research

I am interested in combining artificial intelligence with connected devices to solve practical problems. My interests include the Internet of Things (IoT), intelligent systems, and intelligent vehicles and transportation. I am currently developing the mathematical, programming, and systems foundations needed to pursue research in these areas.

AI and the Internet of Things

I am exploring how intelligent software and connected devices can support everyday tasks. My ongoing Smart Medication Management System combines medication reminders and digital tracking with a Bluetooth-enabled pillbox concept and a WeChat Mini Program in development. This work is helping me build practical skills in IoT, product design, and problem solving while exploring medication adherence and smart healthcare applications.

Autonomous driving

I am interested in deep learning and sensor-fusion methods that improve perception, planning, and control for self-driving vehicles. Relevant problems include obstacle detection, path planning, and real-time traffic-aware decision-making.

Vehicular communication

Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication can improve road safety and traffic efficiency. I am interested in predictive models that help vehicles coordinate their behavior using shared information.

Human-vehicle interaction

Autonomous systems must communicate their intentions clearly and support safe, intuitive interaction. I am interested in interfaces that improve trust, usability, and situational awareness for drivers and passengers.

Sustainable transportation

AI can contribute to more sustainable mobility through route optimization, reduced emissions, electric-vehicle integration, and smart-city coordination. I hope to explore how intelligent transportation systems can balance efficiency, safety, and environmental impact.

Academic foundations

• Computer Programming: Python and Java programming fundamentals, data types, control flow, functions, and error handling.
• Object-Oriented Programming: encapsulation, inheritance, polymorphism, abstraction, and modular software design.
• Data Structures: arrays, linked lists, stacks, queues, trees, graphs, search and sorting algorithms, and complexity analysis.
• Computer Architecture: CPU organization, instruction-set architecture, memory hierarchies, input/output systems, and foundations of parallel computing.
• Operating Systems: processes, concurrency, memory management, file systems, device management, and system security.
• Machine Learning: regression, support vector machines, decision trees, clustering, feature engineering, model evaluation, and hyperparameter tuning.
• Discrete Structures: logic, set theory, combinatorics, graph theory, and algorithms.
• Probability and Statistics: random variables, distributions, statistical inference, and applications to data analysis and machine learning.