Ph.D.

Summary

Dedicated Applied Statistician and self-motivated Ph.D. in Mathematics with more than 7 years of innovative research experience with demonstrated success in solving complex problems and more than 4 years of well-developed working experience in industrial segments. Certified in Applied Management Principles (AMP) with proven leadership-oriented abilities and 3 years of extensive experience in team building & management, decision-making, and public speaking & presentations. Aera of Expertise include:
Statistical:
Data assessment for quality measures such as completeness, accuracy, and applicability
Data Management using SQL
Quantification Statistical Data Analysis using SAS & R Statistical Visualization using R & Tableau
Statistical Machine Learning & Bias-Variance tradeoff Asymptotic & Cross-Validation optimization techniques
Cluster Sampling Parametric and Non-parametric Kernel Density Estimations methods
Foliage classification of LiDAR point cloud data Dynamic Principal Components Analysis (PCA)
Mathematical
Dynamic Computed Tomography Radon, X- & Light-Ray transforms, Partial Differential Equations
Biharmonic/Schrödinger equations Reconstruction of potential/magnetic fields utilizing scattering amplitudes
Wave propagation Landweber Iterative Image Reconstruction technique
Fourier Spectral Analysis and FFT-based Signal Analysis
Smart Structures & Non-Linear Vibration
Thermal stress management of engineered multilayered structures Sensitivity Analysis
Time-frequency Analysis of the dynamical behavior of Real-Time Hybrid Structures.
Nonlinear Normal Modal in vibrating systems and formulation of the solution of equations of motion
Cyber-physical smart structures and identification of the stability switch moment, MatLab Simulations

Expectations

Looking for Data Science positions

Employment Preferences

Academic Degree

Experience

Skills

• Python
• R
• SAS
• SQL
• Data Assessment For Quality Measures Such As Completeness
• Accuracy
• Applicability Data Management Using SQL
• Tableau
• Quantification
• Visualization
• Statistical Data Analysis
• Statistical Machine Learning
• Bias-Variance Tradeoff
• Asymptotic
• Cross-Validation Optimization Techniques
• Parametric
• Non-parametric Kernel Density Estimations Methods
• Classification
• LiDAR Point Cloud Data
• Dynamic Principal Components Analysis
• PCA