About Me
Awards, Leadership, and Involvement
Honors/Awards
Highest Distinction, B.S. in Computer Science; UVA Engineering Dean's List; Honor of living in 52 East Range, part of the Academical Village; 1st Place Hack TJ 7.5 — EastBanc TransitIQ.
Leadership
Google Developer Groups executive and webmaster, teaching assistant experience, and long-running student leadership in CS, robotics, and software teams.
Clubs/Organizations
Active across engineering communities including Google Developer Groups, theCourseForum, HooHacks, ACM, ML Club, UVA Rocketry, and Project: Code.
Relevant Coursework
Graduate and upper-level coursework aligned with systems, scale, data, and AI domains.
Operating Systems
CS 4414 / CS 6456
Topics Include: Baremetal Programming (UART/printf, Timers & Interrupts), Embedded Scheduling, Memory Allocation & Task Management, Virtual Memory & User/Kernel Separation, System Calls, Filesystems (xv6, FAT32), Device Drivers (USB & Sound), libc (newlib), Raspberry Pi Development, Multicore Processing, and DOOM on GameHAT.
Database Systems
CS 4750
Topics Include: Database Design (ER Modeling, Normalization), SQL & Relational Algebra, Indexing & Transaction Processing, Database Programming & Interfacing, Relational & NoSQL Databases, Distributed Databases & Scaling, Load Balancing, Caching, Content Delivery Networks, Storage Systems (Object Storage, Data Warehouses), and Messaging Queues.
Internet Scale Applications
CS 4260
Topics Include: HTTP & CGI, Monolithic and Microservice Architecture, JavaScript & the DOM, Events & CSS, JavaScript Objects & Classes, JSON & Asynchronous Programming, Modals & Node, Microservices, Databases (SQL & NoSQL), Databases at Scale & CRUD, Authentication, Virtualization, Platforms & Orchestration, Load Testing, and Software Testing.
Machine Learning
CS 4774 / CS 6316
Topics Include: Linear Models for Regression and Classification, Bayesian Inference & Decision Theory, Kernel Methods & Support Vector Machines, Neural Networks, Graphical Models & Bayesian Networks, Mixture Models & Clustering, Expectation-Maximization (EM) Algorithm, Dimensionality Reduction & PCA, and Hidden Markov Models.
Natural Language Processing
CS 6770
Topics Include: Text Classification, Word Embeddings (LSA, Word2Vec), Language Modeling (n-gram & Neural LMs), Statistical Machine Translation, Sequence-to-Sequence Models, Text Generation & Summarization, Transformers (GPT, BERT), Parameter-Efficient Fine-Tuning, In-Context Learning & LLM Practice, and NLP Applications for Science and Society.
2D + 3D Computer Vision
CS 4501 / CS 6501
Topics Include: Image Formation, Image Filtering, Detectors & Descriptors (SIFT, Feature Matching), Image Alignment & Model Fitting, Neural Networks, Backpropagation, CNNs, Sequential Models (RNNs & LSTMs), Vision Transformers, Dense Prediction & Transfer Learning, Generative Models (VAEs, GANs, Diffusion Models), and Multiview Geometry & 3D Representations.