Introduction

• Senior studying Data Science (B.S.) at the University of Michigan Ann Arbor.
• Working on building end-to-end, a tutorial mode feature for Hazel at the Future of Programming Lab (PI: Cyrus Omar, Assistant Professor).
• Working on social network analysis on Bluesky at Blue Data Lab (PI: Tian An Wong, Assistant Professor).

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Publications and Presentations

• Faisal R, Omar C. From Holes To Whole: Building A Guided Experience for Functional Programming in Hazel. Invited for poster presentation at Explore CS Research Showcase, April 10, 2025
• Ahmed KS, Faisal R, Ali MM, Virani SS, Marcinak CT, Zafar SN. Predicting Post-discharge Infection: A Machine Learning-driven Composite Outcome Model for Patients Undergoing Pancreatectomy. Submitted to the American College of Surgeons’ Clinical Congress 2025

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Projects

LogoScout

Demo | 03/2024

• This project is a web-based image extraction tool that crawls websites and retrieves images efficiently, built primarily in Java.
• Supports any publicly accessible webpage as input.
• Uses JSoup to fetch and parse webpages for images.
• Queues subpages only within the same domain to prevent external crawling.
• Implements multi-threaded crawling so that the crawl can be performed on multiple sub-pages at a time.
• Uses the Google Vision API to make image classification and to detect whether a logo is spotted within an image.
• All images produced, link to the respective page or sub-page that they have been crawled from.

Technologies: Java, JSoup, Gson, HTML, CSS, JavaScript, JSON, Google Vision API

Weather Patterns Time-Series Forecasting and Anomaly Detection

Report | 01/2025

• Conducted comprehensive analysis of the Global Weather Repository dataset containing over 46,000 rows of meteorological data, including temperature, precipitation, and air quality indicators.
• Designed and implemented advanced anomaly detection techniques (Z-Score, IQR, Isolation Forest) to identify statistical and environmental outliers.
• Developed and compared time-series forecasting models (SARIMA, Random Forest, Stacked Ensemble) to predict temperature and precipitation trends with a focus on anomaly scenarios.
• Explored geographical and spatial analysis through choropleth maps, highlighting regional variations in weather conditions and anomalies.
• Evaluated feature importance using SHAP values, identifying wind speed and humidity as key drivers of air quality.

Technologies: Python, Scikit-learn, Pandas, NumPy, Plotly, Graphviz

Search Engine

Demo | 12/2024

• Built a scalable search engine similar to Google or Bing, for EECS 485.
• Includes information retrieval concepts like text analysis (tf-idf) and link analysis (PageRank), and parallel data processing with MapReduce.
• Uses a Service-Oriented Architecture to scale dynamic pages and web search.
• Creates a segmented inverted index of web pages using a pipeline of MapReduce programs.
• Built an Index server, a REST API app that returns search results in JSON format.
• Built a Search server, a user interface that returns search results just like Google or Bing.

Technologies: Python, SQL, Flask, HTML and CSS, JSON

Mortality Outcome Prediction in TB and HIV

Report | 11/2024

• This project leverages logistic regression to measure the predictive performance of Case Detection Rate (CDR) in country-specific mortality outcomes for patients with a dual burden of Tuberculosis (TB) and Human Immunodeficiency Viruses (HIV).
• Using data from the Tuberculosis Burden by Country dataset, it evaluates the role of CDR in determining mortality-to-incidence ratios (MIRs).
• Feature engineering techniques such as logarithmic scaling and quantile transformation were applied to the baseline model to address data skewness and improve model performance.
• The final model demonstrated an improvement in AUC for dual-burden mortality predictions, emphasizing the importance of early case detection in mitigating public health challenges.

Technologies: Python, Scikit-learn, Pandas, NumPy, Plotly

MapReduce Framework

Demo | 10/2024

• MapReduce framework in Python inspired by Google’s original MapReduce paper for EECS 485.
• Executes MapReduce programs with distributed processing on a cluster of computers like AWS EMR, Google Dataproc, or Microsoft MapReduce.
• Includes program execution, distributed systems, fault tolerance, OS-provided concurrency facilities (threads and processes), and networking (sockets).
• Consists of a Manager which listens for user-submitted MapReduce jobs and distributes the work among Workers, and multiple Worker instances that receive instructions from the Manager and execute map and reduce tasks that combine to form a MapReduce program.

Technologies: Python, Madoop (Custom version of Hadoop)

Insta485 Client-Side Web Application

Demo | 10/2024

• Developed an Instagram clone as part of a three-project sequence for EECS 485, focusing on building client-side dynamic pages using JavaScript, React, and AJAX.
• Refactored the server-side logic from Flask into a REST API to handle asynchronous data requests, enabling real-time updates for user interactions such as likes, comments, and infinite scrolling without page reloads.

Technologies: Python, SQL, HTML, CSS, JavaScript, React, Flask, AWS

Travelling Salesman Problem

Demo | 06/2024

• Applied branch and bound algorithm to solve TSP problem for complete weighted graph, used MST to get the lower bound for remaining cost, and explored various heuristic approaches to achieve a nearly-optimal solution, for EECS 281.
• To find the optimal tour, we started with our nearly optimal solution and then employed the brute-force method of exhaustive enumeration to achieve the optimal path while being time efficient.

Technologies: C++