gaussian Process Regression algorithms in MATLAB for automated structure discovery in time-series through compositional kernel search
• The search procedure automatically recognizes a space of composite kernel structures which captures underlying patterns in data and enables long-range extrapolation, while optimally deciding proper model complexity using Bayesian Information Criterion
• Fully functional with Gaussian Processes for Machine Learning Toolbox, currently under development for pattern recognition in financial time-series
Data Handling Tutorial in MATLAB
35-minute tutorial ( video and code report ) on Data Handling Basics in MATLAB for ENG101: INTRODUCTION TO COMPUTERS AND PROGRAMMING class offered at the University of Michigan. The following topics are demonstrated:
During the demonstration of the topics above, core programming concepts covered are: Array Manipulation, Random Number Generation, Linear & Logical Indexing, Code Vectorization.
Please click on a title above the blockquotes to access the related GitHub repository
Null-based Bayes Factor computation for evidence quantification and model selection through Bayesian hypothesis testing in Multivariate Regression designs
Improved spectrum and spectral density estimation by Fourier transforms at log-scaled frequencies
An event-driven restaurant simulator with randomized customer arrival and service times drawn from probability distributions. Utilizes personally developed Sorted Doubly Linked List and Queue templated classes.
Statistical accumulations such as the longest line, average service and waiting times, as well as the percentages of server busy time, customers served and customers waited in line are computed and reported.
Networked Battleship Game written by using socket programming principles. The game can be played between two remote players using client-server communication or against the PC as a single player.