Profile Summary

Kyle’s interest in cybersecurity is deeply rooted in the exploration of advanced topics such as advanced computer architecture, network and web security and cryptography engineering. He is keen on understanding how complexity and system performance engineering can be leveraged to fortify cybersecurity frameworks. Additionally, Kyle is interested in the mathematical foundations that underpin these areas, such as calculus, linear algebra, algebra theory, number theory, probability theory, information theory, complexity theory, differential equations, artificial intelligence and machine learning. Furthermore, his curiosity extends to emerging technologies like quantum computing, where he seeks to explore their potential impact on cybersecurity and other areas of technology. Kyle’s other interests in cybersecurity include ethical hacking, penetration testing and privacy engineering in sofware systems.

Outside his academic pursuits, Kyle dedicates time to coding projects in his down time to challenge his skills and deepen his understanding of computing and programming principles. He is also committed to self-learning cybersecurity, mathematics and quantum physics or quantum computing.

Skills

Technical skills

Python, C#, MATLAB, R, MS Office Suite, LaTeX

Language skills

English (Fluent), Mandarin (Conversational), Malay (Conversational)

Soft skills

Creativity, Critical thinking, Problem solving, Problem analysis, Independence, Teamwork

Published Computer Science and Physics Papers

Computational Physics Collision Simulation (1^st)

Computational Collision Physics Paper

This project focuses on collision physics which is simulated through Python. Using both elastic and inelastic collisions, the simulation presents the dynamics of colliding entities in one and two dimensions, effectively demonstrating the principles of momentum and kinetic energy conservation. By applying classical mechanics, particularly Newtonian motion laws, this paper at the mechanisms of momentum and energy transfer during collisions, along with the consequences of various collision types on these parameters. The simulation is designed to not only accommodate simulate elastic and inelastic collisions but also offer visual representations of the outcomes. Through these simulations, the project paper evaluates the influence of parameters such as mass, velocity and impact angle on collision results.

Published Computer Science and Mathematics Papers

Computational Mathematics Differential Equations Project (1^st)

Optimization Problem With Differential Equations Paper

This project is about applying differential equations in Python to tackle a self-created optimization problem. The main objective of this project is to either minimize or maximize objective functions while accommodating a range of physical or mathematical constraints delineated by first-order, higher-order and partial differential equations, which are introduced and explained in this paper. Furthermore, results of the solution for the optimization problem are provided and these are emphasized on and explained in the reflection and conclusion sections of the paper.

Education

University College London (UCL)

Qualification: BSc Crime and Security Science (2023 - 2026)

Department: Department of Security and Crime Science, Faculty of Engineering Sciences

Societies: UCL Artificial Intelligence Society, UCL Blockchain Labs Society, UCL Physics Society, UCL Malaysian Society

Year 1 Modules: Crime Mapping, Understanding the Crime Event, Qualitative Methods, Probability and Statistics I, Crime and Society, Introduction to Crime and Security science, Terrorism, Programming for Crime Scientists

Year 2 Modules: Systems and Problem Solving, Situational Crime Prevention, Probability and Statistics II, Psychology and Crime, Introduction to Research, Security Technologies, Organised Crime, Project in Security and Crime Prevention

Year 3 Modules: Cybercrime, Data Science for Crime Scientists, Criminal Investigation and Intelligence, Security and Crime Science Research Project

University of Warwick

Qualification: IFP Computer Science (2022 - 2023)

Department: Department of Computer Science, Faculty of Engineering

Societies: Warwick Computing Society, Warwick Maths Society, Warwick Physics Society, Warwick Malaysian Student Association

Modules: Further Mathematics and Statistics, Pure Mathematics, Computer Science, English for Academic Purposes, Inquiry and Research Skills

St Joseph Instituition International School

Qualification: IGCSE (2020 - 2022)

Societies: Competitive Mathematics squad, Chess, Exploring the Blockchain, Football

Subjects: International Mathematics, Physics, Chemistry, First Language English, English Literature, Economics, Foreign Language Mandarin, Geography

Community Outreach Programme

Refugee Outreach Campaign (2022) - Raised donations, collection of recycling items for international refugees

Community service project (2021) - 12 Hour Run, raised funds for ‘Refugee Community’

Community service project (2019) - Raised funds for underprivileged community, contributed to environment clean up

Physics Membership

Institute of Physics

Roles: Associate member of the UK physics community (September 2024 - )

