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Khabusi Simon Peter (PhD)

Khabusi Simon Peter, PhD

Lecturer

  • Ph.D. in Electrical Engineering from National Tapei University of Technology (2025)
  • Master of Technology in Computer Science & Engineering from Delhi Technological University (2025)
  • Bachelor of Computer Engineering from Busitema University (2016)

TECHNICAL SKILLS

  • Artificial Intelligence: Heuristic search, hill climbing, production systems, fuzzy systems, neuro-fuzzy systems.
  • Programming Languages: Python, C++, C, MATLAB, PHP, Python-Flask, MySQL.
  • Computer Science Theory: Set Theory, Probability Theory, Statistics, Linear Algebra, Discrete Mathematics, Digital Logic, Data Structures and Algorithms.
  • Mathematical Optimization: Bayesian Optimization, Linear Programming, Network Flow Theory, Simulated Annealing, Constraint Formulation, Sensitivity Analysis, Gaussian and Poisson Distributions.
  • Machine Learning: Supervised - Linear Regression, Logistic Regression, Decision Trees, Random Forests, KNNs, SVMs, Gaussian Process Regression, Neural Networks; Unsupervised - KMeans, Principal Component Analysis (PCA); Time series Analysis-RNNs, GRUs, LSTMs, Linear Dynamical Systems, Dynamic Mode Decomposition (DMD).
  • Deep Learning & Computer Vision: Image classification, object detection, image segmentation, activity recognition - CNNs, Vision Transformers (ViT), Generative Modeling-GANs, VAEs, Diffusion Models; Reinforcement Learning (Deep RL)- DQN, DDQN, PPO, A3C.
  • Data Science Tools: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, TensorFlow, Keras, etc.
  • Data Systems: Database Management Systems (DBMS)-MySQL, SQLite, Relational Schema Design; Data mining and warehousing, pattern recognition.
  • Information and Network Security: Network Monitoring-firewall configuration (Cisco), Wireshark; Data Backup & Recovery, System Hardening, Information Security (IPsec, SSL and TLS, DTLS, SNMP, Kerberos, HTTP and HTTPS).
  • ICT Infrastructure: System and Application Software Support, Server Administration, LAN/WAN Configuration, End-User Support.
  •  A segmentation–classification pipeline using attention-augmented U-Net, optimized neuro-fuzzy systems and multilayer convolutional neural network-online sequential extreme learning machine for accurate fish disease recognition from underwater imagery.
  • A three-stage framework for real-time water quality data labeling, prediction and forecasting combining fuzzy inference, compact adaptive neuro-fuzzy systems, and hybrid LSTM-Transformer model.
  • A novel weighted fuzzy rough set-based feature selection method to handle high-dimensional data efficiently, solving the equal fuzzy dependency degree problem inherent in fuzzy rough quick reduct algorithm.

Email: skhabusi@utamu.ac.ug

Curriculum Vitae