research

papers and research work.

Research Experience

  • Jan 2026 - present

    Stanford, CA

    Stanford Tambe Lab
    Agentic RTL Design & Verification
    Advisor: Dr. Thierry Tambe
    • Developing reinforcement learning environments and verification-grounded infrastructure for AI agents that autonomously generate and debug Verilog RTL, advancing reliable AI-driven chip design.
    • Building rigorous evaluation pipelines for synthetic RTL benchmarks through specification auditing, coverage-driven testbenches, and deterministic cosimulation-based correctness validation.
  • Sept 2025 - Dec 2025

    Stanford, CA

    Stanford Robust Systems Group
    Technology-Architecture Co-Design
    Advisor: Dr. Subhasish Mitra
    • Co-developed technology-architecture co-design framework for symbolic modeling of system-level performance and scaling tradeoffs.
    • Implemented symbolic memory models by porting CACTI’s delay/energy equations to Python for integrated tech-arch optimization.
  • Jan 2025 - Jun 2025

    Stanford, CA

    Stanford Accelerate
    Model Quantization and Sparse Accelerators
    Advisor: Dr. Priyanka Raina
    • Developing a post-training quantization pipeline using PyTorch incorporating model-specific pre-processing and quantization specs.
    • Researching KV-Cache optimization and quantization/sparsity techniques for implementation in ASIC-based sparse accelerator.
  • Apr 2021 - Sept 2023

    Stanford, CA

    Stanford Robust Systems Group
    Emerging Memory Device (RRAM) Modeling
    Advisor: Dr. Subhasish Mitra
    • Developed deep neural networks and probabilistic models to predict statistical behavior of RRAM during SET/RESET programming.
    • Described use case in which model serves as key testing component in functional verification of multiple-bits-per-cell RRAM controller.
    • First author on IEEE research paper and presented at SISPAD 2023 in Kobe, Japan.
  • Jun 2022 - Sept 2022

    Stanford, CA

    Stanford AI in Medical Imaging (AIMI) Center
    ML for Chest X-Ray Analysis
    Computer vision, medical imaging
    • Developed novel CV pipeline to identify and box central venous catheters, endotracheal tubes, and chest tubes in chest X-ray data.
    • Achieved 97% accuracy across multiple test sets, won first place award, and presented work at AIMI Symposium 2024.