I am a Master's student in Computer Intelligent Systems at the University of Verona, currently conducting my thesis research at EPFL in Lausanne.

My work focuses on applied artificial intelligence, machine learning, computer vision, scientific Python, and research-oriented software development. I am especially interested in building practical AI systems that combine clean code, reproducible experiments, model evaluation, and user-facing applications.

I am currently working on thesis research at EPFL involving multidimensional biological imaging data, temporal tracking, image analysis workflows, method evaluation, and practical scientific software development.

Alongside my thesis work, I am building applied AI and computer vision projects focused on:

• Machine learning and deep learning workflows
• Computer vision and object detection
• AI chatbots and document question-answering systems
• Customer support and FAQ-based assistants
• Streamlit-based AI applications
• Time-series analysis and anomaly detection
• Clean, reproducible Python project structure

FAQ-based customer support chatbot built with Python and Streamlit.

The app retrieves answers from a business FAQ knowledge base, avoids unsupported responses when the FAQ does not contain enough information, and includes a simple lead request form.

Tech: Python, Streamlit, Pandas, scikit-learn, TF-IDF, OpenAI API optional

Repository:
https://github.com/aun151214/ai-customer-support-chatbot

Document question-answering app that allows users to upload PDF or TXT files, ask questions, retrieve relevant document sections, and optionally generate concise answers using the OpenAI API.

The app is designed to avoid inventing answers when the uploaded document does not contain enough information.

Tech: Python, Streamlit, scikit-learn, pypdf, TF-IDF, OpenAI API optional

Repository:
https://github.com/aun151214/ai-document-chatbot-streamlit

Computer vision web app for object detection using a pretrained YOLO model.

The app allows users to upload an image, detect objects, view confidence scores and bounding boxes, and download the annotated output image.

Tech: Python, Streamlit, Ultralytics YOLO, OpenCV, Pillow, Pandas

Repository:
https://github.com/aun151214/yolo-object-detection-streamlit

Machine learning and deep learning pipeline for Remaining Useful Life prediction using multivariate time-series data.

The project includes data preparation, model benchmarking, evaluation, and technical reporting.

Tech: Python, TensorFlow, scikit-learn, Pandas, NumPy, Time-Series Analysis

Repository:
https://github.com/aun151214/predictive-maintenance-cmapss

Interactive Streamlit dashboard for anomaly detection and monitoring using time-series data.

The project focuses on making machine learning results easier to inspect and interpret through a simple user interface.

Tech: Python, Streamlit, scikit-learn, Pandas, Data Visualization

Repository:
https://github.com/aun151214/Real-Time-Anomaly-Detection-Dashboard-NAB-Dataset

End-to-end computer vision workflow for object detection.

The project includes dataset preparation, training, inference, evaluation, and reproducible project structure.

Tech: Python, PyTorch, OpenCV, YOLOv8, Computer Vision

Repository:
https://github.com/aun151214/YOLOv8-Object-Detection-Project-1

Programming and Data
Python, Pandas, NumPy, Jupyter, Git, GitHub

Machine Learning and AI
scikit-learn, TensorFlow, PyTorch, Machine Learning, Deep Learning, Model Evaluation, Benchmarking

Computer Vision
OpenCV, YOLO, Object Detection, Image Processing, Scientific Image Analysis

AI Applications
Streamlit Apps, Chatbots, Document Q&A, FAQ Retrieval, OpenAI API, LLM-based Prototypes

Software and Research Workflow
Clean Python Structure, Reproducible Experiments, Technical Documentation, Debugging, Problem Solving

• Applied AI and machine learning
• Computer vision and image analysis
• Scientific Python and research software
• Biomedical image analysis
• LLM-based tools and document chatbots
• AI assistants for practical workflows
• Time-series analysis and anomaly detection
• User-facing AI applications

I am open to opportunities in:

• Machine Learning Engineering
• Applied AI Development
• Computer Vision Engineering
• Research Engineering
• Scientific Software Engineering
• Python and Streamlit App Development
• AI/ML Internship or Junior Roles
• PhD or research assistant opportunities related to AI, computer vision, and scientific imaging

• LinkedIn: https://www.linkedin.com/in/aunali-ml-cv/
• GitHub: https://github.com/aun151214