Research & Projects
Exploring the future of biologically-inspired artificial intelligence
My research has focused on developing intelligent autonomous navigation systems using active inference and neuroscience principles. Now, I'm interested in exploring more applications in data science and machine learning while maintaining a focus on biologically-inspired approaches.
Active Inference
Machine Learning
Data Science
Neuroscience
Biology
Active Inference Research
Active inference is a theoretical framework that describes how biological systems learn, perceive, and act by minimizing free energy. I've applied this framework to autonomous navigation since September 2024 and have interned at the Active Inference Institute.
An Active Inference Approach to Autonomous Navigation
This project compares active inference algorithms to Deep Q-Networks (DQNs) in a realistic 3D robotics environment called CoppeliaSim. The research explores how active inference can improve navigation performance in complex, uncertain environments. This project won 2nd place in Computational Biology at the Alameda County Science and Engineering Fair.
Hierarchical Active Inference for Autonomous Drone Navigation in Microsoft AirSim with Environmentally Aware Adaptive Planning
Currently working in Microsoft AirSim with drone navigation. Developing hierarchical active inference framework and using hybrid methods to scale it up. The project implements dynamic planning to optimize computational cost and ensure drone safety. Enhanced robustness is achieved through this framework design. Implementation uses RxInfer (Julia) and Python for AirSim integration.
AI, ML & Data Science
I've been working on machine learning and data science projects since May 2024. I enjoy studying the theoretical foundations of these fields and applying them to solve real-world problems.
Nutritional Behavior Analytics
Designed and conducted a comprehensive survey to identify local nutrition habits and patterns. Collected data from diverse demographics and performed statistical analysis to uncover insights about dietary behaviors and health awareness.
Nutritional Recommendation System Development
Currently developing a recommendation system to provide personalized nutritional guidance. This ongoing project involves researching neural network architectures specialized for recommendation systems, with plans to deploy as a user-friendly application.
Machine Learning & Statistical Methods
- Supervised Learning (Regression, Classification)
- Unsupervised Learning (Clustering, Dimensionality Reduction)
- Ensemble Methods & Boosting
- Bayesian Statistics & Probabilistic Models
Deep Learning & Advanced AI
- Neural Networks Architectures
- Reinforcement Learning & Active Inference
- Computer Vision & Natural Language Processing
- Generative Models
Data Engineering & Tools
- PyTorch, TensorFlow, Scikit-learn
- Data Processing (NumPy, Pandas)
- Visualization (Matplotlib, Seaborn)
Kaggle Competitions
Participating in data science competitions to solve real-world problems and benchmark against industry standards. Working on various machine learning challenges to enhance my practical data science skills and stay current with cutting-edge techniques.
Other Technical Projects
GazeTracking
I developed an accessible eye-tracking communication system using Python that works with a laptop webcam. This project aims to provide a low-cost alternative to expensive eye-tracking devices for people with communication challenges.
Website Development
Building responsive and modern websites using HTML, CSS, and JavaScript. This includes creating user-friendly interfaces and implementing interactive features.
FocusGrid - AI-Powered Study Planning App
A minimalist study planning application that helps students create and optimize their study schedules using AI. Built with React, TypeScript, and integrated with OpenRouter AI for intelligent study plan optimization. Currently a work in progress.