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Meet me!
My Skills
Work Experience
// career timeline
Microsoft
·past$Software Engineer InternMay 2026 — Aug. 2026
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Amazon
·past$Software Development Engineer InternMay 2025 — Aug. 2025
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Georgia Tech VIP
·past$ACT Driving Simulator ResearcherAug. 2024 — Dec. 2025
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German Performance Auto
·past$Software Engineering InternMay 2024 — Aug. 2024
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Education
// Bachelor's in Computer Science at the Georgia Institute of Technology
coursework — gatech
$cat coursework.txt
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Concentration:Information Internetworks & Intelligence
DATA STRUCTURES & ALGORITHMS
— Linked Lists
— Trees and Graphs
— Sorting Algorithms
— Complexity Analysis
DESIGN & ANALYSIS OF ALGORITHMS
— Divide and Conquer
— Graphs and Trees
— Dynamic Programming
— NP Completeness
OBJECT ORIENTED PROGRAMMING
— Classes and Objects
— Inheritance
— Polymorphism
— Design Patterns
DATABASES
— Schema Design from Real-World Scenarios
— Advanced SQL (Joins, Views, Procedures)
— Normalization & Functional Dependencies
— Relational Mapping & Constraints
COMPUTER NETWORKING
— Routing & Transport Protocols
— Networking Layers
— HTTP/DNS
— Application Data Exchange
SYSTEMS AND NETWORKS
— Operating Systems Concepts
— Processes and Threads
— Scheduling
— Memory Management
OBJECTS AND DESIGN
— UML Diagrams
— SOLID Principles
— System Design Concepts
— Team Collaboration and SCRUM
INTERNET COMPUTING SYSTEMS, SERVICES, AND APPLICATIONS
— Web Scraping/Search Engines
— Web Services and Applications
— Scalability Challenges
— Distributed Systems
COMPUTER ORGANIZATION & PROGRAMMING
— Layers of Abstraction
— Machine Language & LC3
— Assembly
— C Programming
ARTIFICIAL INTELLIGENCE
— Search Algorithms
— Probabilistic Models
— Classification
— RNNs, MDPs, AlphaZero, RLs, CNN, GenAI
MACHINE LEARNING
— Supervised/Unsupervised Learning
— Clustering
— Linear Regression
— Neural Nets
NATURAL LANGUAGE PROCESSING
— Text Representation
— Language Modeling
— Sequence Models
— Neural NLP Methods
DISCRETE MATH
— Logic and Proofs
— Set Theory
— Combinatorics
— Cryptography
LINEAR ALGEBRA
— Matrix Operations
— Vector Spaces
— Eigenvalues and Eigenvectors
— Applications in Machine Learning
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