About Me
I'm a PhD candidate at MESL of UCSD CSE, advised by Rajesh K. Gupta and Earlence Fernandes. I obtained my bachelor degree in Electrical and Computer Engineering from University of Michigan-Shanghai Jiaotong University Joint Institute (UM-SJTU JI).
My research interest spans widely, including CPS-IoT (smart buildings in particular), security, privacy, and applied cryptography. I missioned myself to help the general public embrace the utility and productivity of evolving technologies such as AI, smart hardware, etc. without being worried about various security, privacy, and safety issues including but not limited to ubiquitous surveillance, personal info leakage, and data misusage.
I'm currently looking for 2024 summer internships and will be looking for full time job opportunities for 2025 summer. Feel free to reach out if you find my work interesting!
Education
Interests
Selected Research
Brick & Building Operating System
In this project, we are aiming to create a Building Operating System (BOS) providing an interface between the underlying building hardware (e.g. HVAC system, light, sensor) and users (e.g. developers, building managers, residents). With this interface, developers can create applications to make the life of building managers and residents easier. Paper undersubmission to ICCPS 2024.
Misusing Tools in Large Language Models With Visual Adversarial Examples
Large Language Models (LLMs) are being enhanced with the ability to use tools and to process multiple modalities. These new capabilities bring new benefits and also new security risks. In this work, we show that an attacker can use visual adversarial examples to cause attacker-desired tool usage. For example, the attacker could cause a victim LLM to delete calendar events, leak private conversations and book hotels. Different from prior work, our attacks can affect the confidentiality and integr ...
Physics-Informed Data Denoising for Real-Life Sensing Systems
Sensors measuring real-life physical processes are ubiquitous in today’s interconnected world. These sensors inherently bear noise that often adversely affects performance and reliability of the systems they support. Classic filtering-based approaches introduce strong assumptions on the time or frequency characteristics of sensory measurements, while learning-based denoising approaches typically rely on using ground truth clean data to train a denoising model, which is often challenging or prohi ...
Context-Aware, Continuous Authentication Using Biometrics & Fuzzy Extractors
In our work, we deviate from the status quo and show how users can authenticate themselves using biometrics whilst. We utilize primitives from cryptography -- namely fuzzy extractors -- to ensure that there is no requirement to perform template matching (of a template stored in the clear) on trusted hardware.
Improving gVisor Memory Subsystem Performance
In this project, we analyzed the performance of the gVisor memory management subsystem, starting from benchmarking malloc and ending up focusing on MMAP. We further profiled MMAP performance within gVisor and identified its bottlenecks. We proposed an optimization in the free page searching algorithm of virtual memory space within gVisor (from O(N) to O(logN)). This optimization patch has been merged into production.