Interests: Semiconductor physics, Computational phyics, Quantum physics, Nuclear physics, Particle Physics, Condensed Matter Physics, Solid State Physics

Personal Interests

Physics

Understanding the principles of quantum physics
Quantum superposition of interacting quantum particles
Quantum entanglement of interacting quantum particles
Quantum many-body systems of interacting quantum particles
Quantum many-body states of interacting quantum particles
Quantum equillibrium dynamics of quantum systems and states
Quantum non-equillibrium dynamics of quantum systems and states
Quantum state representations of quantum systems and vectors
Quantum state transformations of quantum systems and vectors
Quantum electrodynamics of quantum electric charges and fields
Quantum flux parametron of Josephson circuits
Quantum hall effect of 2-D electron systems
Quantum supersymmetry of elementary particles
Quantum harmonic oscillator of simple harmonic motion
Quantum field theory of quantum fields
Quantum matrix theory of quantum matrices
Quantum theory of matter in quantum states and systems
Understanding the principles of plasma physics
Plasma approximation as a state of matter
Plasma ionization degree as a state of matter
Plasma density degree as a state of matter
Plasma temperature as a state of matter
Plasma potential as a state of matter
Plasma magnetization as a state of matter
Plasma modelling as a state of matter
Understanding the principles of fluid physics
Newtonian equations of fluids
Non-Newtonian equations of fluids
Navier-Stokes equations of fluids
Stokes-theorem equations of fluids
Compressible flow equations of fluids
Non-compressible flow equations of fluids
Steady flow fluid motion of fluids
Non-steady flow fluid motion of fluids
Principles of particle physics
Standard Model of particles
Feynmann diagrams of particles
Mechanics of simulating particle accelerators
Mechanics of simulating cyclotrons
Mechanics of simulating synchotrons
Weak interaction of particles
Strong interaction of particles
Cosmic rays of particles in chambers
Higgs Boson application in particle accelerators
Principles of nuclear physics
Nuclear stability of radioactive elements
Nuclear models of radioactive elements
Nuclear fission of radioactive elements
Nuclear fusion of radioactive elements
Radioactive decay of radioactive elements
Mechanics of nuclear reactors for experiments
Mechanics of nuclear weapons for warfare
Understanding the principles of condensed matter physics
Crystallography properties of solid and liquid matter objects
Electronic structure properties of solid and liquid matter objects
Superconductivity properties of solid and liquid matter objects
Optical properties of solid and liquid matter objects
Lattice properties of solid and liquid matter objects
Magnetic properties of solid and liquid matter objects
Experimental techniques of solid and liquid matter objects
Understanding the principles of solid state physics
Crystallography properties of solid matter objects
Electronic structure properties of solid matter objects
Superconductivity properties of solid matter objects
Optical properties of solid matter objects
Lattice properties of solid matter objects
Magnetic properties of solid matter objects
Thermal properties of solid matter objects
Experimental techniques of solid matter objects
Understanding the principles of thermodynamics physics
Laws of thermodynamics and energy changes in systems
Equilibrium thermodynamics of systems
Non-equillibrium thermodynamics of systems
Thermodynamical system models of matter states
Thermodynamical states and processes of system models
Understanding the principles of statistical physics
Particle statistics of atomic motion
Chemical reactions of statistical systems
Flow of particles and heat in statistical systems
Kinetic theory of gases in statistical systems
Equation of state of gases in statistical systems
Statistical methods of particle assesmblement in statistical systems
Probability theory of particle movement in statistical systems
Probability distrubitions of particle movement scenarios
Monte Carlo method applications in statistical systems
Stochastic method applications in statistical systems
Non-equillibrium thermodynamics of statistical system processes
Understanding the principles of semiconductor physics
Electronic band structures of semiconductor materials
Conductivity band structures of semiconductor materials
Energy band structures of semiconductor materials
Charge carrier structures of semiconductor materials
P-N junctions of semiconductor materials
Metal-Semiconductor junctions of semiconductor materials
Carrier generation and recombination of semiconductor materials
Principles of electromagnetism physics
Electric flux and Gauss’s law applications
Magnetic flux and Gauss’s law for magnetism
Transformers and energy in magnetic fields
Energy and momentum in electromagnetic waves
Wave equation for electric and magnetic fields
Transformations of electric and magnetic fields

