Tiana Longjam

Tiana Longjam

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Data Science & Mathematics

University of Wisconsin–Madison | Class of 2027

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About

Greetings! I am Tiana, a junior at UW-Madison. Double Majoring in Data Science and Mathematics.


I enjoy using machine learning and data analysis to explore complex real-world datasets and utilise my skills to better understand the world and apply them in different ways.


One of my favourite Sustainable Development Goals is to Reduce Inequalities in the world, I want to achieve this through my own accomplishments and efforts.

Experience

Community Assistant - University Housing

Nov 2024 - Present
  • Utilize software tools including Microsoft Office Suite (Outlook, Word, Excel), SharePoint, and databases to efficiently complete administrative and operational tasks.
  • Leverage spreadsheets and web-based systems to track, organize, and manage information accurately.
  • Demonstrate core values of care, integrity, creativity, respect, optimism, stewardship, and excellence in all interactions.
  • Maintain desk security and confidentiality, and respond promptly to emergencies using StarRez, SharePoint, and EMS

Data Science Intern – Deloitte

Jun–Aug 2024
  • Engineered and consolidated datasets by analyzing confidential client BIG data from over 5 financial years, for a Loyalty Case Study analysis.
  • Contributed to the development of a new retail website by optimizing front-end components with HTML, enhancing the user interface and overall user experience.
  • Conducted extensive data analysis using Python and Pandas library to generate financial conclusions, significantly deepening my analytical skills and understanding of financial data.

Hackathon

exaapiproject

PrivacyLens

github CheeseHacks 2026 Feb 28 – Mar 1

React

FastAPI

Flask

  • Architected a full-stack privacy audit web application using React, FastAPI, Flask, and Google Gemini 2.5 Flash, enabling users to scan 30+ apps from a single phone screenshot and receive plain-English privacy breakdowns in under 30 seconds
  • Engineered a dual-backend system separating AI processing (Flask) from data and scoring logic (FastAPI), integrating Google Gemini Vision API to automatically detect and audit app icons from uploaded phone screenshots with 90%+ detection accuracy across 37 apps
  • Developed a risk-weighted privacy scoring algorithm using real permission and tracker data sourced from the Exodus Privacy database, normalizing scores across 50+ apps to surface actionable insights for non-technical users at CheeseHacks 2026
reflect

Reflect

github MadData 2026 Feb 21 – 22

React

Vite

Express

Supabase

Google Maps API

  • Engineered a full-stack mental health web application at MadData 2026 by building 4 core features including a facility finder, mood tracker, AI journal, and streak tracker using React, Vite, and Supabase
  • Integrated Google Maps Distance Matrix and Geocoding APIs into a custom Express backend route to calculate real-time driving distance and straight-line distance across 8,000+ SAMHSA-verified mental health facilities, enabling results to sort automatically by proximity
  • Designed and populated a PostgreSQL database on Supabase by parsing 60+ service codes from the SAMHSA National Directory of Mental Health Treatment Facilities into queryable boolean columns, supporting flexible multi-filter search across 8,319 facilities

Projects

exaapiproject

Bias & Source Diversity Checker

github

Python

Exa AI API

HuggingFace

  • Built a tool to analyze search results for source bias and diversity using domain distribution metrics (HHI, Gini, Shannon entropy).
  • Performed sentiment analysis on snippets to detect positivity/negativity trends across domains.
  • Visualized insights with bar charts, pie charts, and heatmaps to highlight dominant sources and overall diversity.
wisconsin

Wisconsin Census Population Predictor

github

Python

GeoPandas

SQLite

Scikit-learn

  • Engineered and integrated multi-source geospatial, raster, and relational data (GeoJSON, shapefiles, SQLite, and land use raster) into unified DataFrames for predictive modeling.
  • Developed and evaluated multiple regression models (LinearRegression, Pipelines with StandardScaler) to predict Wisconsin county and tract-level population, achieving strong explained variance and model interpretability.
  • Applied feature engineering and visualization using GeoPandas, Matplotlib, and rasterio to map spatial patterns, analyze land-use impact, and assess model performance across regions.
loanspic

Loan Insights & Fair Lending Analysis

github

Python

OOP

Binary Search Trees

Matplotlib

  • Engineered a custom object-oriented framework (Applicant, Loan, Bank classes) to model and analyze 2020 Wisconsin HMDA loan data, enabling structured data handling and extensibility.
  • Implemented a Binary Search Tree (BST) to optimize loan lookups and benchmark performance against traditional iteration, achieving significant efficiency gains.
  • Conducted exploratory and statistical analyses on interest rates, applicant demographics, and lending trends, uncovering insights into potential bias and fairness patterns across banks.
wisconsin

