Research / Exploration Topics
🔹 System & Performance Oriented
High-performance computing in .NET – explore how Span, Memory, and ValueTask improve performance. Benchmark against traditional arrays and tasks.
Memory management research – analyze how the .NET garbage collector behaves under different workloads and optimize allocation-heavy code.
Parallelism and concurrency – implement CPU-bound tasks with Task Parallel Library (TPL) vs async/await vs Channels, and compare efficiency.
Custom memory allocator in C# – simulate or implement a specialized memory pool and measure its efficiency for game engines or simulations.
🔹 AI / Machine Learning
ML.NET exploration – train models for text classification, recommendation systems, or anomaly detection using C#.
Neuroevolution in C# – write a genetic algorithm that evolves a neural network to solve a simple problem (e.g., Flappy Bird bot).
Symbolic AI in C# – implement a rule-based system (like an expert system) and compare it with ML-based approaches.
🔹 Web & Cloud
Real-time data visualization in ASP.NET Core – build a dashboard using SignalR for real-time updates (e.g., stock market or IoT data).
C# in serverless computing – deploy Azure Functions written in C#, research cold-start performance.
API performance comparison – benchmark REST vs gRPC vs GraphQL in .NET Core.
🔹 Security & Cryptography
Custom cryptographic algorithm in C# – implement and test a lightweight cipher, then analyze security/performance.
Static code analysis tool – build a tool in C# that scans .NET codebases for vulnerabilities or bad practices.
Blockchain prototype in C# – implement a mini blockchain system with consensus (PoW/PoS) and measure performance.
🔹 Graphics & Game Development
Procedural content generation – generate random maps, mazes, or terrain in C# (Unity or raw GDI+).
Physics engine exploration – implement a 2D rigid-body physics simulation in C#.
Ray tracing with C# – research GPU vs CPU performance in rendering basic 3D scenes.
🔹 Data Science & Algorithms
Big data processing in C# – experiment with handling huge datasets using LINQ, PLINQ, and streaming APIs.
Graph algorithms – implement and benchmark algorithms like PageRank, shortest path, and community detection.
Genetic algorithm & optimization – use C# to solve NP-hard problems (scheduling, traveling salesman).