Computer Science

Understanding the principles of quantum computing
Quantum algorithm simulations in quantum systems
Quantum circuit simulations in quantum systems
Quantum logic gate simulations in quantum systems
Quantum error correction simulations in quantum systems
Quantum AC Josephson effect in Josephson junction circuits
Quantum DC Josephson effect in Josephson junction circuits
Quantum inverse AC Josephson effect in Josephson junction circuits
Quantum information theory of quantum communication channels
Quantum complexity theory of computational capabilities and limiations
Quantum computability theory of computational capabilities and limiations
Quantum error correction theory of computational capabilities and limiations
Quantum information processing of data transportation
Quantum Fourier transform applications in exponential searching algorithm simulations
Quantum Laplace transform applications in forming complex frequency domains
Quantum annealing optimization techniques in quantum systems
Quantum network channel applications for communication
Quantum supremacy applications for speedy calculations
Quantum cryptography applications for advanced security
Quantum machine learning applications in training models
Quantum decoherence implementation for improved efficiency in quantum computers
Quantum parallelism implementation for improved efficiency in quantum computers
Quantum Turing machine theory and its application in quantum computers
Neuromorphic-based quantum computing techniques in quantum computers
Adiabatic-based quantum computing techniques in quantum computers
Meausrement-based quantum computing techniques in quantum computers
Topological-based quantum computing techniques in quantum computers
Principles of graph network computing
Graph theory diagrams in graph networks
Graph theory algorithm applications in graph networks
Matrix structures in graph networks
Network flow structures in graph networks
Tree diagram structures in graph networks
Graph metric structures in graph networks
Types of graphs in graph networks
Data structure and algorithm applications in computing
Data structure and algorithm design techniques in computing
Types of data structures used in computing
Types of data algorithms used in computing
Types of data models used in computing
Database applications in computing
Database design techniques in computing
Types of databases used in computing
Roles of databases in computing
Understanding the principles of architecture systems in computing
Understanding the principles of cybersecurity in computing
Understanding the principles of hardware in computing
Understanding the principles of algorithms in computing
Understanding the principles of computational techniques in computing

Mathematics

Mathematical analysis of limits, sequences, series, functions
Systems of linear equations and matrices in multi-dimensions
Understanding the principles of vector spaces and linear transformations
Understanding the principles of modelling and data analysis
Understanding the principles of multi-dimensional vector calculus and differential operators
Understanding the principles of statistical interference and modelling in computers
Understanding the principles of statistical theory to develop and evaluate optimum statistical procedures
Applications of group theory and rings
Lebesgue’s theory of measure and integration in partial differential equations
Mathematical theory of analyzing complex model systems
Mathematical theory of analyzing formal mathematical structures in logic and set theory
Number theory and properties of natural numbers and prime numbers
Probability concepts of the joint behaviour of several random variables
Computational methods used to solve linear systems and other linear algebra computations
Computational methods used for solving partial differential equations and spectral methods
Computational methods used for the Monte Carlo methodology
Numerical solutions and methods for solving ordinary differential equations
Markov processes and modelling of random evolutions
Geometric analysis of complex numbers and 2-D problems in physics
Functional analysis of vector spaces and linear maps
Mathematics of Navier-Stokes derivation and simplification methods of fluid motion equations
Mathematics of Navier-Stokes derivation and simplification methods of boundary layer equations
Mathematics of Newtonian mechanics and dynamics of forces, energy, momentum
Linear algebra techniques in simplifying matrices and systems of linear equations
Linear algebra applications in eigenvalues in eigenvectors
Linear algebra applications in Markov matrices and Fourier transform
Linear algebra applications in orthogonal vectors and subspaces
Linear algebra applications in orthogonal matrices and Gram-Schmidt
Linear algebra applications in projection matrices and least squares
Linear algebra applications in complex matrices and Fast Fourier Transform
Linear algebra applications in graphs and networks
Single variable calculus techniques in approximation and graph sketching
Single variable calculus techniques in optimization and related rates
Single variable calculus techniques in solving differential equations
Single variable calculus techniques in solving integral equations
Single variable calculus applications in finding areas and volumes
Single variable calculus applications in probability and numerical integration
Multivariable calculus techniques in determining tangent optimization and approximation problems
Multivariable calculus techniques in determining gradient and directional derivatives problems
Multivariable calculus techniques in determining Lagrange multipliers and constrained differentials
Multivariable calculus techniques in solving double integrals and line integrals in a 2-D plane
Multivariable calculus techniques in solving triple integrals and line integrals in a 3-D plane
Multivariable calculus techniques in solving first-order differential equations
Multivariable calculus techniques in solving second-order differential equations
Multivariable calculus techniques in solving Fourier series equations
Multivariable calculus techniques in solving first-order systems

