Hi, I'm Nicholas Meeks
Data Science student at Boston University passionate about turning data into actionable insights
About Me
Hi! My name is Nicholas Meeks, but please call me Nick. I am currently an undergraduate student at Boston University pursuing a degree in Data Science with an expected graduation in December 2025. I grew up in Connecticut and graduated from Guilford High School in 2022.
I am passionate about leveraging data science, machine learning, and optimization algorithms to solve complex real-world problems. My experience spans from satellite network optimization to financial technology, where I enjoy building scalable solutions that make a meaningful impact.
With a 3.9 GPA and consistent Dean's List recognition, I've maintained academic excellence while gaining hands-on experience through internships at companies like Viasat and Capital One. In my free time, I enjoy staying physically active, exploring the outdoors, and spending time with family and friends creating great memories!
Boston University
Data Science
Connecticut
Originally from
3.9 GPA
Dean's List
Experience
Viasat
- •Developed a dynamic bandwidth allocation algorithm using network telemetry data and passenger count variance analysis within planes to optimize bandwidth redistribution across in-flight networks, targeting congested areas for maximum improvement
- •Built a real-time data pipeline to process 500GB+ of passenger and network usage data, deriving quantifiable measurement of customer experience improvements
- •Designed a scalable network optimization solution targeting congested areas using threshold-based event detection to minimize network changes while maximizing network connection pass rate improvements
Viasat
- •Aggregated 50GB+ of satellite usage and capacity datasets to model network performance profiles using Amazon Athena
- •Developed ML algorithms to optimize satellite capacity allocation by efficiently querying a large data lake (500GB+) to extract capacity info and leveraging deep learning to traverse a complex search space and derive optimal satellite reallocation potential
- •Implemented Bayesian hyper-parameter tuning with Amazon SageMaker, leveraging parallel processing strategies to achieve accelerated model convergence
- •Presented comparative visual analysis (GeoPandas, Shapely, Matplotlib) of pre- and post-optimization network states to key stakeholders, illustrating improvement in capacity allocation and overall beam utilization (20% reduction in allocations)
Eleven58
- •Optimized Python-based object detection deep learning model using 10,000+ proprietary high-quality images
- •Developed automated waste sorting system with improved accuracy through data augmentation techniques
- •Manually annotated additional data sources using CVAT for enhanced model training
- •Established team roles, project deadlines, and coordinated cross-functional collaboration
Przytycki Lab - Boston University
- •Processed 30,000+ multimodal genomic datasets using R-based algorithms to understand disease processes
- •Developed Python/R algorithms for large-scale genomic data preprocessing and analysis
- •Integrated patch-seq data with single-cell RNA-seq data using network-based models
- •Applied graph theory and statistical methods to identify cellular disease mechanisms
Capital One
- •Developed financial literacy app for college students as part of Software Engineering Summit
- •Created interactive games, real-time chatbot, and data visualizations for spending trends
- •Utilized HTML/CSS/JavaScript for visualizations and ChatGPT API for intelligent chatbot features
- •App selected as finalist and ranked in top 1% of all competition submissions
Featured Projects
Built TOSS (Temporally-Oscillating Satellite Schedules), a tool designed to ingest satellite data and deliver a satellite schedule optimized with an objective function of minimizing the maximum capacity of any group on a satellite. Implemented dual annealing algorithm on a complex space of time-series dataset to converge on optimal time.
Employed machine learning techniques to predict Lyft prices in Boston based on weather, time of day, surge multiplier, and geographic region. Utilized Principal Component Analysis for dimensionality reduction and compared various models (kNN, Naïve Bayes, Linear Regression, Decision Trees, Neural Networks, Random Forests).
Utilized CellWalkR, an R package designed to help identify cell type-specific regulatory regions, to integrate patch-seq data with single-cell RNA-seq data. This research can lead to biological advancements such as precision medicine development by identifying potential targets for more effective treatment.
Data engineering project combining multiple Microsoft Azure services including Cosmos DB, Data Factory for ETL pipelines, and Synapse Analytics. Created visualizations using Microsoft Power BI running on Azure VM to analyze various COVID-19 policy data sources.
Developed a competitive savings app for mobile devices targeting college-aged banking clients. Features included a Financial Literacy Chatbot, financial savings and spending visualizations, and gamification towards positive financial practices.
Leadership & Impact
BU Faculty of Computing and Data Sciences Student Government
Mar 2024 - Present
Liaising with faculty to build relationships and promote student engagement. Managing administrative tasks including keeping accurate records and developing weekly agendas for the DS program.
Key Achievements:
- Faculty relationship building
- Administrative record management
- Weekly agenda development
Boston University
Jan 2023 - May 2023
Shared excitement about BU with prospective and admitted students through campus tours and student panels. Collaborated with university admissions executives monthly to receive updated campus information and hone the campus tour narrative.
Key Achievements:
- Regular campus tours
- Student panel participation
- Monthly admissions collaboration
Hope Chest - 501(c)(3) Nonprofit
2022 - 2023
Determined optimal companies to receive donations through Principal Component Analysis, Decision Trees, and Random Forests, optimizing aid for impoverished neighborhoods in New Haven. Set monthly agendas, managed donations with organizations, and connected with potential donors/receivers.
Key Achievements:
- Raised and donated $5000+
- Applied ML for donation optimization
- Monthly agenda management
My Skill Set
Passionate about leveraging data science and machine learning to solve complex problems. Experienced in end-to-end project development from data ingestion to model deployment.