Bo Chu

Data Cleaning and Multidimensional Statistical Analysis:

Performed data cleaning, deduplication, and threshold-based analysis to create frequency distributions, regional totals, and multi-level aggregation analysis (e.g., grouped statistics of athlete height and weight averages). Utilized SQL queries and Python Pandas for efficient statistical summaries and visualizations, such as top-10 athlete height and weight percentage contributions and regional athlete distribution statistics.

Predictive Modeling and Algorithm Implementation:

Trained a logistic regression model using cleaned data, evaluated its predictive performance with AUC (Area Under the ROC Curve), and applied it to forecast athlete success rates. Applied threshold segmentation and multi-level classifiers to compare regional and event dominance, uncovering success patterns and traits of long-career athletes.

Dynamic Network Architecture and Front-End/Back-End Interaction Optimization:

Built a dynamic network system using IntelliJ IDEA and PostgreSQL, separating user and admin functionalities and enabling real-time data interaction via Java Servlet and JSP. Designed front-end pages using Bootstrap and online auto-design tools, integrated efficiently with back-end APIs for synchronized page and data updates.

Database Optimization and System Feasibility Design:

Designed relational databases based on ER modeling, creating multiple tables (e.g., users, orders, foods) to support many-to-many relationships, optimizing table structures and indexing for improved query efficiency and system stability. Conducted detailed feasibility analysis to define data structures and network interaction logic, resolving data loss and query performance bottlenecks to ensure smooth development.

High-Performance Multiplayer Network Synchronization:

Applied a multiplayer game architecture with Photon.Pun, which supports low-latency client-server communication using PhotonNetwork.ConnectUsingSettings() and PhotonNetwork.JoinLobby() to enable real-time synchronization of player actions, scores, and health states.

Precision Shooting and Weapon Management System:

Using bullet-hit logic by Unity’s Raycast (Physics.Raycast) and real-time damage feedback through RPC calls, developing a modular weapon system that supports dynamic configuration and switching of firing rates, damage values, and bullet trajectory effects.

Dynamic Spawning and Distributed Performance Optimization:

Designed a random spawn algorithm using Random.Range to ensure dynamic distribution of player spawn points for improved fairness; optimized multithreaded callbacks via Photon's MonoBehaviourPunCallbacks mechanism to efficiently handle room lists and player status updates, significantly reducing network latency.

Precise Obstacle Detection and Climbing Response:

Achieved real-time obstacle detection using Physics.Raycast, triggering climbing mode upon detecting objects tagged as "Obstacle" and dynamically activating climbing animations via Animator.SetBool. Seamlessly integrated physics control with visual animations using an event-driven mechanism, ensuring smooth and responsive climbing actions.

Character Control and Performance Optimization:

Dynamically disabled movement components during climbing using Character Controller Manager to prevent logic conflicts, and automatically restored control post-climbing. Optimized collision detection efficiency by dynamically adjusting ray parameters and supported path planning for various obstacle shapes (e.g., flat surfaces, slopes, rooftops).

SVM Model Optimization and Decision Boundary Tuning:

Conducted systematic analysis of model performance by adjusting penalty parameter C and kernel parameter gamma, identifying optimal parameter combinations for classification accuracy, achieving a maximum test accuracy of 97.33%. Dynamically adjusted decision boundaries to evaluate classifier sensitivity and robustness, leveraging SVM's non-linear boundaries to handle complex datasets effectively.

Feature Engineering and Integrated Data Mining Techniques:

Enhanced model performance using all features of the IRIS dataset and validated the impact of feature selection on accuracy by comparing contributions of different feature combinations. Applied data preprocessing techniques like cleaning and deduplication to ensure input data integrity and accuracy, providing a reliable foundation for algorithm optimization and decision-making.