Engineering

Understanding the principles of electrical engineering
Computer modelling techniques used to solve and evaluate the performance of engineering systems
Kirchhoff’s current and voltage laws to calculate currents and voltages
The concept of power in AC and DC circuits in real transformers and other circuits
The concept of linearity and time-invariance in electrical circuits
Design issues in building multistage amplifiers with desired gain and bandwidth
Basic mathematical tools in signals and systems analysis
Basic elements of digital communications systems and electrical circuits
Principles of electronic engineering
Analysis and modelling of simple control systems
Physical principles, operating parameters and small signal analysis of BJT and MOSFET
Internal architecture of a typical microprocessor system
Relationship between assembly language and machine code
The function of flip-flops, registers and memory, applications in sequential logic circuits
Description and analysis of electromagnetic fields applied electronic engineering
Electrical properties of semiconductors controlled and used in electronic device
Quantitative mathematical, scientific and engineering tools to the analysis of communications-related problems
Design circuits and systems to implement communications systems and sub-systems
Principles underlying communications systems and information theory
Analysis and design of high frequency electronic systems and components
Formation, electrostatics and transport of metal-semiconductor contacts
Formation, electrostatics and transport of metal-oxide-semiconductor contacts
Structure and characteristics of light-emitting diodes and semiconductor lasers
Analysis of high-frequency response in discrete and integrated amplifiers
Analysis of low-frequency response in discrete-circuit amplifiers
Analysis and design of opto-electronics sytstems and optical communications systems
Analysis and design of photonic systems and sub-systems
Understanding the principles of analogue and digital electronics and associated components
Understanding the principles of analogue and digital signals and systems
Understanding the principles of single-stage MOS amplifiers
Understanding the principles of differential MOS amplifiers
Understanding the principles of network engineering
Network security protocols and encryption
TCP/IP protocol stack optimization
Wireless communication standards and protocols
Software-defined networking (SDN) architecture
Network traffic analysis and monitoring
High-speed data transmission technologies
IP address management and allocation
Network latency and bandwidth optimization
Fiber optic communication system design
Virtual Private Networks (VPN) implementation
Routing algorithms and network topologies
Network scalability and performance metrics
Domain Name System (DNS) configuration
Internet Service Provider (ISP) architecture
Network load balancing and redundancy
Quality of Service (QoS) assurance
Network intrusion detection and prevention
IPv6 addressing and transition strategies
Wireless mesh network design principles
Ethernet and Wi-Fi network standards
Bandwidth throttling and traffic shaping
Network access control (NAC) systems
Real-time data packet analysis

Quantum Computing

Understanding and manipulating quantum bits (qubits)
Exploring superposition and entanglement in qubits
Implementing single-qubit gates like Pauli-X, Pauli-Y, Pauli-Z and Hadamard
Using multi-qubit gates such as CNOT, Toffoli and SWAP
Designing and visualizing quantum circuits
Optimizing and finding equivalence in quantum circuits
Applying the Quantum Fourier Transform (QFT)
Applying Shor’s algorithm for factoring integers
Implementing Grover’s algorithm for database searching
Exploring quantum error correction codes, including Shor and Steane codes
Identifying and mitigating sources of noise in quantum systems
Techniques for reducing quantum decoherence
Conducting projective measurements in quantum systems
Utilizing Positive Operator-Valued Measures (POVM)
Performing measurements in different bases
Comparing quantum and classical complexity classes
Learning about quantum key distribution (QKD) protocols such as BB84 and E91
Measuring entropy and information in quantum systems with von Neumann entropy
Understanding and implementing quantum mutual information
Exploring the protocol and significance of quantum teleportation
Comparing the circuit model, MBQC and topological quantum computing
Investigating superconducting qubits, trapped ions, and photonic qubits
Simulating physical systems using quantum computers
Understanding the principles and applications of quantum annealing
Comparing quantum and classical annealing techniques
Utilizing the Variational Quantum Eigensolver (VQE)
Exploring the Quantum Approximate Optimization Algorithm (QAOA)
Encoding data in quantum systems for machine learning
Developing quantum neural networks and support vector machines
Investigating quantum repeaters for quantum networking
Techniques for reconstructing quantum states in state tomography
Applying quantum metrology to enhance measurement precision
Techniques for quantum control and optimal control
Generating true randomness using quantum processes
Investigating quantum optics and states of light like squeezed states and entangled photons
Exploring quantum strategies in game theory
Comparing quantum and classical game theory
Understanding quantum speedup and practical limits
Studying relations between quantum and classical circuit complexity
Understanding the evolution of quantum systems over time
Simulating Hamiltonian evolution in quantum systems
Developing and applying quantum sensors for precision measurements

