🧪 Research Portfolio – Emma Green

Welcome to my research portfolio!
I am a Postdoctoral Researcher specializing in data-driven modelling and control systems.
My work bridges academic research and industrial applications, with experience in:

• Data-driven Modelling and Control
• Secure Cloud-native Control Algorithms
• Signal Processing in Energy Harvesting Wireless Sensor Networks
• Model Predictive Control (MPC), Linear–quadratic regulator (LQR), and Kalman Filtering (KF) Applications
• Regularized System Identification with Kernel Methods

Contents

• 🔧 [Data-driven Modelling + Control]: In this project, we focus on an alternating minimization-based hyperparameter tuning method for SURE estimation.

• 🌐 [Cloud-native Secure Controller]: This project focuses on the investigation of a sequential detection policy for combating the effect of the replay attack on a cloud-native controller. The impact of such an attack is mitigated by adding random signals to the optimal control signal before the actuation process, a technique known as watermarking policy. We study the effectiveness of this joint scheme of sequential detection with watermarking in a cloud-native controller with varying levels of delay introduced in the communication network between the cloud server and the physical plant.

• 📈 [Signal processing with Stochastic Control]:

  Subtopics:

  1. ⚡ Dynamic Programming-based Energy Allocation:
     Developed optimal energy allocation strategies using dynamic programming to maximize throughput and prolong network lifetime under stochastic energy arrivals.

  2. ⏱️ Quickest Change Detection-based Delay Minimization:
     Designed detection policies leveraging the quickest change detection to minimize delay in event reporting while adhering to energy constraints.

  3. 📊 Asymptotic Analysis of Local Change Detection:
     Conducted asymptotic performance analysis of local change detection algorithms for distributed sensor networks under limited energy budgets.

🔗 Related Research Repositories

• Related Research Project: Interpretation Error in Dynamical Systems
  Focuses on defining and describing a specific type of modeling error, termed the interpretation error, that may arise from the wrongful interpretation of the physical interactions within a dynamical system.

Each project includes code, results, and documentation to support reproducibility and industrial relevance.

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📂 Repository Structure

📁 research-portfolio/
│
├── cloud-native-contoller/
│   ├── controller.py
│   ├── cloud_lqr.py
│   ├── plant.py
│   ├── k8s_delay.yml
│   ├── sigma_gamma_tilde.py
│
├── data-driven-modeling-control/
│   ├── calibrationUtilities.py
│   ├── gradientHypUtilities_v4.py
│   ├── hyperparam_tuning_v4.ipynb
│   ├── utilities.py
│
│
├── distributed-detection/
|   ├── dynamic-programming/
│   │   ├── cau_non_adap_heu_main.m
│   │   ├── fin_hrz_non_cau_dp.m
│   |── quickest_change_detection/
│   │   ├── fc_cusum.py
│   │   ├── non_causal_opt.m
│   │   ├── opt_thr_obs_pol.m
│   │   ├── thr_fun.m
│   ├── asymptotic-analysis-local-detection/
│   │   ├── norm_mult_usr_seq_harv_t_min.m
│   │   ├── norm_mult_usr_seq_harv_t_max.m
│   │   ├── norm_Seq_maj_log.m
│   │   ├── Lauricella_A.m
│

└── README.md