About Me
I'm Amul Poudel
As a Computer Science major at Texas State University, I'm deeply interested in artificial intelligence, data science, and machine learning. I focus on developing my skills in Python programming and data analysis techniques to build predictive models and visualization tools.
I'm particularly fascinated by data analysis, machine learning, and AI applications. I enjoy working with statistical models and developing predictive algorithms that can extract meaningful insights from complex datasets to solve real-world problems.
Name:
Amul Poudel
Email:
pramulchettri@gmail.com
Location:
San Marcos, Texas
Study:
Texas State University
Professional Skillset
Tools I Use
Research Interests
Areas of interest that I am passionate about.
Exploring advanced ML algorithms and their applications in solving complex real-world problems. Particularly interested in supervised and unsupervised learning techniques.
Investigating how machines can understand, interpret, and generate human language. Interested in sentiment analysis, text classification, and language generation.
Studying how computers can gain high-level understanding from digital images and videos. Fascinated by object detection, image segmentation, and visual recognition systems.
Researching neural network architectures and their applications. Keen to explore transformers, CNNs, RNNs, and their implementations for various domains.
Certifications
Continuous learning and skill development
Projects
Showcasing my technical skills in machine learning, data analysis, and software development
Built an end-to-end Streamlit dashboard to analyze residential listings in Gurgaon. The app features an interactive map of properties, BHK price distribution, composition breakdowns, and an Area-vs-Price scatter. It also includes a price-estimation workflow and a content-based recommender using cosine similarity of engineered features to find similar apartments. Visuals are rendered with Plotly; data processing uses Pandas/NumPy with cached pipelines for speed.
ResQMe is a real-time AI-powered emergency response system designed to detect distress situations, notify local authorities, and intelligently allocate nearby resources. The platform includes victim-side alerts, responder dashboards, and an admin panel, all powered by location-aware logic and cloud-based infrastructure. Built during RiverHacks 2025 and awarded for excellence in community impact and innovation.
Used OpenCV and TensorFlow to detect and classify American Sign Language hand gestures in real-time. Deployed as a web app using Streamlit, Docker, and GitHub CI/CD. Hosted on a custom Streamlit Cloud domain for easy access and interaction.
Developed an interactive web application using Streamlit to explore and analyze census data through customizable charts, tables, and filters. Implemented real-time data manipulation with Pandas and integrated Plotly visualizations for clear insights. Deployed seamlessly on Streamlit Cloud for public access and linked with GitHub for continuous updates.
Contributed to a web-based project that delivers smart, localized recommendations using a fine-tuned LLaMA model. Focused on integrating AI inference to personalize user experiences, enhance relevance, and dynamically adapt to user input. Ensured smooth model integration and optimized performance for real-time use.
Contact Me
Let's connect and discuss opportunities