Cybersecurity

Understanding the principles of cybersecurity
Identifying and mitigating various types of cyber threats
Implementing network security measures
Exploring encryption techniques and algorithms
Applying public key infrastructure (PKI)
Conducting risk assessment and management
Developing security policies and procedures
Implementing secure software development practices
Exploring the principles of ethical hacking
Conducting penetration testing and vulnerability assessments
Using intrusion detection and prevention systems (IDS/IPS)
Understanding firewalls and their configurations
Implementing multi-factor authentication (MFA)
Securing wireless networks
Protecting data in transit and at rest
Exploring endpoint security solutions
Understanding malware analysis and mitigation
Implementing secure access control mechanisms
Conducting digital forensics and incident response
Understanding social engineering attacks and defenses
Exploring the principles of cryptographic protocols
Implementing secure coding practices
Protecting against Distributed Denial of Service (DDoS) attacks
Understanding threat intelligence and sharing
Exploring security information and event management (SIEM) systems
Implementing data loss prevention (DLP) solutions
Understanding compliance and regulatory requirements (e.g., GDPR, HIPAA)
Protecting cloud environments and services
Exploring identity and access management (IAM)
Implementing secure virtualization practices
Understanding the principles of zero trust architecture
Exploring blockchain technology for security applications
Protecting critical infrastructure systems
Understanding secure network architecture design
Implementing mobile security measures
Exploring biometric security solutions
Understanding the principles of security auditing and monitoring
Protecting against insider threats
Exploring the principles of privacy and data protection
Implementing secure email communication
Understanding phishing and spear-phishing attacks
Exploring secure file sharing and collaboration tools
Implementing secure remote access solutions
Understanding ransomware attacks and defenses
Exploring artificial intelligence and machine learning for cybersecurity
Protecting Internet of Things (IoT) devices and networks
Exploring threat hunting techniques
Understanding the principles of secure DevOps (DevSecOps)
Implementing security in software development life cycle (SDLC)
Exploring the principles of application security
Protecting against SQL injection and other web vulnerabilities
Understanding the principles of security testing
Implementing security awareness training programs
Exploring the principles of security governance
Understanding the principles of cyber resilience
Exploring the principles of security metrics and reporting
Implementing secure communication protocols
Understanding the principles of network segmentation
Exploring the principles of security in supply chain management

Machine Learning

Supervised learning algorithms and their applications
Techniques in unsupervised machine learning models
Principles of reinforcement learning for tasks
Models and applications of deep learning
Architecture of neural networks in detail
Convolutional neural networks for image recognition
Recurrent neural networks for sequence data
Long Short-Term Memory networks for sequences
Natural language processing with machine learning
Sentiment analysis using various learning algorithms
Machine learning applications in image recognition
Algorithms for object detection in images
Transfer learning applications in neural networks
Generative adversarial networks for image generation
Support vector machines in classification
Decision tree algorithms for predictive modeling
Random forest classifiers for ensemble learning
Gradient boosting machines for improved accuracy
K-nearest neighbors for classification tasks
Principal component analysis for data reduction
K-means clustering methods for groupings
Hierarchical clustering techniques for data organization
Techniques for dimensionality reduction in datasets
Feature selection methods for model improvement
Evaluating and validating machine learning models
Cross-validation strategies for model accuracy
Overfitting and underfitting in learning models
Managing the bias-variance tradeoff effectively
Ensemble learning methods for model robustness
Techniques for hyperparameter tuning in models
Gradient descent optimization algorithms in practice
Bayesian optimization techniques for hyperparameters tuning
Analyzing and forecasting with time series
Anomaly detection using machine learning techniques
Predictive maintenance through machine learning models
Autonomous systems and robotics using AI
Recommendation systems powered by machine learning
Ethical considerations in machine learning applications
Explainability and interpretability of machine learning

