👋🏼 Hello there, I’m Novella!

👩🏻‍💻 I’m Novella — a recent M.S. Computing (AI) graduate from the University of Utah, passionate about building data-driven systems, from designing ML pipelines to engineering the full-stack applications that bring them to life.

🔬 During my graduate studies, I worked as a research assistant at LL4MA under Prof. Tucker Hermans, where I built ETL pipelines for robotics applications and co-authored a paper accepted for CoRL 2025.

🚀 Interest

• Data Science & Analytics
• Data Engineering
• Software Development
• Artificial Intelligence
• Machine Learning & Deep Learning
• Natural Language Processing
• Computer Vision
• Data Mining & Database Systems

📚 Education

Master of Science in Computing, Artificial Intelligence Aug 2024 — Dec 2025

University of Utah Salt Lake City, UT

Bachelor of Science, Data Science Jan 2022 — Dec 2023

University of Utah Salt Lake City, UT

Associate Degree of Science, Computer Science and Mathematics Sep 2019 — Jun 2021

University of Durham Durham, UK

📝 Featured Publications

Yixuan Huang, Tucker Hermans, Novella Alvina, Mohanraj Devendran Shanthi. “Fail2Progress: Learning from Failures with Stein Variational Gradient Descent for Robot Manipulation Tasks”. Conference on Robot Learning (CoRL). 2025.

• Co-author on a research paper investigating learning from failures based on an active-learning approach, posing failure-informed data generation in simulation as a variational inference problem
• Helped in analysis of experimental findings to generate figures and video demonstration of the planning approach and failure reasoning methodologies to communicate results and theoretical concepts
• Adapt and evaluate object detection and segmentation model in robotic tasks, contributing to the refinement of failure-handling strategies

💼 Experience

Research Assistant for LL4MA lab Aug 2024 - present

University of Utah

• Designed and maintained ETL pipelines integrating vision and language model outputs into unified backend systems, implementing validation, error handling, and performance monitoring to ensure data integrity for downstream robotics applications.
• Collaborated cross-functionally with colleagues and department heads to define requirements, document technical specifications, and co-author a paper accepted for CoRL 2025 publication
• Adapt and optimized motion planning, grasping algorithms, and data heuristic collection code for the STRETCH robot using Isaac Sim, ensuring seamless simulation functionality

Teaching Assistant for Machine Learning course Aug 2023 — Dec 2023

University of Utah Salt Lake City, UT

• Mentored 60+ students from Bachelor to PhD levels, guiding the implementation of advanced machine learning models for real-world challenges, including TensorFlow, scikit-learn, Keras, PyTorch and XGBoost
• Assessed assignments and developed a comprehensive curriculum, resulting in a 20% increase in student engagement and achievement

Financial Systems Analyst Intern Jun 2021 — Aug 2021

Harapan Bangsa Foundation Tangerang, Indonesia

• Implemented automated tracking for 1000+ employee loans using Python, cutting processing time by 20% and errors by 30%
• Streamlined the salary database, achieving a 30% increase in data accuracy and a 15% reduction in payroll processing time
• Deployed a monthly expense prediction model with a 5% margin of error, enhancing budget management and providing data presentation for decision-making in the finance department

💻 Project

RentRank, Developer Jan 2026 - present

• Built a full-stack personalized rental housing scoring platform website (Python/FastAPI + React/TypeScript) that pulls live data from multiple sources, scores listings across 10+ factors, and ranks them by user-configurable weights, backed by PostgreSQL, a caching layer, and Alembic migrations, containerized with Docker, and deployed on AWS (EC2 for compute, RDS for PostgreSQL).
• Designed a multi-stage ETL data pipeline that integrates three external APIs using concurrent threading with per-API rate limiting, handles missing values via bedroom/bathroom-tier imputation, flags outliers with percentile capping, and validates output with schema checks and automated quality reports.
• Built a preference-weighted scoring model with min-max normalization, bedroom/bathroom-tier grouping, and flexible amenity matching (exact, category, and alias), outputting transparent factor-level score breakdowns with five-tier grading.
• Developed a multi-step interactive UI with Google Maps integration for POI proximity mapping, census demographics data visualizations using ApexCharts, and user-tunable preference sliders that drive real-time scoring weights.

