Bilal Kaludi
🗺️ Chicago, IL
About Me
Welcome! I am an Environmental Data Scientist with a Master of Science in Big Data Analytics/Data Science from San Diego State University (completed December 2024) and a Data Science Bootcamp graduate from Northwestern University. I also hold a Bachelor of Science degree in Disability Studies Research from the University of Illinois at Chicago.
Currently, I work in a unique dual appointment as Data and Sensor Network Manager at Argonne National Laboratory and Visiting Research Data Scientist at the University of Illinois Chicago, where I contribute to DOE-funded environmental and climate research initiatives. At Argonne, I design and implement comprehensive data management infrastructure and LoRaWAN-based sensor networks for the Wetland Function and Resilience Science Focus Area (SFA), building end-to-end data pipelines from field sensors to centralized storage across multi-institutional research teams. At UIC, I lead sensor network integration and AI applications for the CROCUS (Community Research on Climate and Urban Science) project, where I've developed YOLOv8-based computer vision models for environmental monitoring. Additionally, until December 2024, I worked remotely as a Data Analyst at Northshore Clinical Labs for over three years, where I transformed complex healthcare data into actionable insights. I am a Machine Learning and IoT enthusiast who loves working with big data, building scalable sensor networks, and solving complex problems by thinking outside the box. My technical expertise includes Python, SQL, NoSQL, LoRaWAN, IoT architecture, time-series databases (InfluxDB), cloud platforms, machine learning, data visualization (Tableau, Streamlit, React), and building REST APIs. I have hands-on experience in data pipeline design, automated quality control systems, real-time monitoring, environmental data management, and contributing to DOE's ESS-DIVE repository. With over three years of experience as a Data Analyst, combined with my research roles at Argonne and UIC, I have developed deep expertise in data integration, data cleaning, big data analytics, data visualization, business analytics, and sensor network deployment. My bachelor's degree in Disability Studies Research gives me a unique perspective on inclusive research design, data ethics, and conducting analytics on large, complex datasets. As a strong advocate for open source, I have created and published numerous personal projects publicly and am proud to say that my open-source projects have collectively achieved over 2,000,000 downloads. This reflects my commitment to contributing to the wider data science community and my dedication to advancing accessible, innovative technology. Whether it's building IoT sensor networks for wetland monitoring, developing computer vision models for plant analysis, or creating data visualization dashboards for research teams, I'm passionate about leveraging data science and technology to address real-world environmental and societal challenges.
💡 My Skill Set
🧠 I’m currently learning Streamlit and how it can help to quickly make WebUI's, as well as how good it is with Machine Learning models.
❓ Ask me about anything related to Python, Streamlit, game development in Unreal Engine 5, working with Big Data or Machine Learning Models!
ℹ Fun fact: I trained my own Stable Diffusion Model that surpassed my expectations. You can find the model and view details here!
👨💻 Frontend
Tools I Have Worked With
🔙 Backend
Tools I Have Worked With
💻 Programming Languages
Tools I Have Worked With
🛠️ Cloud Platforms & DevOps/Tools
Tools I Have Worked With
📊 Data Visualization
Tools I Have Worked With
🎨 Design
Tools I Have Worked With
💼 Portfolio
👋 Welcome to my portfolio!
With a passion for big data and a knack for creative problem-solving, I enjoy challenging myself to think outside the box.
Below are some of the projects I have worked on and created which include everything from training a text2image model, to a fully personalized recipe recommender.
You can learn more these projects by clicking on the links as well as viewing the written code.
Food Category Classification & Recipes Recommender
This app is a Food Category Image Classifier that recognizes 12 food categories and provides personalized recipe recommendations based on user preferences. Its user-friendly interface and recipe database integration make it an ideal tool for food enthusiasts seeking recipe suggestions.
ChatGPT-Turbo SMS
ChatGPT-Turbo SMS is a Flask application that allows users to send SMS text messages to ChatGPT-Turbo using Twilio and receive instant responses. This application can be run locally or hosted on services like DigitalOcean to stay active 24/7 using tmux. The application uses Flask, Twilio, OpenAI, and ngrok python libraries.
VirtualBrainGPT: Digital Journal
VirtualBrainGPT is a digital journal streamlit application that leverages the power of OpenAI's Embeddings and Langchain to create a seamless and efficient journaling experience. With the capability to search and extract information from journal entries in seconds, VirtualBrainGPT becomes an indispensable tool for users who want to recall specific details from their past.
Instagram Insights
Instagram Insights is a Python Streamlit application that allows you to analyze and visualize your Instagram data like users not following you back, users you aren't following back, and more.
File Q&A AI Assistant
This app allows users to upload CSV or PDF files, or enter text and ask questions related to the content. The app uses OpenAI’s ChatGPT model to assist users in answering their questions about the uploaded content.
AI Assistant: revChatGPT
This app allows you to chat with ChatGPT using a reverse-engineered API library called revChatGPT. Replies from the Chatbot are streamed back to the user in real-time, which gives the user an experience similar to how ChatGPT streams back its answers.
US Census API Web Application
An Authentication protected web application created for Dr. Rooshey that allows users to query US Census data using the Census API. It fetches, displays, and analyzes the data based on the URL provided. Users can also download the fetched data and summary tables as CSV files. (Private repo due to request)
US Refugee LIVE Data Visualization
A Python Streamlit application created for Dr. Rooshey that provides an interactive visualization of US refugee data for 2022. The data is retrieved live from a Google Sheet, allowing users to explore the most up-to-date information. Users can select different tabs from the sheet and refresh the data to load the latest information. The application will display a table and a line chart for the selected tab. (Private repo due to request)
ARTificialJourney-v1.0-768
A Text2Image AI model trained on 768X768 images on top of Stable Diffusion 1.5 using the “artificial-journey style” keyword, which is designed to create high-quality close-up portrait pictures and landscape images in resolutions of 1024x768px, 768x1024px, and 768x768px. For best results, include detailed text with the prompt. Link 1 | Link 2
ChatGPT-BingChat Prompt Generator
Generates ChatGPT/BingChat & GPT-3 prompts using this model trained by Kaludi. Enter a role and a prompt will be generated based on it.
OpenAI Chatbot Web Interface
Interact with OpenAI's GPT-3 models in real-time using your OpenAI API. Choose from a selection of their best models, set the temperature and max tokens, and start a conversation. Delete the conversation at any time and start fresh.
Quick Summarizer App
This is a Text Summarization Model that has been trained by Kaludi to transform long and complex texts into concise and meaningful summaries. Get a quick and accurate overview of any document in seconds, saving you time and effort.
Web Interface | Model | Dataset
EurekaQA
EurekaQA is a Question & Answering Model that has been trained by Kaludi to analyze text data and automatically answer questions based on the information contained within. The model uses advanced machine learning algorithms to perform extractive question-answering, meaning it selects the relevant information from a given text document to present as the answer to the question.
Web Interface | Model | Dataset
Reviews Sentiment Analysis App
A tool that has been trained by Kaludi to analyze the overall sentiment of customer reviews for a specific product or service, whether it’s positive or negative. This analysis is performed by using natural language processing algorithms and machine learning from the model ‘Reviews-Sentiment-Analysis’ trained by Kaludi, allowing businesses to gain valuable insights into customer satisfaction and improve their products and services accordingly.
Web Interface | Model | Dataset