Projects

Python

Python (Programming for Crime Scientists - UCL)

This programming for crime scientists GitHub repository contains Python programming projects focused on fundamental programming concepts such as data structures, types, iteration and file input/output. In addition to these core principles, it includes the use of mathematical and statistical packages to analyze real-world crime-related datasets. These datasets include crime reports, survey data and community data, providing practical applications of computational methods. Weekly lab exercises were provided for the module and I leveraged on these experiences to refine my programming skills and analytical techniques for crime data analysis. Please refer to the github repository below to view the source code of the projects I worked on.

kylenyh Programming for Crime Scientists UCL Github

Python (Quantum Physics - Personal)

This quantum physics Github repository is a fork of a quantum physics and quantum optics repository designed to provide hands-on learning through interactive Jupyter notebooks. The content primarily focuses on the fundamental principles of quantum mechanics and quantum optics, with practical implementations using QuTiP (Quantum Toolbox in Python), which is a powerful Python library for simulating quantum systems. The primary goal of this repository is to facilitate self-learning for those interested in quantum physics, quantum computing and quantum optics through real-world demonstrations, coding exercises and interactive simulations. I have been leveraging these materials to deepen my understanding of both theoretical and computational aspects of quantum mechanics. Please refer to the github repository below to view the source code of the Jupyter notebooks.

kylenyh Quantum Physics Github

Python (Quantum Computing - Personal)

This quantum computing Github repository is a fork of a linear algebra and mathematical repository designed for understanding quantum computing. The materials are structured as Jupyter notebooks and Python scripts, providing an interactive and practical approach to mastering the foundational concepts required for quantum computation. The content is ideal for both beginners and those with a foundational knowledge of mathematics who want to deepen their understanding of the computational aspects of quantum theory. I have been self-learning through the Jupyter notebooks provided here, exploring the key mathematical tools needed for quantum computing, while leveraging the Python scripts for hands-on experience with quantum algorithms and simulations. Please refer to the github repository below to view the source code of the Jupyter notebooks.

kylenyh Quantum Computing Github

Python (Classical Physics - Personal)

Python (Game Development - Personal)

This game development GitHub repository showcases several Python-based projects, including a chess engine, snake game and pong game, each designed with unique mechanics and gameplay. The chess engine incorporates Stockfish engine for strategic gameplay, while the snake and pong games demonstrate smooth animations and responsive controls using the Pygame library. These projects reflect my skills in Python programming, game physics implementation and algorithmic problem-solving. The repository is a growing portfolio of my work, with plans to add new features, such as enhanced game modes and additional levels. Please refer to the github repository below to view the source code of present projects and future projects.

kylenyh Game Development Github

Python (Graph Theory - Personal)

This graph theory GitHub repository contains a collection of graph theory algorithms implemented in Python. It features well-known algorithms like Dijkstra’s for shortest paths, DFS/BFS algorithms for graph traversal and Kruskal’s algorithm for minimum spanning trees, among others. These implementations cover essential graph theory concepts such as connectivity, graph coloring and network flow. The repository leverages Python for efficient manipulation and visualization of graph data. The algorithms are optimized for performance, with a focus on time complexity and scalability. Ongoing work includes expanding the repository with more advanced algorithms. Please refer to the github repository below to view the source code of present projects and future projects.

kylenyh Graph Theory Github

Python (Machine Learning - Personal)

kylenyh machine learning Github

R

R (Crime Mapping - UCL)

This crime mapping UCL Github repository showcases my work on crime mapping using Geographic Information System (GIS) techniques, implemented in R. It includes projects focused on analyzing geographical data and visualizing crime patterns, with a strong emphasis on different mapping methods such as heatmaps, choropleth maps, and hotspot analysis. The repository also demonstrates cartographic skills, addressing the strengths and weaknesses of various crime mapping techniques. By applying GIS principles, I explore the practical uses of crime mapping, providing insights into crime trends and spatial distribution. Please refer to the github repository below to view the source code of the projects.

kylenyh Crime Mapping UCL Github

R (Probability, Statistics and Modelling I - UCL)

