Skills

Javascript

  • Javascript ECMA Script - ES14
  • Familiar with Figma and UI/UX web frame design concepts
  • MERN Development
  • Next.js; frameworks and templates for UI development
  • React.js for localState mangagement and building reusable components and integration w/ Virtual DOM
  • React.js router library for handling routing using <Link> & <Route>
  • Angular.js for Front-End Develpoment
  • Familiar with Framer Motion, Three.js and Anime.js
  • Redux- for global state mangement
  • Passport.js, JWT and bcyrpt.js for authentication and security.
  • Typescript
  • API development and backend architecture w/ Node.js & Express & Mongoose
  • Version Control with Git
  • SCRUM Development & Agile Development & Problem Solving
  • Mocha, Chai, Jasmine Testing
  • Postman and cURL for testing API, debugging requests, and troubleshooting connectivity issues
  • Tailwind CSS; component based UI development + HTML5.3 + Bootstrap + Chakra UI
  • AJAX and other HTTP requests inlcuding POST, PUT, DELETE & PUT
  • CORS- enabling cors poilcy to connect specific domains with one another
  • Familiar w/ libraries with Chart.js, D3.js, Plotly of visualizing data in pie charts, line charts and gauges

Databases

  • MongoDB + Mongoose, Mongo Compass & Atlas
  • Vector Database Development
  • MySQL Database
  • Seeding data using Mongoose
  • Sharding, Cleaning, Integrating data into Pipelines
  • Designed and implemented MongoDB schemas for a product catalog and user management.
  • Integrated MongoDB with Express.js for handling API requests (CRUD operations).
  • Used Mongoose ODM for schema validation and query optimizations.
  • Implemented indexing and aggregation pipelines to improve query performance.
  • Worked with MongoDB Compass for database inspection and debugging.
  • Seeded databases using scripts to populate test and data.
  • Data propogation of the process of transferring, updating, and synchronizing data across different systems, databases, or components in a distributed environment.

Python

  • Getting familiar with:
  • ~ Object-Oriented Programming and fundamentlas of syntax, data structures, lists, dictionaries, tuples, sets and functions
  • ~ Conccurency/Paralellism: familiarity with threading, multiprocessing, and asyncio for handling paralell computation tasks; especially for high-performance computing in nueral networks

    • Machine Learning Frameworks

  • ~ TesnorFlow and Keras for deep learning models. Nueral Networks like CNNs, RNNs and GANs
  • ~ PyTorch-deep learning framework
  • ~ Scikit-learn for regression, clustering, and classification

    • Data Handling and Analysis

  • ~ NumPy- for numerical operations, manipulating arrays and matrices for large scale neural data and image processing
  • ~ Pandas for structured data sets CSV, Excel and JSON. Data manipulation tasks like filtering, grouping and joining datasets.
  • ~ SciPy:For scientific computing optimization, linear algebra, stastics and numerical routines
  • ~ Matplotlib/Seaborn: for creating static, animated and interactive plots. Used for visualizing nueral data and model performance.

    • Deep Learning Specializing

  • ~ Keras: Building and Training deep learning models
  • ~ Fast.ai: For training deep learning models
  • ~ OpenCV: used for computer vision tasks; for processing and analyzing brain imaging data like MRI and CT scans
  • ~ Reinforcement Learning (RL): Learn RL algorithms i.e., Q-learning, policy gradient methods, Deep Q Networks DQN, as RL is often used for training agents.
  • ~ OpenAI Gym: for designing and training reinforcement learning algorithms in a simulated environment
  • ~ TensorFlow Agents or RLlib: for reinforcement learning specifically

    • Neuroimaging and Signal Processing

  • ~ MNE-Python: for analyzing brain signal and working brain data and time frequency analysis, source localization and visualization of brain activity
  • ~ NIfTI: familiarity with NIfTI image format for storing nueroimaging, and tools like Nibabel for reading and writing NIfTi files
  • ~ PyDICOM: working with DICOM files, files used to extract and manipulate imaging data.
  • ~ SciPy and Singal Processing: For processing signals and performing tasks like filtering, fourier transforms and wavelet analysis

Deployments & Cloud Computing

    Cloud and Infrastructure

  • AWS/GCP/Azure: familiraity with cloud Platforms for manaiging resources, scaling and deploying machine learning models and data pipelines
  • Docker: Containerization for running applications in isolated environments, useful for deploying models and maintaining a consistent development environment.
  • Kubernetes: For managing containerized applications at scale, especially useful for machine learning model deployment and handling large-scale computational workloads.
  • Apache Kafka: For real-time data streaming and processing of large volumes of data, especially in high-frequency neural data analysis.

Experimentation & Collaboration Tools

  • Jupyter Notebooks:for interactive data analysis, experimentation and prototyping, especially for research and data exploration.
  • Wegihts & Biases: A tool for tracking machine learning experiments and visualizing training runs and performance metrics.
  • MLflow: Tracking machine learning experiments, models and results.

Resume