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Research

Research Constellations

My research spans three connected directions: spacecraft autonomy, navigation, and estimation; optimal control; and artificial intelligence and machine learning for engineering materials.

Space Navigation & Estimation Optimal Control AI/ML for Engineering Materials
01

Research Area

Space Autonomy, Navigation, and Estimation

My work in this area focuses on nonlinear state estimation and higher-order Kalman-filter updates for space applications, with particular emphasis on non-Gaussian uncertainty, polynomial estimators, and sigma-point methods.

Kalman Filtering QEKF / QUKF Polynomial Filtering Sigma-Point Methods Conjugate Unscented Transformation Monte Carlo Analysis Clohessy-Wiltshire Dynamics CR3BP

SPACE Laboratory · Iowa State University · May 2024 – Present

Quadratic Extended and Unscented Kalman Filtering

Development of Quadratic Extended and Unscented Kalman Filters using quadratic approximations of the minimum mean-square-error estimator, extending conventional EKF and UKF measurement updates beyond affine dependence on the measurement.

Highlights

  • Incorporated higher-order central moments, including skewness and kurtosis, into nonlinear measurement updates under non-Gaussian uncertainty.
  • Benchmarked quadratic filters against EKF and UKF baselines.
  • In a nonlinear scalar test, QUKF reduced RMSE from 0.0395 to 0.0290, approximately a 27% reduction.
  • Evaluated performance on Clohessy-Wiltshire dynamics under non-Gaussian measurement noise.

SPACE Laboratory · Iowa State University

Higher-Order Polynomial Filtering and Conjugate Unscented Transformation

Generalization of the quadratic measurement update to arbitrary-order polynomial minimum mean-square-error estimators while retaining a recursive Kalman-filter architecture.

Highlights

  • Integrated Conjugate Unscented Transformation rules to evaluate higher-order moments required by polynomial filtering.
  • Applied polynomial-CUT filters to Clohessy-Wiltshire and Circular Restricted Three-Body Problem dynamics.
  • Investigated estimation accuracy and covariance consistency under non-Gaussian measurement uncertainty.
02

Research Area

Optimal Control

My optimal-control research focuses on stochastic optimal control, covariance steering, nonlinear optimization, and trajectory optimization for dynamical systems.

Stochastic Optimal Control Covariance Steering LEQG Nonlinear Optimization Trajectory Optimization Newton Methods Backtracking Line Search

Iowa State University · August 2026 – Present

Linear Exponential Quadratic Gaussian Covariance Steering

Research on continuous-time Linear Exponential Quadratic Gaussian covariance steering between prescribed Gaussian endpoint covariances.

Highlights

  • Derived the optimal risk-sensitive linear state-feedback structure.
  • Established local existence and uniqueness near the corresponding risk-neutral covariance-steering solution.
  • Implemented Newton's method with backtracking line search for the resulting implicit nonlinear matrix equation.
  • Demonstrated the method on a noisy double-integrator system.

February 2025 – May 2025

Titan Parafoil Terminal-Descent Optimization

A nonlinear trajectory-optimization study for terminal descent and soft landing of a parafoil vehicle in the Titan environment.

Highlights

  • Formulated a nonconvex trajectory-optimization problem for terminal descent and soft landing.
  • Implemented glide-slope, pointing-angle, and virtual-control constraints.
  • Evaluated the guidance formulation across varying initial states.
03

Research Area

AI/ML for Engineering Materials

My work in this area applies machine-learning and statistical methods to the prediction and characterization of mechanical and wear behavior in composite materials.

Machine Learning Taguchi Design of Experiments Linear Regression Decision Trees Random Forest Support Vector Regression Artificial Neural Networks Composite Materials

Vellore Institute of Technology · May 2023 – May 2025

Machine Learning for Mechanical and Wear Characterization of Hybrid Composites

Investigation of sisal-fiber, dolomite-dust, and epoxy hybrid composites combined with machine-learning models for material-property and wear prediction.

Highlights

  • Investigated hand-laid sisal-fiber/dolomite-dust/epoxy hybrid composites.
  • The reported 15 wt% dolomite formulation increased tensile strength by 177% and flexural strength by 127% relative to the unfilled specimen.
  • Applied a Taguchi L16 design of experiments to sliding-wear characterization.
  • Evaluated linear regression, decision tree, random forest, support vector regression, and artificial neural network models for material-property prediction.

Explore Further

Publications and experience

Explore my publications for the scholarly output associated with these research directions, or view my experience for additional academic, teaching, and engineering work.

View Publications View Experience