I'm closely following the entropix project and contribution where I can.

• GitHub: main Repository

An innovative approach to enhance language model reasoning using Monte Carlo Tree Search.

• Tech Stack: Python, asyncio, Pydantic
• Key Features:
  • Dual-model architecture using Andromeda and Prometheus models
  • Monte Carlo Tree Search (MCTS) adapted for language model reasoning
  • UCB1 score calculation for node selection
  • Asynchronous processing for improved efficiency
  • Graph visualization for interpretability
• Achievement: Improved accuracy from 62.50% to 78.12% on complex reasoning tasks
• Technical Details:
  • Implemented custom MCTS algorithm with selection, expansion, simulation, and backpropagation steps
  • Utilized Pydantic models for structured data and type safety
  • Developed probing question generation and answer evaluation mechanisms
• GitHub: grapes Repository

Implementation of MADDPG for cooperative AI in PettingZoo's MPE environment.

• Tech Stack: Python, TensorFlow, PettingZoo
• Key Features:
  • Multi-agent reinforcement learning in continuous, partially observable environments
  • Centralized critic with decentralized actors architecture
  • Custom reward shaping for cooperative behavior
• Technical Details:
  • Implemented MADDPG algorithm with separate actor and critic networks for each agent
  • Utilized experience replay buffer for off-policy learning
  • Employed soft target updates for stability
  • Customized action and observation spaces for the Simple Adversary environment
• GitHub: MADDPG Repository

Implementation of Twin Delayed Deep Deterministic Policy Gradient with Hindsight Experience Replay for sparse reward environments.

• Tech Stack: Python, TensorFlow, Gymnasium, Panda-Gym
• Key Features:
  • Sparse reward learning in continuous action spaces
  • Hindsight Experience Replay for efficient learning from failures
  • Twin Delayed DDPG for reduced overestimation bias
• Technical Details:
  • Implemented TD3 algorithm with dual critics and delayed policy updates
  • Developed custom HER strategy for the PandaReach-v3 environment
  • Utilized Ornstein-Uhlenbeck process for exploration noise
  • Employed soft target updates and custom learning rates for actor and critic
• GitHub: TD3-HER Repository

DDDQN model solving the OpenAI Atari Pong environment using PyTorch.

• Tech Stack: Python, PyTorch, OpenAI Gym
• Key Features:
  • Double Q-learning to reduce overestimation
  • Dueling network architecture for better state-value estimation
  • Prioritized experience replay for efficient learning
• Technical Details:
  • Implemented custom CNN architecture with separate advantage and value streams
  • Developed softmax action selection with decreasing temperature
  • Utilized Huber Loss and He initialization for improved training stability
  • Implemented frame stacking and preprocessing for Atari environment
• GitHub: DDDQN Repository

iOS native app for AI-powered workout design and sharing.

• Tech Stack: Swift, SwiftUI, Firebase, OpenAI API
• Key Features:
  • GPT-3.5 integration for personalized workout generation
  • Real-time data synchronization with Firebase
  • User authentication and secure API communication
• Technical Details:
  • Developed custom SwiftUI views and animations for smooth UX
  • Implemented Firebase Authentication for secure user management
  • Utilized Firebase Cloud Functions for serverless OpenAI API integration
  • Designed efficient data models for Firestore database
• GitHub: private repo

End-to-end application of Credit Risk models based on Basel III standards.

• Tech Stack: Python, Pandas, Scikit-learn, StatsModels
• Key Features:
  • Probability of Default (PD), Loss Given Default (LGD), and Exposure at Default (EAD) calculations
  • Feature selection and engineering for credit risk factors
  • Model validation and monitoring techniques
• Technical Details:
  • Implemented logistic regression for PD modeling
  • Utilized beta regression for LGD modeling
  • Developed custom scorecards and rating systems
  • Implemented stress testing and sensitivity analysis
• GitHub: Credit Risk Repository

Implementation of a basic blockchain and cryptocurrency system.

• Tech Stack: Python, Flask, Cryptography libraries
• Key Features:
  • Distributed ledger implementation
  • Proof-of-Work consensus mechanism
  • Transaction management and validation
• Technical Details:
  • Developed custom block structure with SHA-256 hashing
  • Implemented distributed consensus algorithm
  • Created REST API endpoints for mining, transactions, and chain validation
  • Utilized Flask for creating multiple nodes on different ports
• GitHub: Blockchain Repository

Sentiment analysis of r/WallStreetBets subreddit to study its influence on stock market movements.

• Tech Stack: Python, NLTK, TextBlob, Pandas, Matplotlib
• Key Features:
  • Reddit data scraping and preprocessing
  • Sentiment analysis of subreddit posts and comments
  • Correlation analysis with stock market data
• Technical Details:
  • Implemented custom Reddit scraper using PRAW
  • Developed sentiment analysis pipeline with NLTK and TextBlob
  • Utilized time series analysis for correlation with stock prices
  • Created interactive visualizations for sentiment trends
• GitHub: private repo

Deep learning model for predicting Corn Futures prices based on US weather patterns.

• Tech Stack: Python, TensorFlow, AWS (EC2, S3, Lambda)
• Key Features:
  • Time series forecasting of commodity prices
  • Integration of weather data for improved predictions
  • Scalable cloud deployment on AWS
• Technical Details:
  • Developed LSTM-based neural network for time series prediction
  • Implemented data pipeline for weather and price data integration
  • Utilized AWS Lambda for automated data updates and model retraining
  • Deployed model on EC2 with auto-scaling for handling variable load
• GitHub: corn Repository

• Languages: Python, Swift, JavaScript
• Frameworks: TensorFlow, PyTorch, Langchain, SwiftUI
• ML/AI: Reinforcement Learning, Natural Language Processing, Computer Vision, Sentiment Analysis
• Cloud: AWS, Firebase
• Blockchain: Basic implementation
• Tools: Docker, Git, Jupyter Notebooks
• Other: Asynchronous Programming, Graph Algorithms, API Integration, iOS Development