UC Berkeley

4D Holographic Particle Tracking Velocimetry (PTV) (Feb 2026 - Present) Undergraduate Researcher | Computational Imaging Lab, UC Berkeley Advised by Dr. Nalini Singh & Mingxuan Cai Details Lagrangian Representation: Departed from traditional grid-based views by adopting a Lagrangian perspective, using precise floating-point coordinates to represent individual particles for off-grid trajectory tracking. Forward Modeling: Developed an analytical forward projection model to optimize optical kernels, improving the fidelity of in-line holographic reconstructions. Hybrid Initialization: Implemented an Angular Spectrum Method (ASM) combined with Gaussian representations for robust particle localization. Physics-Informed Neural Field: Represented complex velocity fields using Multi-Layer Perceptrons (MLPs) to regularize ill-posed inverse problems in fluid dynamics. Pipeline Engineering: Built an end-to-end pipeline integrating particle localization and 4D flow field reconstruction. Zero-Day Vulnerability Detection via Revelio (Mar 2026 - Present) Research Assistant | Sky Computing Lab, UC Berkeley Advised by Yiwei Hou ...

Course Overview An intensive exploration of computer networking fundamentals, internet architecture, and protocol design. The coursework focused on building core network utilities and foundational protocols from the ground up, moving from the network layer (routing and packet parsing) up to the transport layer (reliable data transfer and flow control). Core Project 1: Robust Network Diagnostic Tool (Traceroute) Engineered a resilient traceroute client capable of handling real-world internet volatility and anomalous router behaviors. ...

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. ...