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README-gsantac.md

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+## Introduction
+Hi, I'm Gabriel SantaCruz and am a current senior studying computer science on the Artificial Intelligence track. I am very interested in the startup space and have mostly worked with startups ranging from healthcare to blockchain technologies here in the bay. Feel free to reach me at gsantac@stanford.edu or my number: 623-633-0158!
+
+## Technical Skills
+- **Languages:** Python, C, C++, Typescript, Solidity
+- **Tools & Frameworks:** TensorFlow, PyTorch, AWS, React, NextJS
+- **Cloud Services:** AWS (EC2, S3, Lambda), Google Cloud Platform
+- **Machine Learning:** TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy
+- **AI Frameworks:** Hugging Face Transformers, Legal BERT, PoseCNN
+- **Blockchain:** Ethereum, Smart Contracts
+- **Version Control:** Git, GitHub, GitLab
+
+## Projects
+### EightBall Protocol - Prediction Market Platform
+- **Description:** Developed a sophisticated prediction market system utilizing a customized Constant Function Market Maker (CFMM) for efficient liquidity management. The protocol enables users to provide liquidity, make predictions, and resolve outcomes in a decentralized manner.
+- **Key Contributions:**
+  - Designed and implemented an innovative Automated Market Maker (AMM) that allows market initialization at any probability
+  - Optimized liquidity injection mechanisms to eliminate leftover shares
+  - Developed comprehensive testing infrastructure to simulate various market scenarios
+  - Modified Uniswap's core mechanisms and introduced new mathematical models for dynamic probability balancing
+- **Technologies:** Solidity, Ethereum, Smart Contracts, Automated Market Makers, Testing Frameworks
+- **Skills Applied:** Smart Contract Development, DeFi Protocol Design, Mathematical Modeling, Test-Driven Development
+
+### Ensemble RL for Portfolio Optimization
+- **Description:** Developed an innovative stacking-based reinforcement learning strategy for portfolio optimization, combining five advanced RL algorithms (A2C, DDPG, PPO, TD3, SAC) to enhance trading performance.
+- **Key Achievements:**
+  - Designed and implemented a novel stacking architecture integrating multiple RL algorithms
+  - Achieved higher average portfolio returns compared to traditional single-agent approaches
+  - Reduced variance in trading performance through ensemble methodology
+  - Successfully integrated complex RL frameworks including Actor-Critic and Policy Gradient methods
+- **Technologies:** Python, PyTorch, Multiple RL Frameworks, Financial Analysis Tools
+- **Skills Applied:** Reinforcement Learning, Financial Engineering, Ensemble Methods, Algorithm Design
+
+### Legal Case Analysis Using Advanced ML Techniques
+- **Description:** Developed a sophisticated legal case analysis system using Legal BERT and multiple clustering algorithms to organize and analyze court cases effectively.
+- **Key Achievements:**
+  - Implemented Legal BERT for specialized legal document processing and embeddings
+  - Designed and compared five different clustering approaches:
+    - K-means and K-means++ for basic case categorization
+    - Expectation-Maximization (EM) for soft clustering of overlapping legal categories
+    - DBSCAN for density-based automatic cluster detection
+    - Hierarchical clustering with Ward's method for exploratory analysis
+  - Created a comprehensive preprocessing pipeline for legal documents
+  - Developed methods to handle complex relationships between related cases
+- **Technologies:** Legal BERT, Python, Scikit-learn, Transformer Models
+- **Skills Applied:** Natural Language Processing, Clustering Algorithms, Legal Document Analysis, Machine Learning
+
+### 6D Pose Estimation for Robotic Manipulation
+- **Description:** Enhanced PoseCNN architecture for accurate 6D pose estimation in robotic applications, focusing on complex object manipulation scenarios.
+- **Key Achievements:**
+  - Generated a comprehensive 2GB synthetic dataset featuring 25 YCB objects
+  - Created diverse camera angles and occlusion scenarios to simulate real-world conditions
+  - Extended PoseCNN (VGG16-based) architecture for improved pose estimation
+  - Developed sophisticated data generation pipeline for training scenarios
+- **Technologies:** PyTorch, Computer Vision Libraries, VGG16, PoseCNN
+- **Skills Applied:** Deep Learning, Computer Vision, Dataset Generation, Robotics
+
+## Key Coursework
+1. **CS229: Machine Learning**
+   - **Key Topics:** 
+     - Statistical Pattern Recognition & Regression (Linear/Non-linear)
+     - Deep Learning & Neural Networks
+     - Support Vector Machines & Kernel Methods
+     - Probabilistic Models (GLMs, Exponential Family)
+     - Unsupervised Learning (Clustering, EM, Density Estimation)
+     - Dimensionality Reduction (PCA, ICA)
+     - Reinforcement Learning (MDPs, Policy Search, Adaptive Control)
+
+2. **CS231N: Deep Learning for Computer Vision**
+   - **Key Topics:** 
+     - Neural Network Architecture & Training
+     - Image Classification & Object Detection
+     - Convolutional Neural Networks (CNNs)
+     - Visual Recognition Systems
+     - State-of-the-art Deep Learning Methods
+     - Network Fine-tuning & Optimization
+     - Applications in:
+       - Search & Image Understanding
+       - Autonomous Vehicles & Drones
+       - Medical Imaging
+       - Mapping & Navigation
+
+3. **CS238: Decision Making Under Uncertainty**
+   - **Key Topics:** 
+     - Probabilistic Models & Decision Theory
+     - Computational Methods for Stochastic Systems
+     - Bayesian Networks & Influence Diagrams
+     - Dynamic Programming & Reinforcement Learning
+     - Partially Observable Markov Decision Processes
+     - Applications in:
+       - Air Traffic Control
+       - Aviation Surveillance Systems
+       - Autonomous Vehicles
+       - Robotic Planetary Exploration
+
+## Professional Experience
+### Blockchain Developer at EightBall Protocol
+- Duration: June 2024 - Present
+- Key Responsibilities:
+  - Led the development of a novel prediction market protocol using customized CFMM architecture
+  - Architected and implemented an innovative AMM system enabling flexible market initialization
+  - Developed robust testing infrastructure to ensure protocol reliability and accurate payouts
+  - Optimized liquidity management systems by modifying Uniswap's core mechanisms
+  - Collaborated with team members to design and implement new mathematical models for probability balancing
+
+### Residential Assistant at Stanford Summer Engineering Academy
+- Duration: Summer 2022
+- Key Responsibilities:
+  - Mentored and supported high school students participating in Stanford's engineering program
+  - Facilitated engineering workshops and study sessions to enhance student learning
+  - Organized and led community-building activities for program participants
+  - Provided guidance on engineering concepts and academic success strategies
+  - Ensured a safe and inclusive residential environment for diverse student groups
+
+
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