Tech: Python, React, Typescript, FastAPI, SQL, PostgreSQL, SQLAlchemy, Alembic, Docker, AWS (EC2, RDS), Tailwind

Financial NER with Symbolic Consistency Layer, Developer Aug 2025 - Dec 2025Aug 2025 - Dec 2025

• Fine-tuned a BERT-based NER model on financial documents to extract structured entities, with emphasis on reliability, interpretability, and error analysis for financial text analytics.
• Designed rule-based validation (symbolic validation layer) to enforce domain constraints and detect inconsistent predictions, reduced constraint violations from 6% to 0% while maintaining 94% accuracy.

Tech: Python, PyTorch, Huggingface

Comparative Analysis of Reinforcement Learning Models (Overcooked-AI), Developer Feb 2025 - May 2025Feb 2025 - May 2025

• Implemented and evaluated multiple reinforcement learning models in a multi-agent simulation environment.
• Designed experiments to compare learning stability, coordination behavior, and performance across models.
• Analyzed training curves and reward trends to assess strengths and limitations of each approach.
• Built tools to support systematic evaluation and comparison of model behavior.

Tech: Python, PyTorch, TensorFlow, Pandas, NumPy

Research-Based Data Cleaning System (IHCS) -Data Processing & Standardization, Lead Developer Feb 2025 - May 2025Feb 2025 - May 2025

• Built an ETL pipeline to clean and standardize noisy datasets emphasized data quality, consistency, and readiness for downstream analytics.
• Improved data accuracy from 65% baseline (OpenRefine) to 80% through probabilistic consistency checking.

Tech: Python, SQL, Apache Spark, Pandas, NumPy

Deep Learning Based Chatbot, Developer Oct 2024 - Dec 2024

• Developed a deep learning-powered chatbot using advanced NLP techniques (BERT, Rasa NLU, DiagloGPT) for personalized recipe recommendations, focusing on intent recognition and entity extraction to enhance user interaction
• Evaluated chatbot performance using metrics such as Intent and Entity Recognition Accuracy, BLEU, and ROUGE for semantic similarity

Tech: Python, BERT, Rasa NLU, DiagloGPT, NLTK

Web App Development, Back-end Developer and Database Engineer Jan 2023 — Jan 2024

• Enhanced book recommendation and content genre generator precision by 20% through automated machine learning and improved NLP preprocessing, boosting accuracy by 25%, reducing loading times by 30%, and increasing user engagement by 25%.
• Engineered a robust API using Django’s Rest Framework, optimizing communication between frontend and backend systems; increased system integration efficiency by 30% and reduced latency by 50 milliseconds.

Tech: Python, Django, SQL, Docker, Git, Github, Gitlab, AWS, PostgreSQL, Postman, tensorFlow, nltk

AI Pacman Agent Game, Developer Aug 2023 — Dec 2023

• Developed and implemented an AI for a Pac-Man game using Python and Tkinter, integrating reinforcement learning (Q-learning) and advanced pathfinding algorithms (A* and BFS) to optimize gameplay, resulting in a 90% win rate over conventional systems.
• Conducted a comprehensive analysis of AI behavior under various game scenarios to fine-tune parameters and improve the decision matrix using MinMax, ExpectiMax, and Alpha-Beta Pruning methods, leading to a a 30% improvement in ghost evasion tactics and significantly increased game longevity and scoring through detailed behavioral analysis and parameter optimization.

Tech: Python, Tkinter

D3 Data Visualization, Developer Aug 2023 — Jan 2024

• Optimized Python-based data pipeline with NLP preprocessing, increasing efficiency by 50%. Implemented diverging bar charts for sentiment analysis on artists’ songs over multiple years to perform exploratory data analysis
• Constructed a spider chart for various song attributes, to analyze of song characteristics leading to 30% increase in decision clarity

Tech: Python, d3, Javascript, html, css, pandas, numpy, sci-kit learn, nltk, Github

Web Server Learning Management System(LMS), Database Engineer Jan 2023 — May 2023

• Developed a resilient web server database for LMS resulting in a 30% reduction in administrative time. Wrote complex SQL queries and employed LINQ API library in C# for data processing and increased operational efficiency

Tech: SQL, MySQL, C#, .NET Core

Machine Learning Libraries, Developer Aug 2022 — Dec 2022

• Constructed a comprehensive supervised ML training program from scratch, including decision trees, ensemble learning models, linear regression, artificial neural networks (ANN), support vector machines (SVM), and perceptron
• Achieved a 30% enhancement in output prediction accuracy across diverse data models and algorithms

Tech: Python, pandas, numpy, matplotlib, seaborn

🛠️ Skill