Research Timeline

Welcome to my research portfolio! Below is an interactive and detailed timeline showcasing my experiences and contributions in cutting-edge research across various institutions. Each project highlights my commitment to innovation and impact in AI, ML, and applied sciences.

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🗓️ Research Timeline

2024

Stanford Artificial Intelligence Lab

AI Researcher | Jun 2024 – Sep 2024

• Key Contributions:
  • Developed LLM agents for AI-driven security, enhancing model performance by 96% using PyTorch and HuggingFace.
  • Built a state-of-the-art golden benchmark for adversarial defenses with C++, Python, Docker, and Linux, under the guidance of Professor Percy Liang.

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Columbia University

LLM Robustness & ML Researcher | May 2024 – Sep 2024

• Key Contributions:
  • Enhanced Large Language Models (LLMs) with equivariant approaches, improving non-semantic adversarials using Deep Neural Networks.
  • Achieved a 15% improvement in attack adaptation with scalable and flexible models.

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Harvard Business School

ML & Data Science Researcher | Mar 2024 – Sep 2024

• Key Contributions:
  • Applied Machine Learning and DiD tests for pharma patent analysis, improving TKDL innovation metrics by 80%.
  • Streamlined 5 years of economic data into 1 month using Deep Learning, Keras, and advanced econometric techniques.

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2023

University of Chicago Booth School of Economics

Financial Q-Learning Researcher | Apr 2023 – May 2024

• Key Contributions:
  • Developed Q-learning and deep learning approaches for chess valuation funded by a $40K grant using TensorFlow and NumPy.
  • Published a first-author paper in MDPI Entropy, leveraging economic techniques to analyze chess performance.

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Yale University

Graph Neural Explainability Math Research Assistant | Mar 2023 – Oct 2023

• Key Contributions:
  • Enhanced GNN interpretability by developing a counterfactual-explainer in PyTorch-Geometric achieving 94% accuracy.
  • Improved explainability metrics by 20% through calculus-based PyTorch implementations and gradient learning.

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2022

Iowa State Agronomy Research Lab

Ensemble Learning AI Researcher | Sep 2022 – Apr 2024

• Key Contributions:
  • Authored Agri-GNN for crop prediction using Graph Neural Networks (GNNs).
  • Contributed to a pest detection app leveraging TensorFlow, ArcGIS, and data science tools.
  • Published 3 papers and presented at MLCAS2022.
  • Awarded Best Paper Award at the Springer ICEIL 2024 Conference.

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📜 Published Works

• MDPI Entropy Paper – Chess Valuation with Q-learning (2024).
• Springer ICEIL Paper – Agri-GNN for Crop Prediction (2024).
• MLCAS2022 Presentation – Pest Detection App Using AI.

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Explore my journey in AI and beyond. Feel free to connect or dive into the linked works for more insights!