SEC Filing & Server Log Intelligence

github

Python

Pandas

NumPy

Visualization

Data Wrangling

  • Developed a Python data pipeline to parse and analyze SEC EDGAR server logs and company filings, integrating real-world datasets to extract patterns in user access, filing activity, and organizational behavior.
  • Leveraged data analysis libraries (Pandas, NumPy, Matplotlib) to clean, aggregate, and visualize large-scale logs, identifying top IP sources, time-based trends, and state-wise filing distributions.
  • Demonstrated advanced data wrangling and exploratory data analysis (EDA) skills by merging heterogeneous datasets, building insight-driven visualizations, and optimizing parsing efficiency for high-volume text data.
books

Amazon Top 50 Bestsellers

github

Python

Pandas

Seaborn

Matplotlib

  • Performed exploratory data analysis (EDA) on a decade of Amazon bestseller data (2009–2019) to uncover sales trends, genre dominance, and rating distributions using Pandas and NumPy.
  • Visualized key insights such as yearly category performance, author popularity, and pricing correlations through Seaborn and Matplotlib.
  • Applied data cleaning, feature engineering, and statistical analysis techniques to identify patterns influencing book popularity and consumer behavior over time.
loanspic

Stock Price Prediction

github

Python

Machine Learning

Scikit-learn

Matplotlib

Data Visualization

Seaborn

XGBoost

Pandas

NumPy

  • Developed a machine learning pipeline to predict Tesla stock price movement using historical OHLC data; implemented feature engineering, classification models (Logistic Regression, SVM, XGBoost), and performance evaluation with accuracy and ROC metrics.
  • Applied financial data analysis techniques by computing derived features (price spreads, volatility, quarter-end effects) to enhance model interpretability and trading relevance.
  • Conducted exploratory and statistical analyses on interest rates, applicant demographics, and lending trends, uncovering insights into potential bias and fairness patterns across banks.

Extracurriculars

dotData

Jan 2026 – Present
  • Competed in MadData hackathon by collaborating with a team to analyze and model a real-world dataset, delivering data-driven insights within a time-constrained environment
  • Engaged with 10+ industry professionals at speaker and networking events to gain exposure to data science career paths and emerging trends in the field
  • Strengthened technical and analytical skills by attending workshops and study sessions focused on [specific topics, e.g., SQL, machine learning, data visualization]

Wisconsin RangDe

Sep 2025 – Present
  • Wisconsin’s All Girls Recreational South Asian-Fusion Dance Team!
  • Perform at various university and community events throughout the year.
  • Collaborate with team members to choreograph and execute routines combining Bollywood, Bhangra, Contemporary, and Hip-Hop styles.

Wisconsin Emerging Scholars

Jan 2024 – Dec 2024
  • Earned additional academic credits through co-enrollment in calculus and the WES calculus course, demonstrating commitment and dedication to academic excellence within the WES community.
  • Contributed to a collaborative learning environment as a member of a diverse student group, gaining unique problem-solving techniques and achieving high academic performance, consistently outperforming standard calculus sections.

Skills

Languages
Python
R
SQL
HTML
CSS
Big Data & ML
Spark
Spark MLlib
HDFS
BigQuery
Cassandra
Kafka
Infra & APIs
🐳
Docker
REST API
Protobuf
gRPC

Relevant Coursework

Introduction to Artificial Intelligence

Comp Sci 540

Data Science Major

Machine Learning

Knowledge-based search techniques

Automatic deduction

Predicate logic

Probabilistic reasoning

Problem solving

Data mining

Natural language understanding

Computer Vision

Speech recognition

Robotics

Modern Algebra

MATH 541

Mathematics Major

Groups

Normal subgroups

Cayley's theorem

Rings

Ideals

Homomorphisms

Polynomial Rings

Abstract Vector Spaces

Introduction to Finance

Finance 300

Business Minor

Corporate finance and investments

Financial Environment

Securities Markets

Financial Markets

Financial Statements and Analysis

Working Capital Management

Capital Budgeting

Cost of Capital

Dividend Policy

Asset Valuation

Investments

Decision-making under Uncertainty

Mergers

Options