This probability, statistics and modelling I UCL Github repository contains a collection of R scripts focused on applying quantitative analysis to real-world crime phenomena. The projects include various statistical tests and probability distributions, such as binomial distribution, chi-square tests, t-tests, ANOVA and Poisson distribution. These tools enable rigorous data analysis, fostering an intuitive understanding of uncertainty and providing insights into crime-related datasets. By exploring these quantitative concepts, I demonstrate how data can be formally represented and interpreted to uncover patterns and trends in crime. The repository reflects my work from UCL’s module on quantitative reasoning, providing a solid foundation in both theoretical and practical aspects of crime data analysis. Please refer to the github repository below to view the source code of the projects.

kylenyh Probability, Statistics and Modelling I UCL Github

R (Probability, Statistics and Modelling II - UCL)

This probability, statistics and modelling II UCL Github repository features a collection of R scripts that apply statistical modeling techniques to crime and security science data. The repository covers a wide range of models and tests, including binary and ordinal logistic regressions, multiple linear regression, non-parametric tests (e.g., Mann-Whitney U, Wilcoxon) and correlation tests (Pearson and Spearman). These scripts illustrate the use of generalized linear models with various link functions, emphasizing statistical inference, intuition and interpretation. Each project builds on concepts from UCL’s Probability, Statistics and Modelling I module on statistical analysis, which builds on the foundation of applying quantitative methods to statistically interpret and analyze real-world crime datasets. Please refer to the github repository below to view the source code of the projects.

kylenyh Probability, Statistics and Modelling II UCL Github

R (Data Science for Crime Scientists - UCL)

kylenyh Data Science for Crime Scientists UCL Github

MATLAB

MATLAB (Linear Algebra - MIT)

This linear algebra MIT Github repository contains MATLAB-based projects that apply concepts from MIT’s Linear Algebra (18.06SC) course. It covers topics such as matrix operations, vector spaces, eigenvalues, eigenvectors, and linear transformations. The projects focus on solving systems of linear equations, exploring the properties of matrices and applying linear algebra techniques to real-world problems. Each script demonstrates a practical understanding of the key principles and computations involved in linear algebra, building upon self-learned material. This repository provides a comprehensive exploration of foundational linear algebra concepts, showing their utility in both theoretical and applied contexts. Please refer to the github repository below to view the source code of the projects.

kylenyh Linear Algebra MIT Github

MATLAB (Single Variable Calculus - MIT)

This single variable calculus MIT Github repository features MATLAB-based projects derived from my self-learning of MIT’s Single Variable Calculus (18.01SC) course. The projects focus on fundamental calculus topics, including limits, derivatives, integrals, and series. Through hands-on computations, I explore real-world applications of calculus such as curve sketching, optimization problems and calculating areas under curves. The scripts demonstrate a practical understanding of how these core principles apply to both theoretical and applied problems. This repository reflects my engagement with calculus concepts and showcases how MATLAB can be used to visualize and solve calculus-related challenges. Please refer to the github repository below to view the source code of the projects.

kylenyh Single Variable Calculus MIT Github

MATLAB (Multivariable Calculus - MIT)

This multivariable calculus MIT Github repository contains MATLAB-based projects from my self-learning of MIT’s Multivariable Calculus (18.02SC) course. Topics include partial derivatives, gradients, multiple integrals, and vector calculus. The projects emphasize practical applications such as calculating flux, understanding vector fields, and solving optimization problems involving several variables. Each script showcases a computational approach to visualizing surfaces, integrating functions over complex regions and working with vector-valued functions. This repository demonstrates my ability to apply multivariable calculus concepts to real-world problems, combining theory with MATLAB’s powerful computational tools. Please refer to the github repository below to view the source code of the projects.

kylenyh Multivariable Calculus MIT Github

MATLAB (Differential Equations - MIT)

This differential equations MIT GitHub repository incldues MATLAB-based projects from my self-learning of MIT’s Differential Equations (18.03SC) course. This course provided a comprehensive introduction to the theory and applications of differential equations. My MATLAB projects demonstrate a variety of techniques for solving both ordinary and partial differential equations. These works showcase methods such as separation of variables, eigenvalue problems and numerical simulations. Each project highlights my understanding of the subject and my ability to implement solutions in MATLAB. Additionally, I have focused on real-world applications of differential equations in fields like physics and engineering. Please refer to the github repository below to view the source code of the projects.

kylenyh Differential Equations MIT Github