EECS

Course Overview An intensive exploration of advanced computer architecture and hardware-software co-design. The coursework focused on processor microarchitecture, memory hierarchy, cache coherence protocols (such as MSI), and hardware-level performance optimization. Assignments were heavily implemented in C and RISC-V Assembly to analyze instruction-level parallelism, pipeline behavior, and data-level parallelism via vector extensions. Core Laboratory Projects 1. Microarchitectural Profiling & Pipeline Hazards Co-designed and executed microbenchmarks in C to reverse-engineer hardware memory hierarchies and expose processor pipeline characteristics. ...

Course Overview An intensive, project-driven exploration of operating systems and system programming. The coursework focused on kernel-level development, concurrency control, memory management, and file systems. A significant portion of the assignments was implemented in Rust to enforce memory safety and data-race freedom in complex concurrent environments. Core Project: Pintos OS Kernel Development Co-developed a monolithic instructional operating system (Pintos) in C, implementing critical subsystems from the ground up. 1. User Programs Engineered a secure, POSIX-compliant system call interface and process execution environment, implementing rigorous user-kernel boundary validation and memory protections to guarantee kernel integrity. ...

Course Overview An intensive, project-driven exploration of computer security principles and applied cryptography. The coursework focused on exploiting memory safety vulnerabilities at the binary level, identifying web application flaws, and engineering cryptographically secure systems from the ground up to resist active adversaries. Security Warning: Do not click this suspicious link Core Project: Secure File Sharing System (project2, Go) Architected a secure, end-to-end encrypted, and distributed file-sharing client in Go, capable of resisting active Datastore adversaries attempting to tamper with or leak data. ...

Course Overview An intensive, project-driven exploration of foundational Artificial Intelligence algorithms. The coursework utilized the classic Pacman environment to bridge theoretical AI concepts with practical implementations, covering autonomous planning, decision-making under uncertainty, probabilistic tracking, and modern deep learning architectures. Core Project 1: State-Space Search & Adversarial Planning Engineered autonomous agents capable of navigating complex deterministic mazes and surviving stochastic, adversarial environments. Heuristic Search Strategies: Implemented uninformed search (DFS, BFS, UCS) and informed search (A* Search). Designed highly optimized, custom admissible and consistent heuristics to solve complex state-space problems, such as finding the optimal path to consume all food dots and navigating through specific corner coordinates. Adversarial Game Tree Search: Modeled adversarial ghost behaviors by developing multi-agent search algorithms. Implemented Minimax with Alpha-Beta Pruning to drastically reduce the expanded search space, and Expectimax to account for the stochastic, sub-optimal nature of random ghost movements. Designed custom evaluation functions to dynamically weigh game states. Core Project 2: Reinforcement Learning & MDPs Modeled the Pacman environment as a Markov Decision Process (MDP) to develop agents that learn optimal policies through trial and error. ...

Course Overview An intensive exploration of the hardware-software interface, system-level programming, and modern computer microarchitecture. The coursework ranged from low-level memory management in C and implementing mathematical algorithms in pure RISC-V assembly, to building a pipelined RISC-V processor core from scratch at the digital logic level. Core Project: Game Logic and Low-Level Memory Management in C Developed a complete Snake game engine from scratch in C, focusing on dynamic memory allocation, pointer arithmetic, and the lifecycle management of complex data structures. ...

Course Overview An intensive, transistor-level design project focused on creating a complete EEG signal processing module supporting end-to-end EEG acquisition and conversion. The architecture encompasses a preamplifier, a sampling module, a single-slope analog-to-digital converter (ADC), and a digital-to-analog converter (DAC). Core Project: End-to-End EEG Signal Processing Module Designed and simulated a comprehensive integrated circuit module from the ground up, focusing on the precise amplification and digital conversion of weak biological electrical signals. ...

Course Overview An intensive exploration of deep learning applications in medical imaging. The coursework involved implementing and optimizing advanced neural network architectures for dynamic MRI reconstruction and cardiac image segmentation, balancing computational efficiency with high-fidelity clinical outputs. Core Project 1: Dynamic MRI Reconstruction Engineered a deep learning pipeline for reconstructing dynamic cardiac cine MRI from 5x undersampled k-space data, addressing the trade-off between spatial-temporal resolution and acquisition time. ...

Course Overview A practical exploration of Object-Oriented Programming (OOP) and software design principles through the end-to-end development of a fully functional 2D Roguelike game. The project utilized Python to build custom game mechanics, focusing on event-driven state management, rendering pipelines, and collision physics. Core Project: Python-Based Roguelike RPG Co-developed an expansive, tile-based 2D RPG featuring dynamic map environments, scalable enemy logic, and encapsulated rendering systems. 1. Game Engine & Rendering Subsystems Architected a centralized GameManager infrastructure to orchestrate global event loops, entity updates, and frame-by-frame rendering. ...